Microsoft Word - MM2019_booklet.docxAbstract: AMMA Annual Medal
Lecture
MM2019 Conference Programme December 5th – 8th 2019 Bintan Lagoon
Resort Indonesia
General Information
1
Welcome! It is our great pleasure to welcome you to the MM2019
Conference on Bintan island. This is the latest in the series of
“MM” conferences, the premier conferences of the Association of
Molecular Modellers of Australasia (AMMA). This year will also
feature the award lecture of the 2019 winner of the AMMA medal,
Prof. Amanda Barnard.
This booklet contains the programme, maps for the conference venue
and conference dinner locations, abstracts for all talks and
posters as well as a participants list. We would like to take this
opportunity to thank all speakers, presenters, sponsors who made
this event possible. We are looking forward to four days of
exciting science!
Please note that photographs may be taken during the event. These
photographs may be used by AMM and the Bioinformatics Institute
(BII) A*STAR for publicity and other related purposes.
Should you have any questions, suggestions, problems, comments or
concerns please feel free to approach any of the conference
organisers or helpers:
Local Organizing Committee: Peter J Bond & Chandra Verma,
Bioinformatics Institute (BII) A*STAR (
[email protected])
Organizing Committee: David Winkler, Monash, La Trobe, Nottingham
universities, CSIRO Bret Church, Faculty of Medicine and Health,
University of Sydney Ricardo Mancera, Curtin University Evelyne
Deplazes, University of Technology Sydney & Curtin University
Brian Smith, La Trobe University Helpers, BII (A*STAR): Roland G.
Huber, Nguyen Thanh Binh, Aishwary Shivgan, Jan K. Marzinek, Sonia
Nicolaou, Pietro Aronica, Ashar J. Malik, Alister Boags, Lorena
Zuzic, Shruti Khare
General Information
General Information Practicalities
• Hotel check-in on the first day will be run concurrently with
conference registration, in the Fairways Room.
• Hotel room check-out on the final day will be at 11am. Guests may
leave luggage at the hotel reception.
• All delegates taking the 5:10pm ferry back to Singapore on the
final day should gather at the hotel reception at 4pm for timely
transfer to the ferry.
• The temperature in Bintan is warm (25-30°C) but we are just
entering the monsoon season, so be prepared for unpredictable
weather throughout the day.
• Because of the weather, the ferry ride could be rough, so you may
wish to bring motion sickness tablets (but note that the journey
time is short).
• The dress-code for the conference is casual. But please bear in
mind that despite the warm weather, there may be strong
air-conditioning inside the lecture halls.
• You may wish to bring: a good-quality insect repellent to protect
against mosquitoes and sand flies; sun-safe and light-weight
clothing, hat, sunglasses, sunscreen, etc.; beach-friendly /
swimming gear; comfortable shoes / sandals.
Lectures • All lecture sessions, and the AMMA Annual General
Meeting (AGM) on Friday
afternoon, will be held in the Fairways room. • Speakers should
arrive 10 minutes before their session starts, to check the
equipment
works and/or to upload presentations. Speakers may use their own
laptop if they prefer, but should bring an adapter if
required.
• Plenary Lectures (PLs), Keynote Lectures (KLs), Contributed Talks
(CTs), and Short Talks (STs) will be allotted a total presentation
time (including Q&A) of 50 minutes, 30 minutes, 20 minutes, and
10 minutes, respectively.
• Chairs will give PLs, KLs, CTs, and STs a warning at 10 minutes,
5 minutes, 5 minutes, and 2 minutes, respectively, before the end
of their allotted time.
• The Manado Room will be available throughout the conference for
delegates to use as a room for private work / break-out
discussions.
Posters • Poster presenters should affix their posters in the
Sulawesi room, on the first day.
Posters may be removed on the last day. • Poster viewing will be
possible during afternoon tea breaks on Friday and Saturday,
and during the Friday evening poster session, in the Sulawesi room.
• Posters will be judged for prizes during the Friday evening
poster session.
Refreshments • Breakfast is served each day from 6.30am in the
Fiesta restaurant. • Lunch will be served each day in the Fiesta
restaurant, except on Saturday when a
special brunch will be served with live jazz in the Nelayan
restaurant. • Dinner on Thursday and Saturday will be served at the
Nelayan Upper Deck. • A drinks reception and conference BBQ dinner
will take place on Friday on the beach. • Morning tea breaks will
be held in the Fairways room, whereas afternoon tea breaks
with poster viewing will take place in the Sulawesi room.
General Information
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AMMA Annual Medal Lecture: Amanda Barnard CSIRO Data61 "Digital but
Different - Combining Computational Modeling and Machine Learning"
Amanda S. Barnard, Ben Motevalli, Amanda J. Parker, J. Meli Fisher,
Chris A. Feigl and George Opletal A fundamental aim of
nanomaterials research is to identify features of materials that
can be tuned to control how the nanomaterial performs under
specific application conditions. The combination of computational
chemistry, computational materials science with machine learning
provides a powerful way of relating structural features with
functional properties, but combining these fundamentally different
scientific approaches is not as straightforward as it seems.
Machine learning methods were developed for large data sets with
small numbers of consistent features. Typically materials data sets
are small, with high dimensionality and high variance in the
feature space, and suffer from numerous destructive biases. None of
the established data science or machine learning methods in
widespread use today were devised with materials data sets in mind,
but there are ways to overcome these issues and use them reliably.
In this presentation we will discuss the impact of domain- specific
constraints on data-driven materials design, and explore the
differences between materials simulation and materials informatics
that can be leveraged for greater impact.
Abstracts: Plenary Lectures
5
PL-1: Gerhard Hummer Max Planck Institute of Biophysics "What's
wrong with diffusion?" Gerhard Hummer, Soeren von Buelow, Marc
Siggel, Martin Voegele, Max Linke, Juergen Koefinger Molecular
dynamics simulations of the diffusion of proteins and other
macromolecules in dense solutions and in lipid membranes revealed
unexpected complexities. In systems mimicking the interior of a
living cell, with densely packed proteins, the translational and
rotational diffusion of proteins slow down dramatically at high
protein concentrations, and the Stokes-Einstein relation appears to
break down. In membranes, the apparent diffusion coefficient
appears to grow without bound as the box size is increased. We
resolve these issues by showing, first, that transient clustering
of proteins explains quantitatively the increase in the apparent
Stokes radius and the rise in the viscosity. Second, we show that
the divergence of membrane diffusion is the result of the unusual
hydrodynamics under periodic boundary conditions. A hydrodynamic
correction not only explains the divergence, but also produces
proper diffusion coefficients and difficult-to-calculate membrane
viscosities. In essence, membrane simulations provide a molecular
realization of the Stokes Paradox. Overall, hydrodynamic theory
explains why diffusion in concentrated solutions is hindered
dramatically even by weak and transient protein-protein
interactions and why diffusion in membranes appears to break down
entirely. Accounting for hydrodynamics, we obtain diffusion
coefficients that can be interpreted meaningfully and compared to
experiment.
Abstracts: Plenary Lectures
6
PL-2. Tim Clark FAU Erlangen-Nürnberg "Charge transport in
materials for solar cells"
Very large scale semiempirical MO calculations provide
wavefunctions for aggregates (up to 100,000 atoms) and periodic
systems (up to 50,000 atoms in the repeat unit) relevant to organic
and hybrid solar cells. Semiempirical configuration-interaction
calculations (up to 5,000 atoms) are also used to characterize
excited states involved in singlet fission. Charge transport itself
can be simulated using propagation of the electron density in
imaginary time.
Abstracts: Plenary Lectures
PL-3. Lars Nordenskiöld Nanyang Technological University -
Singapore "Multiscale Modelling of DNA and Nucleosome Phase
Separation: From Atomistic to Mesoscale Level by Inverse Monte
Carlo" Lars Nordenskiöld, Tiedong Sun, Alexander Mirzoev, Vishal
Minhas, Nikolay Korlev and Alexander Lyubartsev
DNA condensation and phase separation is of utmost importance for
DNA packing in vivo with important applications in chromosome
organization, medicine, biotechnology and polymer physics. E.g.,
the presence of hexagonally ordered DNA is observed in virus
capsids, sperm heads and in dinoflagellates. Rigorous modelling of
these processes in all-atom MD simulations is presently difficult
to achieve due to size and time scale limitations. We used a
bottom-up hierarchical approach for systematic coarse-graining
following the inverse Monte Carlo (IMC) approach to extract solvent
mediated effective CG potentials for all interactions in the system
from structural properties of the underlying system. Following
validation, we modeled DNA and nucleosome phase separation at
mesoscale level, induced by multivalent cations. Solvent-mediated
effective potentials for a CG model of DNA were extracted from
underlying all-atom MD simulations. Simulations of hundreds of
100-bp-long CG DNA oligonucleotides in the presence of explicit
cobalt(III)-hexammine ions demonstrated aggregation to a liquid
crystalline hexagonally ordered phase. Proceeding to further
coarse- graining and extraction of effective potentials, we
conducted modelling at mesoscale level. In agreement with electron
microscopy observations, simulations of a 10 kbp-long DNA molecule
showed phase separation to either a toroid or a fibre with distinct
hexagonal DNA packing. The approach used here is based only on the
underlying all-atom force field and uses no adjustable parameters
and is being generalized to modelling chromatin condensation. We
developed a CG model for the nucleosome core particle (NCP) and
extracted the effective potentials on the basis of underlying
all-atom MD simulations and simulated NCP phase separation in the
presence of multivalent cations.
