Structural biology and drug design as simple as this … de Ruyck Jérôme 30/11/2015 Lille - France

Structural biology and drug design

as simple as this …

de Ruyck Jérôme

30/11/2015

Lille - France

Introduction

https://en.wikibooks.org/wiki/Structural_Biochemistry

Introduction

Pre-clinical studies

2-4 years 2 months - 1 year

Chemistry Pharma-cology

Scaling-up

Clinical trials

3-5 years 2-3 years

Phase 1 Phase 2 Phase 3 NDA Phase 4

Introduction

Pre-clinical studies

2-4 years 2 months - 1 year

Chemistry Pharma-cology

Scaling-up

Clinical trials

3-5 years 2-3 years

Phase 1 Phase 2 Phase 3 NDA Phase 4

Scie

ntific

cha

lleng

eM

oney

• Challenging

• Time consuming

• Expensive

Multidisciplinary

approaches

• Efficiency increased

• Time saved

• Cost effectiveness

150 M $

Introduction

In-silico drug-design

BioinformaticsVirtual screening

Molecular modelling

In-vitro drug-design

Medicinal chemistryHigh-throughput screening

Structural biology

Introduction

In-vitro drug design


Structural biology

In-silico drug design


Molecular modelling

Drugs

Structural biology / Molecular modelling

Introduction

In-vitro drug design


Structural biology



Molecular modelling

Target

Molecular biology / Bioinformatics

Hits

HTS

Leads

Medicinal chemistry

Pharmacology / PK-PD predictions

Introduction


Ligand-based drug design

Structure-based drug design

Drugs

Target Hits

Leads

Outline

Virtual screening

Pharmacophore

Lead design

Pharmacophore

Evaluation

Ligand

Fragment


Docking

In situ design

Protein


Optimization

Nomenclature

Different kind of inter- and intra-molecular interactions

Polar interactions

Dipoles-DipolesVdW – H-Bond

ElectrostaticSalt bridges

Hydrophobic interactions

Aliphatic

Aromatic

Nomenclature

Aromatic interactionsQuadropular interactions

π – π interactions

π – cation interactions

Nomenclature

Deriving information on molecular systems

without really synthesizing them !

Quantum Mechanics (QM)Computational Chemistry

Theoretical Chemistry

Molecular Mechanics (MM)Molecular ModellingMolecular Dynamics

Quantum mechanics

• Nuclei and electrons separated

• Time consuming

• Applied to small molecules

• Not suitable for proteins

Method Accuracy Max atoms

Semiempirical Low 2000

HF & DFT Medium 500

Perturbation methods High 50

Coupled-cluster Very High 20

Molecular mechanics

• Spheres and springs model

• Very quick

• Can be applied to small molecules

• Suitable for proteins

• Accuracy depends on a force field

=

Streching Bending Torsion Non-bonded

Force field

• Different force fields for different systems

• Proteins• Sugars• Metals • …

• Different parameterization

• Empirical• Semi-empirical (including QM)

=

Streching Bending Torsion Non-bonded

Direct vs Indirect approaches

Virtual screening

Pharmacophore

Lead design

Pharmacophore

LigandIC50 / Ki

2D Structures

Fragment2D Structures

Ligand-based drug design(Indirect approach)

Docking

In situ design

Protein3D structures

Structure-based drug design(Direct approach)

Direct vs Indirect approachesDirect approach

Know receptorDon’t know ligand

Indirect approachDon’t know receptor

Know Ligands

Structural BiologyProtein - ligand interactions

Docking

Direct vs Indirect approachesDirect approach

Know receptorDon’t know ligand

Indirect approachDon’t know receptor

Know Ligands

Statistical methodsPharmacophore

3D-QSAR

?


Virtual screening

Pharmacophore

Lead design

Pharmacophore

LigandIC50 / Ki

2D Structures


Optimization Selection Evaluation


Pharmacophore

A pharmacophore is a geometrical description of molecular features which are necessary for molecular recognition of a ligand by a

biological macromolecule.

Typical pharmacophore features include hydrophobic centroids, aromatic rings, hydrogen bond acceptors or donors, cations, and anions.

Inhibition data

Structural superimposition

Pharmacophore

generation

Example (1)

“A four-point pharmacophore of COX-2 selective inhibitors was derived from a training set of 16 compounds, using the Catalyst program. It consists of a H bond acceptor, two hydrophobic groups and an aromatic ring, in accordance with SAR data of the compounds and with topology of the COX-2 active site. This hypothesis, combined with exclusion volume spheres representing important residues of the COX-2 binding site, was used to virtually screen the Maybridge database. Eight compounds were selected for an in vitro enzymatic assay. Five of them show COX-2 inhibition close to that of nimesulide and rofecoxib, two reference COX-2 selective inhibitors. As a result, structure-based pharmacophore generation was able to identify original lead compounds, inhibiting the COX-2 isoform.”

Example (2)

“Apolar trisubstituted derivatives of harmine show high antiproliferative activity on diverse cancer cell lines. However, these molecules present a poor solubility making these compounds poorly bioavailable. Here, new compounds were synthesized in order to improve solubility while retaining antiproliferative activity. First, polar substituents have shown a higher solubility but a loss of antiproliferative activity.Second, a Comparative Molecular Field Analysis (CoMFA) model was developed, guiding the design and synthesis of eight new compounds. Characterization has underlined the in vitro antiproliferative character of these compounds on five cancerous cell lines, combining with a high solubility at physiological pH, making these molecules druggable. Moreover, targeting glioma treatment, human intestinal absorption and blood brain penetration have been calculated, showing high absorption and penetration properties.”

Structural biology improvement

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

1138

9

1377

6

1630

6

1989

8 2429

5 2873

3 3425

6 4041

0 4661

5 5328

6 6047

3 6784

3

7602

0

8476

3

9364

5

1016

67

2139

2553

2994