Abstracts: Plenary Lectures
PL-4. Jim Warwicker University of Manchester "Molecular modelling
for protein therapeutics: shape and charge and other old
friends"
Jim Warwicker and Max Hebditch Developability is the somewhat
unwieldy term describing the field of ensuring that protein
therapeutics are able to be processed, stored and delivered as
desired and at sufficiently high concentration. It incorporates
many aspects of standard protein chemistry, for example limiting
chemical modification and protein aggregation. Many groups are
developing models in this area, with a focus on aggregation and
other biophysical properties. Our own work will be discussed, based
around tools available at the www.protein-sol.manchester.ac.uk
site. These are amino acid sequence based and structure based
methods, although in either case non-polar content and charge
feature amongst properties that correlate with experiment.
Alongside these perhaps obvious features, we have come across a few
interesting sidelines, for example that lysine and arginine are
quite different in regards to protein solubility, and that a simple
surface tension parameterised hydrophobic effect can fail badly.
Recent work describing machine learning models for the biophysical
behaviour of antibodies will also be presented.
Abstracts: Plenary Lectures
PL-5. Rebecca Wade Heidelberg Institute for Theoretical Studies
(HITS) and Heidelberg University "Computational Approaches to
Protein Dynamics and Binding Kinetics for Drug Discovery"
The dynamic nature of protein structures and the diversity of
protein binding pocket dynamics provide challenges and
opportunities for ligand design [1]. We have developed TRAPP, a
toolbox of computational approaches to identify TRAnsient Pockets
in Proteins for ligand design. I will present recent developments
in TRAPP to identify pocket conformations with high druggability.
Protein binding site flexibility is one of the factors that can
affect drug- target binding kinetics. Growing evidence that the
efficacy of a drug can be correlated to target binding kinetics has
led to the development of many new methods aimed at computing rate
constants for receptor-ligand binding processes [2], see also:
kbbox.h-its.org. Here, I will describe our studies to explore the
determinants of structure-kinetic relationships and to develop
computationally efficient methods to estimate drug-target binding
kinetic parameters. I will introduce our -random acceleration
molecular dynamics simulation (RAMD) method to compute relative
residence times [3] and discuss how machine learning analysis of
RAMD trajectories [4] and the application of Comparative Binding
Energy (COMBINE) Analysis [5] can be used to decipher the
determinants of drug-target residence times. [1] Stank A, Kokh DB,
Fuller JC, Wade RC. Protein binding pocket dynamics. Acc. Chem
Res., 2016, 49:809-815. [2] Bruce NJ, Ganotra GK, Kokh DB, Sadiq
SK, Wade RC. New approaches for computing ligand- receptor binding
kinetics. Curr Opin Struct Biol. 2018, 49: 1-10. [3] Kokh DB,
Amaral M,……Wade RC. Estimation of drug-target residence times by
-random acceleration molecular dynamics simulations, J. Chem.
Theory Comput. 2018, 14: 3859–3869. [4] Kokh DB, Kaufmann T, Kister
B, Wade RC. Machine Learning Analysis of RAMD Trajectories to
Decipher Molecular Determinants of Drug-Target Residence Times,
Frontiers. Mol. Biosci. 2019, 6: 36. [5] Ganotra GK, Wade RC.
Prediction of Drug–Target Binding Kinetics by Comparative Binding
Energy Analysis. ACS Medicinal Chemistry Letters 2018, 9:
1134–1139.
Abstracts: Keynote Lectures
KL-1. David Winkler Monash, La Trobe, Nottingham Universities,
CSIRO "Machine learning at the (nano)materials-biology
interface"
David A. Winkler, R.M.T. Madiona, N. Welch, B.W. Muir, P.J. Pigram,
Mikulskis, M. Alexander, T.C. Le, M. Penna, I. Yarovsky, S.R.
Ghaemi, B. Delalat, A.L. Hook, S. Gronthos, N.H. Voelcker The past
decade in particular has seen a spectacular rise in research into
materials, with biomaterials in particular playing a dominant role
in regenerative medicine. This has, in part, been driven by
improvements in instrumentation and rapid rise in high throughput
synthesis and characterization methods that now generate orders of
magnitude more data than in previous eras. It is essential to
extract as much information as possible on the molecular details of
materials’ interaction with biological systems to use this for
rational design of materials that generate desirable biological
outcomes while minimizing undesirable side effects. We show how a
concomitant rise in computational and algorithmic research methods
is allowing complex, rich data sets to be efficiently analyzed and
used to design new materials. New artificial intelligence and
machine learning methods, coupled with improved ways of
representing the molecular and physicochemical properties of
materials to train such models and effective sparse feature
selection methods, have addressed the critical need for effective
modeling tools. As in many other areas of science and technology,
deep learning methods are playing increasingly important roles in
providing. Models that make robust, accurate predictions of
biological properties for increasingly diverse classes of materials
Here we illustrate how these powerful but accessible modeling
methods have been used to model a variety of material-biology
systems. We focus on the development of immune- instructive
materials, materials that support growth and proliferation of stem
cells, polymers that do not support attachment of microbial
pathogens or proteins, and materials that minimize foreign body
responses to implantable medical devices.
Abstracts: Keynote Lectures
11
KL-2. Lee Hwee Kuan Bioinformatics Institute (BII) A*STAR "Solving
partial differential equations using Neural Networks"
Hwee Kuan Lee, Kenta Shiina, Sojeong Park Computer simulations are
used widely to perform predictions on physical or biophysical
processes. Most of these computer simulations are essentially
solving partial differential equations that models the physical
process that we wish to predict. However numerical integration is
very computationally intensive leading to long simulation times. We
propose an alternative way to speed up the search for solutions of
partial differential equations using neural networks. As examples,
solving of several systems of partial differential equations will
be presented.
Abstracts: Keynote Lectures
Indian Institute of Science Education and Research (IISER) Pune
"Integrative modeling of the 3D structure of intermediate
filaments"
Neelesh Soni and M.S. Madhusudhan Intermediate filaments along with
micro tubules and micro filaments form the cytoskeletal structure
in cells. Unlike the other two components the molecular structure
of the intermediate filament has not been determined at
residue-level resolution. In this study, we determined the
structure of the Keratin K5-K14 Intermediate filament using
integrative modeling. Our model utilises data from experiments such
as chemical cross linking, X-ray crystallography and SAXS to
resolve the structure of large parts of the filamentous assembly.
Our structure of the Keratin filaments helps identify the molecular
mechanisms behind hundreds of point mutants that disrupt the
filament and hence lead to disease. Our model also suggest how
filament bundles are formed and these findings are consistent with
experimental observations that were not used in model construction.
Further, we have been able to suggest and experimentally validate
new mutations that lead to filament disruption. We believe that
this first 3D model of the full length intermediate filament would
now help study other intermediate filaments. It would also be
useful in designing therapeutic strategies against disease
conditions that stem from filament instability.
Abstracts: Keynote Lectures
13
KL-4. David Ascher Baker Institute and Bio21 Institute "Unraveling
the molecular mechanisms behind mutations and their link to
phenotypes"
The wealth of experimental, structural, genomic and proteomic
information accumulated over the years has opened up opportunities
to use advanced computational approaches to better understand how
mutations will affect protein structure and function. These methods
fall into several major classes based on whether they use protein
sequence or structural information, and whether they rely on
statistical, machine learning or dynamics approaches. Each approach
comes with distinct advantages and limitations; however we have
shown that they are often complementary, and combining distinct
approaches can lead to significantly improved performance. One
problem, especially with machine learning approaches, has been the
unbalanced nature of the curated data- proteins have highly refined
structures and functions, and alterations to it are more likely to
be disruptive. Many of the developed methods therefore do not
perform as well at identifying stabilising mutations. A number of
strategies have been used, with varying success, to address this,
including the use of reverse hypothetical mutations. By refining
how the reverse mutations are implicated, we have developed more
balanced and predictive tools. One of the few approaches that has
been applied to rapidly characterise a wide spectrum of molecular
consequences is the mCSM platform, which uses graph-based
signatures to represent the wild-type environment of a residue in
order to predict pathogenicity and the effects of a mutation on
protein stability and affinity for protein partners, nucleic acids,
metal ions and small molecules, including drugs and ligands.
Analysis of their performance reveals they perform equally well on
homology models. By integrating these approaches, we can understand
the molecular consequences associated with mutations. This has been
successfully used to guide protein engineering, more accurately
identify diseases and predict patient outcomes, and guide the
development of better drugs.
Abstracts: Keynote Lectures
KL-5. Jane Allison University of Auckland "CherryPicker: Automated
parameterisation of large biomolecules for molecular
simulation"
Ivan Welsh, Jane Allison Molecular simulations allow investigation
of the structure, dynamics and thermodynamics of molecules at an
atomic level of detail, and as such, are becoming increasingly
important across many areas of science. As the range of
applications increases, so does the variety of molecules.
Simulation of a new type of molecule requires generation of
parameters that result in accurate representation of the behaviour
of that molecule, and, in most cases, are compatible with existing
parameter sets. While many automated parameterisation methods
exist, they are in general not well suited to large and
conformationally dynamic molecules. I will describe the
CherryPicker method for automated assignment of parameters for
large, novel biomolecules, and demonstrate its usage for peptides
of varying degrees of complexity. CherryPicker uses a graph
theoretic representation to facilitate matching of the target
molecule to molecular fragments for which reliable parameters are
available. It requires minimal user input and creates parameter
files compatible with the widely-used GROMACS simulation
software.
Abstracts: Keynote Lectures
15
KL-6. Debra Bernhardt The University of Queensland "Modelling the
growth and properties of two-dimensional materials"
Wenyu Wei, Saiyu Bu, Debra J. Searles and Qinghong Yuan The
University of Queensland, Australia and East China Normal
University, China The growth of crystalline two-dimensional
materials can be controlled through changes in the catalyst,
feedstock, temperature and pressure. In this talk we will discuss
how these factors influence the structures of graphene,
two-dimensional nitrogen-doped graphene and two- dimensional carbon
nitrides. For example, the growth of graphene using chemical vapour
deposition (CVD) can be catalyzed using copper-nickel alloy
catalysts and the nucleation density and size of the graphene
flakes has been to vary greatly with the copper:nickel ratio.
Computational modelling has allowed us to explain the optimal
ratio, with the computational results in good agreement with
experiment (Y Liu, et al. Advanced Science, 5, 1700961). In another
example we will discuss how experimental conditions can alter the
concentration and type of nitrogen in nitrogen-doped graphene.
Finally, we will show how the electronic properties of the
materials can be tuned using different physical constraints on
structure of layered two-dimensional materials.
Abstracts: Keynote Lectures
16
KL-7. Jonathan Essex University of Southampton "How well do we
model protein electrostatics?"
Fixed-charge atomistic force fields use Coulomb interactions to
model electrostatics. However, because of their empirical, highly
parameterised nature, the extent to which these force fields
correctly model protein electrostatics is unclear. To address this
question, we have compared conventional fixed-charge force fields,
the AMOEBA polarisable force field, linear-scaling Density
Functional Theory, and experimental fields derived using
vibrational Stark effect spectroscopy, to study the electric fields
in two protein systems. The first, the peptidylprolyl isomerase
Cyclophilin A, catalyzes the cis/trans isomerization of the amide
preceding proline residues in proteins. Its mechanism is believed
to be electrostatically modulated, at least in part, by the
conserved R55 residue, which has been proposed to provide a
stabilizing electric field for the transition state. For the
second, ketosteroid isomerase, very large electric fields have been
reported in the binding site (-143 MV cm-1) with implications for
the catalytic mechanism. We find in both systems that the ability
of fixed charge force fields to calculate accurate electric fields
is inferior to that of the AMOEBA force field. Given that AMOEBA is
explicitly designed to model electrostatics through a combination
of distributed multipoles and inducible dipoles, this is perhaps
not surprising. The broader implications of this result for QM/MM
and continuum electrostatic calculations, will be discussed.
.
Abstracts: Keynote Lectures
17
KL-8. Habibah A. Wahab Universiti Sains Malaysia "Drug resistant
influenza strains, what happens before the drugs bind to the active
sites?" The recurrence of influenza pandemics indicated that the
influenza virus has continuously evolved creating a constant threat
to the humanity. Although there are treatment options available,
the increased number of reported drug resistant strains of the
influenza virus demands a complete understanding of the mechanism
of resistance. Many studies have been conducted to uncover the
mechanism of oseltamivir resistances in H274Y NA. However, most of
the reported studies on H274Y were only focused on
drug-bound-system, while direct effects of the mutation towards NA
itself, prior to drug binding, remains unclear. Therefore,
molecular dynamics of NA in apo-form, followed by principal
component analysis and interaction energy calculation, were
performed to investigate the structural changes of NA binding site,
as a result of H274Y mutation. Sliding-box docking suggested that
the binding pathway of OTV was compromised due to this binding site
disruption. This study also highlighted the differences of H274Y
effects in N1-and N2-subtypes and the importance of functional
group at position C6 of sialic acid mimicry. Rational design of a
series of potential neuraminidase inhibitors are also
highlighted.
Abstracts: Contributed Talks
19
CT-1. Sebastian Maurer-Stroh Bioinformatics Institute (BII) A*STAR
"When Sequences meet Structures" Molecular modelling of protein
structures is a wide field and I want to raise awareness of the
many synergies sequence studies can contribute to inform and
complement structural modelling studies and vice-versa. I will
start with successful examples of remote homology detection
supported by structural core motifs, new trends in industry
projects with enzyme design, followed by examples of predicting
peptide-protein interactions using evolutionary sequence
conservation to guide docking and will end by how large-scale virus
sequence analysis combined with structural modelling can provide
powerful insights into viral fitness and drug resistance
mutations.
Abstracts: Contributed Talks
20
CT-2. Ivo C. Martins Universidade de Lisboa "Combining wet lab
experiments with in silico data to understand Zika, West Nile and
Dengue virus capsid protein" Ana S. Martins, André F. Faustino,
Nina Karguth, Nuno C. Santos, Ivo C. Martins Dengue, West Nile and
Zika, closely related viruses of the Flaviviridae family, are an
increasing global threat, due to the expansion of their mosquito
vectors. Our work, dealing with peptides and protein-ligand
interactions in general [1–20], is particulalrly focused on the
capsid (C) protein of these viruses [12–20]. This is a crucial
structural protein that mediates not only viral assembly, but also
encapsidation, by interacting with host lipid systems, as shown by
us, via bioinformatics and wet lab experimental approaches [12-20].
This also led to pep14-23, a drug candidate designed by us [12,15].
We recently investigated further the C protein [17– 20]. It forms a
dimer with a disordered N-terminal region, an intermediate flexible
fold section and a very stable conserved fold region [17,18].
Comparing and analyzing relevant mosquito- borne Flavivirus C
protein sequences and their predicted structures shows alternative
conformations enabled by the N-terminal region essential for its
function [17–20]. Using dengue virus C protein as main model, we
then correlated protein size, thermal stability and function with
its structure/dynamics features [17]. The findings suggest that
minor allosteric changes may modulate the C protein biological
activity. Therefore, this knowledge contributes to future drug
development strategies against Zika, dengue and closely related
flaviviruses. REFERENCES 1. Peptides, 49 (2013). 11. Biotech, 6
(2016). 2. Reprod Biol Endocrinol, 11 (2013). 12. J Virol, 86
(2012). 3. Brain, 140 (2017). 13. Biochem J, 444 (2012). 4. EMBO J,
29 (2010). 14. Sci Rep, 5 (2015). 5. Nat Methods, 7 (2010). 15. ACS
Chem Biol, 10 (2015). 6. EMBO J, 27 (2008). 16. Nanomed (NBM), 10
(2014). 7. Chem Soc Rev, 43 (2014). 17. Int J Mol Sci, 20, 3870
(2019). 8. Arch Biochem Biophys, 531 (2013). 18. Sci Rep, 9 1647
(2019). 9. Proc Natl Acad Sci, 102 (2005). 19. Front Microbiol, 9
(2018). 10. Nanomed, 13 (2018). 20. Front Cell Infect Microbiol, 9
(2019).
Abstracts: Contributed Talks
21
CT-3. Roland G. Huber Bioinformatics Institute (BII) A*STAR
"Integrative Modelling of Viral Genome Structures: Data and
Strategies" Roland G Huber, Wan Yue Dengue (DENV) and Zika (ZIKV)
viruses are clinically important members of the Flaviviridae family
with an 11kb positive strand RNA genome. While structures have been
mapped primarily in the UTRs, much remains to be learnt about how
the rest of the genome folds to enable function and regulation.
Here, we performed structure and interaction mapping on four DENV
serotypes and four ZIKV strains inside their virions and in
infected cells. Comparative analysis of SHAPE reactivities across
serotypes nominated potentially functional regions that are highly
structured, show structure conservation, and low synonymous
mutation rates. Interaction mapping by SPLASH further reveals new
pair-wise interactions, in addition to the circularization
sequence. Approximately 40% of pair-wise interactions form
alternative structures, suggesting extensive structural
heterogeneity. Analysis of shared interactions between serotypes
revealed a conserved macro-organization whereby interactions can be
preserved at their physical locations beyond sequence identities.
Comparing genome structures in virions, released into solution, and
in host cells, revealed that long-range interactions tend to be
disrupted inside cells. Compensatory mutations further demonstrate
the importance of one of these new interactions for virus fitness.
Our findings provide a structural framework to examine DENV and
ZIKV genome organization and serve as a resource for design of RNA
therapeutics that target the RNA structures of viruses.
Abstracts: Contributed Talks
Australian National University "Modelling divalent cations: the
curious case of PsaA"
Hugo MacDermott-Opeskin, Megan O'Mara Manganese homeostasis is
crucial for the viability of the pathogenic bacteria, S.
pneumoniae, protecting against oxidative stress and aiding cellular
metabolism. The substrate binding protein, PsaA, controls the
selective uptake of manganese via coupling to a membrane import
complex. PsaA lacks a metal chelating cofactor and faces
significant competition from other d block metal species.
Competitive and irreversible binding by other d block metals has
been identified as a mechanism for bacterial susceptibility to zinc
and cadmium. In this work compare and benchmark three molecular
dynamics divalent cation models (LJ 6-12, LJ 6-12- 4, multisite
models) to probe how effectively these cation models reproducing
the experimental data for metal ion binding, coordination and
release at the PsaA cation binding site.1,2 We use free energy
calculations to reveal mechanisms of Mn2+ and Zn2+ binding to PsaA.
We show that Mn2+ is scavenged more effectively than competing
metal ions from solution by mobile carboxylates located near the
entrance to the binding site. We demonstrate that ligand
coordination by water molecules is essential in controlling the
reversibility of binding. Our work reveals that the coordination
chemistry of PsaA is precisely controlled to provide selectivity
and reversibility for Mn2+.
Abstracts: Contributed Talks
Australian National University "Molecular Insights into Inhibition
of the neurotransmitter transporter GlyT2"
Katie A. Wilson and Megan L. O'Mara Chronic pain is a condition
that effects 1 in 5 Australians with the prevalence of chronic pain
expected to increase as the population ages. Unfortunately, the
current treatments have low efficacy and unacceptable side effects.
Therefore, new treatments for chronic pain must be explored to
develop new drugs to safely manage chronic pain. The glycine
transporter, GlyT2, is a specific neurotransmitter transporter
involved in neuropathic pain and therefore is of interest for the
therapeutic treatment of chronic pain. Interestingly, previous work
has shown that an endogenous bioactive lipid, N-arachidonyl-glycine
(NAGly), inhibits GlyT2 and successfully reduces chronic pain.
Based on this work, NAGly has been used as a lead compound to
develop novel, potent, selective, and metabolically stable
compounds that are inhibitors of GlyT2. The developed inhibitors
all contain a lipid structure with a hydrophilic amino acid based
headgroup and a single hydrophobic lipid tail. Using a combination
of atomistic and coarse grain molecular dynamics simulations the
binding of the inhibitors to GlyT2 has been characterised. Based on
these calculations we proposed a mechanism of inhibition that is
mediated by cholesterol and involves inhibitor binding to a novel
extracellular allosteric binding site. Furthermore, a
structure-function relationship has been developed for a variety of
GlyT2 lipid inhibitors that vary in the stereochemistry and
chemical composition of the lipid headgroup, and the number of
carbon atoms in the lipid tail.
Abstracts: Contributed Talks
University of Santiago de Compostela "From molecular dynamics
simulations of cyclodextrin-based molecular machines to macroscopic
functional materials"
Ángel Piñeiro, Pablo F. Garrido, Martín Calvelo, Rebeca
García-Fandiño The behavior of native cyclodextrins (CDs) in
aqueous solution and at the air/water interface is much more
complex than expected just considering their apparently simple
molecular structure. Consequently, the correct prediction of their
different properties and skills such as their solubility, their
ability to adsorb at interfaces or to encapsulate different
molecules is not straightforward; not to mention their propensity
to aggregate forming different patterns in the bulk and at the
interface. It is not a surprise then that the behaviour of modified
cyclodextrins, as well as that of the supramolecular complexes they
form upon interacting with different types of molecules, is not
trivial. The surface film resulting from the self- assembly of some
of these complexes at the water/air interface exhibits a strong
viscoelastic response to mechanical perturbations perfectly visible
in macroscopic experiments. In the present work we will show how
this behavior can be explained from the cooperative effect of 2:1
molecular complexes showing a piston-like movement of the guest
molecule inside the nanocylinder formed by the two CD, coordinated
with a chiral-driven directional oscillation of the primary
hydroxyl groups of the cyclodextrins. Moreover, a surprisingly
specific electric dipole moment vector field and packing of the
water molecules around the complex indicate that those solvent
molecules should be considered as a part of the molecular machine.
Acknowledgments This work was supported by the Spanish Agencia
Estatal de Investigación (AEI) and the ERDF (RTI2018-098795-A-I00).
M.C. thanks to Xunta de Galicia for a predoctoral fellowship. P. F.
G. thanks the Spanish Ministry of Economy and Competitiveness and
the European Social Fund for his predoctoral research grant,
reference BES-2016-076761. R.G.-F. thanks to Ministerio de Ciencia,
Innovación y Universidades for a “Ramón y Cajal” contract
(RYC-2016-20335).
Abstracts: Contributed Talks
La Trobe Institute For Molecular Science "Computational Studies of
Insulin Structure and Function"
Nicholas A. Smith, Brian J. Smith Diabetes Mellitus, a disease
which is principally caused by the functional dysregulation of the
peptide hormone insulin, effects millions of people worldwide with
little discrimination. The hormone which is produced in the
pancreas and acts by binding to the insulin receptor expressed on
cells throughout the body, plays an integral role in homeostatic
metabolism, and the processing of glucose from meals. Although
research of insulin and diabetes spans close to a hundred years,
leading to numerous Nobel prizes, an extensive body of literature,
and most recently an understanding of the atomic structures of the
complex between the hormone and receptor, the prevalence of the
disease continues to increase. The emergence of computational
techniques, principally that of the simulation method molecular
dynamics, seeks to help arrest this trend, allowing researchers to
comprehend molecular interactions which occur on an atomic scale,
seldom achievable by other experimental techniques. Exploiting this
method, we focus here on conducting a comprehensive investigation
into the activation of the insulin receptor by the native insulin
peptide and insulin analogues. We examine through a combination of
computational modelling, simulations, free energy investigations
and thermodynamic analysis naturally occurring insulin analogues;
from that of insulin Wakayama, an analogue containing a single
amino acid mutation which causes significant loss of function,
single-chain insulins which are refractory to thermal degradation,
and cone snail venom insulins. We make numerous conclusions on the
internal dynamics and functional interactions which underpin the
molecular interaction between the hormone and the insulin receptor;
with the perspective gained seeking to assist in the designing of
modern therapeutics which have a more advantageous pharmacokinetic
profile.
Abstracts: Contributed Talks
Bioinformatics Institute (BII) A*STAR "Structome: Exploring the
structural neighbourhood of proteins"
Ashar J. Malik*[Bioinformatics Institute, Agency for Science,
Technology and Research, Singapore], Jane R. Allison [School of
Biological Sciences, University of Auckland, New Zealand]
Evolutionary relationships are conventionally uncovered using
protein sequences. Protein structure as opposed to the sequence can
hold evolutionary signals over longer timescales and can therefore
prove useful towards uncovering deep evolutionary relationships. To
this end, Structome has been developed as a resource for
researchers to quickly determine the structural neighbourhood of a
query structure. The structural neighbourhood comprises protein
structures within a certain user selected structural similarity
cutoff. This resource, therefore allows the inspection of the
neighbourhood of a query protein structure from which inferences
can be made about the evolutionary relationships, through the use
of phylogenetic networks. Domain annotation from SCOP and CATH
databases are also provided, to allow users to validate their
observations, along with sequence similarity. Covering ~70% of the
proteins in RCSB PDB, Structome is a comprehensive tool for the
analysis of the protein structure landscape.
Abstracts: Contributed Talks
CSIR-Institute of Genomics and Integrative Biology "Human LC3 and
GABARAP subfamily members achieve functional specificity via
specific structural modulations"
Nidhi Jatana, David B. Ascher, Douglas E.V. Pires, Rajesh S.
Gokhale & Lipi Thukral Autophagy is a key cellular degradation
mechanism in nearly all organisms. Core components of this
machinery are the six human Atg8 orthologs that play a critical
role in autophagosome initiation and expansion. Although LC3B
labeling is perhaps the most well-established and representative
marker for autophagy, the ambiguity regarding its evolutionary
partners (LC3A, LC3C, GABARAP, GABRAPL1, and GATE16) and their
binding specificities is striking. In part, the difficulty in
constructing functional signatures arises due to remarkable
structural similarity as all of them share ubiquitin fold. The
question arises as to how specific sequence variation amongst the
family members lead to different substrate recognition preferences.
To understand this, we developed a computational pipeline to define
structural determinants of human Atg8 family members that dictate
functional diversity. We observed a clear evolutionary separation
between Atg8 orthologs and defined novel sequence recognition motif
for human LC3 and GABARAP subfamilies. By analyzing molecular
dynamics (MD) trajectories of known protein structures, we observed
that functional modules or microclusters reveal a pattern of
intramolecular network, including distinct hydrogen bonding of key
residues that may directly modulate their interaction preferences.
In addition, multiple simulations were performed to characterize
how these proteins interact with a common protein binding partner,
PLEKHM1. This analysis showed remarkable differences in binding
modes via intrinsic protein dynamics, with PLEKHM1-bound GABARAP
complexes showing less fluctuations and higher number of contacts.
We further demonstrate that distinct cancer- related mutations were
likely to lead to significant structural changes. Our findings
provide an extensive structural framework of diversity within human
ATG8 proteins they may serve as an underlying mechanism of
cross-talk and molecular control with other related signaling
pathways.
Abstracts: Contributed Talks
University of Auckland "Phylogenetic inference from protein
structure using a toroidal diffusion model"
Alex Popinga, Fabio Mendes, Jane Allison Typical models designed to
infer phylogenetic trees, evolutionary relationships of the input
data, rely upon aligned sequence data and either discard completely
or only indirectly/partially take into account structural
information. Not taking into account structural data can be
problematic, particularly for deep-rooted trees, as protein
structures may contain information about evolutionary trajectories
that has been lost in their amino acid and corresponding gene
sequences. We introduce two novel extensions of a recently
published angular diffusion model for homology detection (Golden et
al., 2017) in the Bayesian phylogenetic inference package BEAST2
(Bouckaert et al., 2019). The first model, SPITE (Structural
Phylogenetic Inference using Toroidal Evolution), is the foundation
for performing phylogenetic inference using protein structures
described by their dihedral angles. MINOTOR (Molecular
dynamics-Informed iNference On a TORus) is our proposed extension
of SPITE that will incorporate simulations of molecular dynamics to
inform the likelihood of observing sampled proteins as well as the
probability of each ancestral structure.
Abstracts: Contributed Talks
Bioinformatics Institute (BII) & Experimental Drug Development
Centre A*STAR "Planet of the Apps: Scientific Phone Apps &
Mobile Devices"
Many significant biomedical discoveries of old were made in the
private property of famous scientists e.g.Leeuwenhoek and
Archimedes. Today, discoveries are made in brightly-lit, hi- tech,
ergonomic buildings that house research institutes. While such
development is advantageous in many aspects, the spatial
restriction of research into well-organized structures may delay
and limit the spontaneity necessary for discoveries. The smartphone
and peripheral mobile devices have the potential to not only
increase the productivity and mobility of biomedical research, but
also restore some freedom from spatial constraints. One possible
way this can occur is the development of a mobile biomedical lab
that allows researchers to carry out core research processes
‘on-the-go’ without being spatially restrained within a building or
availability of equipment. For this exciting prospect, we surveyed
the Google and Apple app stores, discussing the limitations and the
potential of this area. Based on the developments, it appears to be
just a matter of time before the majority of biomedical labs
processes and equipment become mobile, centred on the smartphone
andperipheral devices.
Abstracts: Contributed Talks
Schrödinger, Inc., Bangalore, India "Molecular modelling - an
industrial perspective"
Schrödinger INC is pioneer in developing applications for both
materials and molecular design. There are more than 2000 research
groups across the globe use Schrödinger simulation suites for their
research. Understanding the molecular level structure-property
relationship allows the researchers to effectively tackle many
complicated problems and fine tune the properties of materials into
the desired range. Schrodinger has designed several automated
workflows to effectively predict the chemical and physical
properties and stability of materials. This presentation will show
few case studies from pharmaceutical formulations, polymers, OLED,
catalysis and energy storage sectors of the industry.
Abstracts: Contributed Talks
CT-13. Raghu Rangaswamy
Vice President, Schrödinger, Inc., Bangalore, India "FEP+ the game
changer for lead optimisation in drug design"
Over the past decade, computational drug design has undergone
tremendous advances in the form of forcefield development,
advancement in algorithms, availability of enormous amount of
crystal structures, GPU and cloud computing etc. These advances in
computational development and enormous amount of data has enabled
the molecular simulations to identify the potential lead molecules
much faster and accurate. This is really a boon for the pharma R
& D a lot where they can reduce the cost and identify the lead
molecules much faster.
The presentation would highlight on the developments in lead
identification methods and on the lead optimisation methods. Today
free energy perturbation calculation methods have matured to the
point where we can calculate the binding affinity of a molecule
with in few hours, which is a boon to a chemist to decide and go
for synthesis. We have observed in our drug discovery
collaborations that the use of free energy perturbation can lead to
a measurable and significant acceleration of drug discovery
projects in prospective applications.
The presentation would highlight few case studies from our drug
discovery collaborations. How we did virtual screening and selected
few lead molecules from database of millions of compounds. How the
selected leads were expanded using computational methods to find
potential analogs and how we did lead optimization to select very
few successful lead candidates which are at par with experimental
results.
Abstracts: Contributed Talks
Santiago de Compostela University "“Intelligent” Materials
Targeting Bacterial And Cancer Cell Membranes: How Computational
Tools Can Help?"
Rebeca Garcia-Fandino, Martin Calvelo, Gideon F. Tolufashe, Antonio
Peón, Bárbara Claro, Alicia Muñoz, Daniel Conde, Ángel Piñeiro,
Margarida Bastos, Juan R. Granja There is a need for the
development of new antineoplastic and antimicrobial therapies, with
higher selectivity, leading to fewer side effects than current
ones. One strategy proposed is the use of bacterial or cancer cell
membranes as a therapeutic target so that their basic properties
are perturbed, altering the membrane potential and inhibiting the
control functions on the signaling, communication or production
bioenergy processes. Therapeutic peptides are a novel and promising
approach for the development of both antimicrobial and anti-cancer
agents that could specifically target bacteria or cancer cells with
lower toxicity to normal tissues, which will offer new
opportunities for cancer and infection treatments. Since lipid
membranes are the major target of most of these therapeutic
peptides, the development of drug resistance is less likely to
occur since damaging cytotoxicity can take place within minutes of
peptide introduction. Traditional design and optimization studies
of peptides (or peptidomimetics) are known to be expensive and
time-consuming. A detailed understanding of the molecular details
of the membrane permeabilization process would allow the rational
design of new molecules with the same mechanism of action, but with
improved activity, selectivity, and bioavailability. Recent
advances in computer power and methodology, including Molecular
Dynamics simulations, have made possible to systematically explore
events that take place into ranges where direct comparison and
experimental testing are starting to be feasible, realizing the
synergistic potential of a combined in-silico/in-vitro approach in
the characterization of the membrane destabilization process by
antitumoral or antimicrobial molecules. In this talk, some recent
examples of our research related to the study of different peptides
and peptidomimetics acting at the membrane level will be
illustrated.
Abstracts: Contributed Talks
CT-15. Ricardo Mancera
Curtin University "How does sucrose change its mechanism of
stabilization of lipid bilayers during desiccation? The roles of
hydration and concentration"
Slawomir S. Stachura, Chris J. Malajczuk and Ricardo L. Mancera The
interactions between sugars and membranes are thought to be
responsible for the stabilisation of cells during desiccation and
freezing, usually associated with a decrease of the main phase
transition temperature of phospholipid bilayers. However, the
underlying molecular mechanism is still not well understood. There
are two opposing views on how this is achieved: the direct
sugar-phospholipid interaction at the bilayer interface (water
replacement hypothesis) and an entropy-driven phase transition
where sugar molecules concentrate away from the lipid interface
(hydration forces explanation). Various experiments support these
two mechanisms but molecular dynamics (MD) simulations have
overwhelmingly shown the occurrence of direct sugar-phospholipid
interactions. We have conducted MD simulations of DOPC bilayers at
different levels of hydration and in the presence of different
sucrose contents as a representative system. Sucrose was found to
behave in a manner that depends on both the sucrose contents and
the level of hydration: at high sucrose concentration at low
hydration it is best described by the hydration forces explanation
model, whereas at low sucrose concentration it is consistent with
the water replacement hypothesis. At low concentration, sucrose
molecules are revealed to preferentially interact directly with the
bilayer interface, while at high concentration they preferentially
accumulate in the inter-bilayer solution. We have observed for the
first time that the transition between the two modes of interaction
is determined by the saturation of the lipid bilayer interface with
sucrose molecules, which occurs more rapidly as the level of
hydration decreases.
Abstracts: Contributed Talks
National Yang-Ming University "Planar and spherical - comparison of
lipid dynamics in bilayers"
Meng-Han Lin and Wolfgang B. Fischer Spherical bilayers such as
vesicles comprise an important container system as drug delivery
vehicles as well as bio-reactors. Lipid composition and
functionalization of the vesicles are a prerequisite for their area
of application. Computational screening of the properties of
vesicles of different composition constitutes a desirable tool to
support the development of this system. Computational lipid
bilayers and vesicles are built using zwitterionic (POPC, DOPC) and
negatively charged (DOPS) lipids and mixtures thereof (5 % DOPS in
either POPC or DOPC) as coarse-grained models (MARTINI) in
molecular dynamics (MD) simulations. Vesicles (Ø 10 nm) are
generated by placing each lipid on a sphere based on the
area-per-lipid value leading to stable and precisely defined
vesicles (tailor-made vesicles). The effect of restrained positive
point charges (Ca-ions) external to bilayers and vesicles on the
dynamics of the lipid molecules is investigated. The curves shape
leads to a reduction of the diffusivity of the lipids. Independent
of the shape of the bilayer and the type of mixture, the presence
of the external charges reduces the diffusivity of the DOPS
molecules the most. The reduced lipid diffusivity leads to lower
DOPS density around the external charges in the vesicles than
around the external charges in the planar lipid bilayer.
Abstracts: Contributed Talks
CT-17. Baris Demir
The University of Queensland "Cation effect on the electrolyte
structure with an applied potential: a molecular level
investigation using a constant potential method"
Baris Demir, Debra J. Searles Future energy storage devices require
both high power and energy densities. In general, batteries have a
high energy density whereas supercapacitors have a high power
density. Developing devices that achieve both is therefore of
significance. A striking difference between these two systems is
the energy storage mechanism. Energy is stored on the electrodes of
a battery through chemical reactions while it is stored via ion
adsorption on the electrodes in supercapacitors. To advance the
performance of supercapacitors, it is vital to understand the
molecular-level formation of layers in the vicinity of electrodes,
or the electric double layers (EDLs) as this is a key factor for
their performance. In this work, we investigate the effect of the
cation structure and dynamics on the layering in the
electrolyte-to-electrode interface using molecular dynamics (MD)
simulations. We implement a constant potential method to fully
capture the dynamics of C2mimNTf2 and N4,1,1,1NTf2 ionic liquids
(IL) at varying potential differences applied across the
electrochemical cell. Two ILs are examined: C2mimNTf2 and
N4,1,1,1NTf2, with graphene electrodes. A constant potential method
has been applied to model electrochemical cells at the molecular
level. After equilibration of the IL at 294 K in the presence of
graphene electrode, a potential difference was applied. Our MD
simulation results indicate that the applied potential created
ion-dense layers at both electrode interfaces. This affected the
charge density distribution in the EDL. Asymmetric charge density
distribution profiles formed in the EDL on both electrode surfaces
when the potential difference was turned on. The EDLs have
substantially different compositions near both electrodes as a
function of applied potential. We explore the charging/discharging
behaviour of supercapacitors at various potential difference by
varying the IL to better understand the dynamics of ions for the
aim of tuning and designing new ILs.
Abstracts: Contributed Talks
University of Wollongong "Atomistic insights into photoprotein
formation: Computational prediction of the properties of
coelenterazine and oxygen binding in Obelin"
Thomas M. Griffiths, Aaron J. Oakley, Haibo Yu Bioluminescence in
marine systems is dominated by the use of coelenterazine for light
production. The bioluminescent reaction of coelenterazine is an
enzyme catalysed oxidative decarboxylation: coelenterazine reacts
with molecular oxygen to form carbon dioxide, coelenteramide and
light. One such class is the Ca2+-regulated photoproteins. These
proteins bind coelenterazine and oxygen, and trap
2-hydroperoxycoelenterazine, an intermediate along the reaction
pathway. The reaction is halted until Ca2+ binding triggers the
completion of the reaction. There are currently no reported
experimental, atomistic descriptions of this ternary Michaelis
complex. This study utilised computational techniques to develop an
atomistic model of the Michaelis complex. Extensive molecular
dynamics simulations were carried out to study the interactions
between four tautomeric/protonation states of coelenterazine and
wide-type and mutant obelin. Only minor differences in binding
modes were observed across all systems. Interestingly, no basic
residues were identified in the vicinity of the N7-nitrogen of
coelenterazine. This observation was surprising considering that
deprotonation at this position is a key mechanistic step in the
proposed bioluminescent reaction. This work suggests that
coelenterazine binds either as the O10H tautomer, or in the
deprotonated form. Implicit ligand sampling simulations were used
to identify potential O2 binding and migration pathways within
obelin. A key oxygen binding site was identified close to the
coelenterazine imidazopyrazinone core. The O2 binding free energy
was observed to be dependent on the protonation state of
coelenterazine. Taken together, the description of the
obelin-coelenterazine-O2 complexes established in this study
provides the basis for future computational studies of the
bioluminescent mechanism.
Abstracts: Contributed Talks
Taylor's University "Benchmarking the performance of MM/PBSA in
virtual screening enrichment with the GPCR-Bench dataset"
Mei Qian Yau, Abigail L. Emtage, Jason S.E. Loo Recent
breakthroughs in G protein-coupled receptor (GPCR) crystallography
and the subsequent increase in number of solved GPCR structures has
allowed for the unprecedented opportunity to utilize their
experimental structures for structure-based drug discovery
applications. As virtual screening represents one of the primary
computational methods used for the discovery of novel leads, the
GPCR-Bench dataset was created to facilitate comparison among
various virtual screening protocols. In this study we utilize 16
GPCR targets, consisting of 3026 actives and 182,927 decoys from
the GPCR-Bench dataset, to benchmark the performance of Molecular
Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) in improving
virtual screening enrichment obtained from docking using Glide. The
top 10% of the docked database was rescored using MM/PBSA, using
both the top-ranking pose and the top ten poses. We additionally
performed the MM/PBSA rescoring following the Binding Estimation
After Refinement protocol, which consists of a short 100ps MD
simulation and energy minimization followed by rescoring. Our
results indicate that MM/PBSA performance in improving enrichment
factors was mixed. MM/PBSA rescoring resulted in an improvement in
the EF1% for 5 targets and a decline for 11 targets, while the EF5%
showed an improvement for 7 targets and a decline for 9 targets.
While some targets such as ADRB1 and CRFR1 showed significant
improvements when using MM/PBSA rescoring, other targets
demonstrated significant decline. There was no clear benefit in
rescoring the top ten binding poses compared to the top docked
pose, as well as in refinement using short MD simulations. In both
cases, although virtual screening performance varied between
individual targets, we observed only small differences in
enrichment factors on average. Overall, the performance of MM/PBSA
rescoring in improving virtual screening enrichment obtained from
docking of the GPCR-Bench dataset was found to be relatively
modest.
Abstracts: Contributed Talks
CT-20. SrinivasarRaghavan Kannan
Bioinformatics Institute (BII) A*STAR "Macrocyclization of an all-D
linear peptide improves target affinity and imparts cellular
activity: A novel stitched α-helical peptide modality"
Srinivasaraghavan Kannan, Pietro G. A. Aronica, Simon Ng, Dawn
Thean Gek Lian, Yuri Frosi, Sharon Chee, Jiang Shimin, Tsz Ying
Yuen, Ahmad Sadruddin, Hung Yi Kristal Kaan, Arun Chandramohan, Jin
Huei Wong, Yaw Sing Tan, Fernando J. Ferrer, Prakash Arumugam, Yi
Han, Shiying Chen, Christopher J. Brown, Charles W. Johannes, Brian
Henry, David P. Lane, Tomi K. Sawyer, Chandra S. Verma, Anthony W.
Partridge Peptide-based inhibitors hold great potential for
targeted modulation of intracellular protein- protein interactions
(PPIs) by leveraging vast chemical space relative to primary
structure via sequence diversity as well as conformationally
through varying secondary and tertiary structures. However, the
development of peptide therapeutics has been hindered because of
their limited conformational stability, proteolytic sensitivity and
cell permeability. Several contemporary peptide design strategies
address these issues to varying degrees. Strategic macrocyclization
through optimally placed chemical braces such as olefinic
hydrocarbon crosslinks, commonly referred to as staples, may
address these issues by i) restricting conformational freedom to
improve target affinities, ii) improving proteolytic resistance,
and iii) enhancing cell permeability. Conversely, molecules
constructed entirely from D-amino acids are hyper-resistant to
proteolytic cleavage, but generally lack conformational stability
and membrane permeability. Since neither approach is a complete
solution, we have combined these strategies to identify the first
examples of all-D α-helical stapled and stitched peptides. As a
template, we used a recently reported all D-linear peptide that is
a potent inhibitor of the p53-Mdm2 interaction, but is devoid of
cellular activity. To design both stapled and stitched
all-D-peptide analogues, we used computational modelling to predict
optimal staple placement. The resultant novel macrocyclic all
D-peptide was determined to exhibit increased α-helicity, improved
target binding, complete proteolytic stability and, most notably,
cellular activity.
Abstracts: Short Talks
Indian Institute of Technology Madras "Molecular Basis of
Differential Stability and Temperature Sensitivity of Zika versus
Dengue Virus Envelopes"
Chinmai Pindi, Venkat R Chirasani, Mohd Homaidur Rahman, Mohd
Ahsan, Prasanna D Revanasiddappa, and Sanjib Senapati* Rapid spread
of zika virus (ZIKV) and its association with severe birth defects
have raised worldwide concern. Recent studies have shown that ZIKV
can survive in high fever, unlike dengue and other flaviviruses. In
spite of recent cryo-EM structures that showed similar architecture
of zika and dengue virus (DENV) envelopes, little is known that
makes the former so unique. Here, we unravel the molecular basis of
greater thermal stability of ZIKV envelope over DENV by employing
all-atom molecular dynamics (MD) simulations. Our results show that
ZIKV envelope retains its structural integrity while DENV envelope
loosens up through the inter-raft interfaces. Protein structural
network extracted from simulation data traced crucial residues
involved in electrostatic and H-bonding interactions that make the
ZIKV raft- raft interfaces robust. The residue-level information
obtained here could pave way for designing small molecule
inhibitors and specific antibodies to inhibit ZIKV E protein
assembly and membrane fusion.
Abstracts: Short Talks
ST-2. Syeda Lubna
BITs Pilani "Evolution of NS1 protein from H1N1 influenza A virus
across the Indian population from 2007 to 2019"
Debashree Bandyopadhyay, Suma Chinta, Prakruthi Burra, Kiranmayi
Vedantham, Sibnath Ray, and Syeda Lubna. Background: Pandemic
outbreak of influenza virus dated back to 2009, in India. Severity
in viral infection varied across different Indian states in last
ten years, indicating possible changes in viral genome. NS1 protein
of H1N1 virus is a potent antagonist to host antiviral immune
response system, thus contributing directly towards viral
propagation and pathogenicity. Objective: This work documented
comparative analyses of NS1 protein sequences and structures from
Indian and global isolates and their possible consequences on NS1
interacting partners. Methods: Sequence analyses of NS1 protein
were based on public and restricted databases. Crystal structure
analyses and hydrophilicity calculations were based on PDB
database. Antigenicity of the NS1 was predicted and compared across
all the sequences, based on online tools. Functional changes in NS1
proteins were curated from literature, over last one hundred years.
Results: Sequence analyses have identified changes in NS1 sequence
positions, 25, 26, 48, 55 and 67, within the time span 2007 to 2017
from Indian isolates. Those residue positions were present on the
antigenic segments of NS1 proteins from Indian isolates on and
after 2015, only. Changes in hydrophilicity of four residues, 25,
26, 48 and 67 were noted based on NS1 crystal structures obtained
from various isolates at different time. Conclusion: Overall study
indicated shift of the antigenic hydrophilic surface of NS1 protein
from Effector Domain (ED) to RNA Binding Domain (RBD) as the virus
evolved from 2009 to 2017 in India. These changes in NS1 proteins
lead to alteration of interacting cellular partners.
Abstracts: Short Talks
41
ST-3. Raphael Tze Chuen Lee Bioinformatics Institute (BII) A*STAR
"FluSurver: current status and future development plans"
Raphael Tze Chuen LEE, Sebastian Maurer-Stroh FluSurver is an
online tool that helps researchers analyze, identify and interpret
the phenotypic consequences of mutations in influenza sequences
sampled from routine surveillance efforts and new influenza
outbreaks. It is widely used by National Influenza Centres, WHO
Collaborating Centres and users of the EpiFlu database by the
GISAID initiative. FluSurver has aided the discovery of new
influenza strain variants with altered antiviral susceptibility,
host specificity, glycosylation and shifts in antigenic properties.
Currently, it uses curated mutational data from the literature to
flag them out to users, highlights mutations on structural models,
collates epidemiological data and processed them for geographic and
temporal visualization. Going forward, it will allow users to
compare their sequences with pre-built genome phylogenies, conduct
predictions on antigenic drifts and egg adaptations, and allow
users to visualize the local and global transmission chains of
their viruses. We will also highlight some areas of our research
where the use of structural models has assisted us in the analysis
of mutations and better understand the biology of the virus.
Abstracts: Short Talks
Bioinformatics Institute (BII) A*STAR & The University of
Manchester "Solvent mapping approach for uncovering cryptic pockets
in membrane-bound proteins"
Lorena Zuzic, Jan K. Marzinek, Jim Warwicker, Peter J. Bond
Flaviviruses are vector-borne human pathogens which include viruses
such as dengue, Zika, yellow fever virus or the West Nile virus.
Dengue virus is associated with 390 million clinical cases
worldwide and the research into a safe and effective neutralizing
antibody or a drug molecule is still ongoing, with a particular
focus being placed on a viral envelope consisting of a protein and
a membrane component. Molecular dynamics simulations with small
organic probes added to the solvent are being used as a method to
efficiently reveal cryptic pockets usually hidden from the water
environment. We have designed a benzene-mapping method which relies
on a modified force-field with added repulsive forces between the
membrane and the benzene molecules, enabling effective cryptic
pocket discovery even with a membrane present in the system.
Repulsion parameters were optimized using a system containing a
heterogeneous membrane. The method was tested on a dengue envelope
and subsequently applied to six viral strains with a goal of
finding a cryptic pocket conserved within the flaviviral family and
with a potential to be a drug-binding site. Cryptic pocket which
experimentally binds n-octyl-β-D-glucoside was consistently
revealed in all simulations with benzene probes in the solvent. The
addition of benzene also enhanced the flexibility and hydrophobic
exposure of pockets within the domain III across all simulated
systems, suggesting the drug-binding capacity of the envelope
protein and the potential to modify or obstruct the interactions
between the virus and the host receptors.
Abstracts: Short Talks
Indian Institute of Technology Madras "Electrostatically determined
asymmetry of substrate binding in HIV-1 protease: A comprehensive
MD simulation study"
Mohd Ahsan, Sanjib Senapati HIV-1 protease (HIVpr), an aspartyl
protease is one of the key viral enzymes in the life cycle of HIV
as it mediates the processing of gag and gag-pol polyproteins into
protein products essential for viral maturation. Therapeutic
inhibition of this protein affects the viral maturation and results
in the formation of immature noninfectious virions. Hence, HIV
protease is one of the prime targets to combat HIV infection.
Currently, more than ten FDA approved drugs acting as competitive
inhibitors are available in the market against HIV protease.
However, studies have reported the emergence of resistance making
these drugs ineffective. Drug resistance in protease causes
imbalance in the molecular recognition process where the resistant
enzyme no longer allows the effective binding of drug molecules but
still can recognize and process the natural substrates. Hence,
detailed understanding of substrate binding and recognition is
required to combat drug resistance. Here, in this comprehensive MD
simulation study we show that all the substrates under the study
when bound to the active site displayed a asymmetrical binding with
higher interaction energy towards unprotonated monomer, both drugs
showed a symmetrical binding with similar interaction energy
towards each monomer. This was primarily due to the preferential
binding of backbone atoms (BB) of substrate residues with
unprotonated monomer and such a behavior is not observed in the
rigid drug molecules. Further, our electrostatic calculations
revealed considerate difference in electrostatic interaction energy
of substrates and drug molecules. Based on our results we propose
that imparting of asymmetric binding pattern by improving
electrostatic component and fitting in substrate envelope by vander
waal component in drug molecules would make them less susceptible
to resistant mutations.
Abstracts: Short Talks
Aarhus University "A generic protocol for constructing nanodiscs in
silico"
Lisbeth Ravnkilde Kjølbye, Birgit Schiøtt A quantum leap in studies
of membrane proteins took place upon the establishment of the
Nanodisc (ND) technology in the late 1990s. In NDs engineered
versions of the human apolipoprotein I (Apo-AI), referred to as
membrane scaffold proteins (MSPs), self-assemble into discoidal
phospholipid bilayers wrapped with an amphipathic helical belt
surrounding the alkyl chains on the phospholipids. NDs can be used
to obtain membrane proteins stable in solution, a main advantage
compared to other available membrane mimics. The selection of the
MSP has so far been governed by two opposing considerations: i)
optimal nanodisc stability obtained with small sizes and ii)
minimization of the finite size effects, obtainable with larger
sizes. A faster and easier approach to select the optimal ND for
the membrane protein in question, can be obtained by getting a
baseline understanding of how the lipid properties change with size
and MSP variant. In this work we present a generic protocol for
constructing molecular models of NDs for molecular dynamics
simulations. The protocol is written in python, making it fast and
easy to modify. We validated and tested the protocol by simulating
seven different NDs in various sizes and versions. The structural
and lipid properties were analysed and shown to be in good
agreement with previously reported studies.
Abstracts: Short Talks
The University of Auckland "Markov State Model of antibacterial
peptides"
Aparajita Chakraborty, Paul Harris, Margaret Brimble, Bettina
Keller, Jane Allison Modern medicine relies heavily on antibiotics
for treating bacterial infections. However, bacterial resistance
against many antibiotics has become a major issue, and this problem
is expected to become much more serious in the not-too-distant
future. This drives the continuous search for new synthetic and
natural antibacterial agents. One promising source of these is
antimicrobial peptides. Antimicrobial peptides are short peptides
often produced by bacteria themselves as a defence mechanism while
competing for food and resources in “bacterial wars”. [1] Our study
focuses on linear structural analogues of battacin. To understand
completely the functionality of these analogues we need to capture
a detailed picture of both the thermodynamics and the kinetics of
the system at atomistic level. Molecular dynamics simulations can
provide this, but unfortunately, it is extremely challenging to
reach biologically relevant timescales with molecular dynamics
simulation, and hence even more challenging to obtain statistics
necessary for accurate understanding of the characteristics of the
system. Using Markov State Models (MSMs), however, we can overcome
this issue. A Markov model represents a network of conformational
states and a transition probability matrix describing the chances
of movement from one state to another at a small time interval.[2]
Since the states in an MSM are defined based on kinetic criteria
rather than on geometric criteria, we can accurately identify the
boundaries between free energy basins and model the complete free
energy landscape of these peptides. References- 1. Cornforth, D.M.;
Foster, K.R., Antibiotics and the art of bacterial war. Proceedings
of the National Academy of Sciences 2015, 112 (35), 10827-10828. 2.
J. H. Prinz et al., “Markov models of molecular kinetics:
Generation and validation,” J. Chem. Phys.2011, vol. 134, no.
17.
Abstracts: Short Talks
Aarhus University "Allosteric Communication in the NMDA
Receptor"
Nils A. Berglund, Jose Flores-Canales, Birgit Schiøtt The NMDA
receptor (NMDAR) is a ligand-gated ion channel present in
postsynaptic neurons. It is part of the glutamate receptor family
and plays an essential function in synaptic plasticity and memory
formation. Dysregulation of this receptor has been implicated in a
range of conditions including Alzheimer’s, epilepsy, schizophrenia
and depression. The NMDAR is therefore of great therapeutic
interest, and modulation of the receptor has thus far shown
promising results. One method used to gain additional insight into
the intricacies of the NMDAR is molecular dynamics. This can
provide atomistic scale insight into the interactions between
protein and ligands, as well as any alterations in protein dynamics
based on ligand binding, something that is of great interest in
drug development. A significant challenge in simulations of the
NMDAR is the size of the protein, as well as the number of loosely
connected domains. Crystal structures have resolved the structure
of the amino-terminal, ligand-binding and trans- membrane domains,
together making up over 3000 residues, leading to systems over
500,000 atoms in size, resulting in sampling issues. The large size
of the system makes these simulations expensive to run and the
number of domains means long timescales are required to ensure
inter-domain communication is achieved and the protein reaches an
equilibrated state. For this reason alternative ways of gaining
atomistic insight than expensive multi- microsecond simulations are
required, in this work we used allosteric network analysis to
compare the inter-domain communication pathways in the presence and
absence of ligands, showing clear differences between the apo state
and in the presence of glutamate and glycine.
Abstracts: Short Talks
ST-9. Lanie Ruiz-Perez
Curtin University "Permeation of short peptides across a stratum
corneum model: application of a new, flexible enhanced sampling
method"
Lanie Ruiz-Perez, Carlo Martinotti, Evelyne Deplazes and Ricardo L.
Mancera In the context of dermal and transdermal drug delivery
there is great interest in predicting which drug candidates show
faster permeation rates across the stratum corneum (SC), the outer
layer of the skin which acts as a barrier. The extracellular
environment of the SC features a series of stacked lipid bilayers
(LBs) in the gel phase. The ceramide-rich composition and high
dehydration state of these LBs contribute to their remarkable
resistance to permeation of exogenous compounds (1). The slow
dynamics of LBs in the gel phase hampers the accuracy of
permeability estimates from molecular dynamics simulations.
Umbrella sampling (US), the most popular enhanced sampling method,
suffers from insufficient sampling as the system is easily trapped
in low-energy configurations of the permeant for up to hundreds of
nanoseconds. Therefore, convergence in the calculation of free
energies of permeation and hence permeability coefficients is
usually poor except for very small molecules. We have developed a
flexible implementation of replica exchange with solute scaling
(REST2) (2) within GROMACS 4.6.7 that can be combined with US. Our
implementation allows the independent scaling of electrostatic and
van der Waals non-bonded interactions, such that they can be
independently modulated between any pairs of components in the
system. This US+REST implementation was used to obtain the
potential of mean force (PMF) for the permeation of the dipeptide
Ala-Tpr across a SC model. Our method yielded broader sampling of
orientations and conformations of the dipeptide, confirming its
ability to overcome local energy minima, especially in US windows
where the dipeptide is embedded in the bilayer. Similarly, the PMF
shows less pronounced global minima and maxima compared to that
obtained by conventional US. These findings suggest significant
enhancement of sampling which is validated against experimental
permeability coefficients and free energy of partition (3).
Abstracts: Short Talks
Indian Institute of Technology- Gandhinagar, Ahmedabad University
“New age antimicrobial peptides: Revealing mode of actions of multi
functional AMPs using molecular dynamics simulation study”
Nirali Desai, Dr. Stephen Fox, Dr. Chandra Verma Currently,
antimicrobial resistance developed by many infectious pathogens is
a severe emerging problem. Antimicrobial peptides can be used as
potential alternatives to conventional antibiotics because of their
multi functionality and non-specificity in targeting pathogens. To
understand different mechanisms of killing via bacterial membrane
by AMPs in detail and to see differences in the mode of action of
two peptides, magainin2 and pleurocidin with different modes of
action we performed Molecular Dynamics simulations. Experimentally,
magainin2 is known to form toroidal pores in the membrane whereas
pleurocidin is known to interact with the intracellular targets.
Molecular dynamic simulations were run for both peptides and for
each orientation for 100-1000 ns using Gromacs and the charmm36m
force field. Modelling a bacterial membrane (POPE:POPG in 3:1)
solvated in the TIP3P water model and 0.15M NaCl ions. Magainin2
was found to significantly disrupt the membrane by forming toroidal
pores, however pleurocidin also seemed to be form pores when forced
in the membrane.
Abstracts: Short Talks
La Trobe University "Molecular Evolution of the Switch for
Progesterone / Spironolactone from Mineralocorticoid Receptor
Agonist to Antagonist"
Ruitao Jin, Sitong He, Peter Fuller, Brian Smith The
mineralocorticoid receptor (MR) is highly conserved across
vertebrate evolution. In terrestrial vertebrates the MR mediates
sodium homeostasis by aldosterone and also acts as a receptor for
cortisol. Although the MR is present in fish, they lack
aldosterone. The MR binds progesterone and spironolactone as
antagonists in human MR but as agonists in zebra fish MR. We have
defined the molecular basis of these divergent responses using MR
chimeras between the zebra fish and human MR coupled with
reciprocal site-directed mutagenesis and molecular dynamic
simulation on the structures of the MR ligand-binding domain.
Substitution of a leucine by threonine in helix 8 of the
ligand-binding domain of the zebra fish MR confers the antagonist
response. This leucine is conserved across fish species whereas
threonine (serine in rodents) is conserved in terrestrial
vertebrate MR. MD identified an interaction of the leucine in helix
8 with a highly conserved leucine in helix 1 that stabilises the
agonist conformation including the interaction between helices 3
and 5, an interaction which has previously been characterised. This
switch in the MR coincides with the evolution of terrestrial
vertebrates and of aldosterone synthesis. It was perhaps mandatory
if the appearance of aldosterone as a specific mediator of the
homeostatic salt retention was to be tolerated. The conformational
changes also provide novel insights into the structural basis of
agonism versus antagonism in steroid receptors, with potential
implications for drug design in this important therapeutic
target.
Abstracts: Short Talks
University of Cambridge "Mechanism of completion of
peptidyltransferase centre assembly in eukaryotes"
Kargas V, Castro-Hartmann P, Escudero-Urquijo N, Dent K, Hilcenko
C, Sailer C, Zisser G, Marques-Carvalho MJ, Pellegrino S, Wawiórka
L, Freund SM, Wagstaff JL, Andreeva A, Faille A, Chen E, Stengel F,
Bergler H, Warren AJ.
Eukaryotic ribosome biogenesis initiates in the nucleus and
involves the concerted action of over 200 trans-acting assembly
factors to produce functional 40S and 60S ribosomal subunits. Upon
export to the cytoplasm, the remaining assembly factors on the
pre-60S ribosomal particles are released and the last ribosomal
proteins are integrated, sculpting the key functional site of the
ribosome called peptidyltransferase centre (PTC). Despite the
recent advances in structural biology, cytoplasmic ribosome
biogenesis still remains elusive. Here, we set out to determine the
mechanism of cytoplasmic 60S subunit maturation and completion of
PTC assembly by using tandem affinity purification, immunoblotting,
cryo electron microscopy (cryo-EM) and molecular modelling. Single
particle cryo-EM analysis resulted in a series of pre-60S
structures representing six distinct late cytoplasmic assembly
states that could be ordered into a sequential maturation pathway.
More specifically, recruitment of eL40 stabilises helix 89 to form
the uL16 binding site. The loading of uL16 unhooks helix 38 from
Nmd3 to adopt its mature conformation. In turn, partial retraction
of the L1 stalk is coupled to a conformational switch in Nmd3 that
allows the uL16 P-site loop to fully accommodate into the PTC where
it competes with Nmd3 for an overlapping binding site. Our data
reveal how the central functional site of the ribosome is shaped
and suggest how the formation of translation-competent 60S subunits
is disrupted in leukaemia- associated ribosomopathies.
Abstracts: Short Talks
ST-13. Malancha Karmakar
University of Melbourne "SUSPECT-PZA: An empirical tool to
determine novel drug resistance in Pyrazinamide"
Malancha Karmakar, Justin T. Denholm and David B. Ascher
Pyrazinamide, a first-line drug with sterilizing activity, plays an
important role in tuberculosis treatment; however, its use is
complicated by side-effects and challenges with reliable drug
susceptibility testing. Resistance to pyrazinamide is largely
driven by mutations in pyrazinamidase (pncA), responsible for drug
activation, but genetic heterogeneity has hindered development of a
molecular diagnostic test. Our objective was to use information
from the proteins 3D structure to accurately identify resistance
mutations in pncA. To achieve this, we curated 610 pncA
non-synonymous single nucleotide mutations with associated high
confidence experimental and clinical information on pyrazinamide
susceptibility. The molecular consequences of these mutations were
assessed using the mCSM platform, which provided insights into
changes in protein stability, conformation, and interactions for
each mutation. Using these structural and biophysical effects, we
could correctly classify mutations as either susceptible or
resistant with an accuracy of 80%. Our model was validated against
a previously documented set of non-redundant clinically resistance
mutations and the CRyPTIC dataset, achieving 79% and 81% accuracy
respectively. We further validated our model using a novel set of
previously unreported clinical mutations with experimental drug
susceptibility testing from over 600 Victorian patients, and
obtained 71% accuracy. Using the insights from this model, we also
performed a real-time analysis on a Victorian tuberculosis patient,
in which pyrazinamide treatment would not be effective and led to
its discontinuation. This was the first use of structural
information to guide clinical resistance detection. We have made
this model freely available through a user-friendly web interface
called SUSPECT-PZA. This will be a valuable resource to analyse any
pncA missense mutation, providing structural insight to help guide
patient treatment decisions and screening programs.
Abstracts: Short Talks
GSK and University of Strathclyde "Binding Pose Metadynamics:
Exploring Ligand stability in Protein Crystal Structures"
Lucia Fusani, David Palmer, Don Somers and Ian Wall The prediction
of the correct protein-ligand binding pose or poses is important in
structure- based drug design and crucial for the evaluation of
protein-ligand binding affinity. Most three- dimensional
protein/ligand structures are obtained from single crystal X-ray
crystallography experiments which result in a single static model
of an ensemble of conformations. The Binding Pose Metadynamics [1]
(BPMD) tool allows the study of ligand stability in full atomistic
detail in a computationally efficient manner averaging over 10 × 10
ns metadynamics runs with the root-mean square deviation of the
ligand heavy atoms as the collective variable. The basic principle
of BPMD is that ligand poses which are unstable (average RMSD >
2 Å) under the bias of the metadynamics simulation are likely to be
infrequently occupied in the energy landscape and make minimal
contributions to the protein-ligand binding affinity. The
robustness of the method is studied using crystal structures with
ligands known to be incorrectly modelled as well as a wider data
set of 63 crystal structures with ligand fit to electron density
from the Twilight [2] database. Results show that BPMD can
successfully discriminate between compounds whose binding pose is
not supported by the electron density from those with well-defined
electron density. We expect that this protocol will enable modelers
to choose high-quality ligand protein crystal structures for the
progression of structure-based drug design projects. 1. Clark,
A.J., et al., Prediction of Protein-Ligand Binding Poses via a
Combination of Induced Fit Docking and Metadynamics Simulations. J
Chem Theory Comput, 2016. 12(6): p. 2990-8. 2. Weichenberger, C.X.,
E. Pozharski, and B. Rupp, Visualizing ligand molecules in Twilight
electron density. Acta crystallographica. Section F, Structural
biology and crystallization communications, 2013. 69(Pt 2): p.
195-200.
Abstracts: Short Talks
ST-15. Carlo Martinotti
Curtin University "Development of new enhanced sampling approaches
for the prediction of the free energy of interaction of small
molecules and peptides with cell membranes"
Carlo Martinotti, Evelyne Deplazes and Ricardo L. Mancera
Understanding the interactions and binding affinities of small
drug-like molecules with biological membranes is important in
fields such as pharmacology, toxicology and rational drug design.
Development of molecular simulation protocols that can provide an
accurate prediction of the free energy of binding (ΔGb) of small
molecules to lipid membranes remains a challenging task in
computational biophysics. Approaches such as umbrella sampling
suffer from insufficient configurational sampling of the molecule,
resulting in the system becoming trapped in local energy minima for
very long times, preventing simulations from reaching convergence
and/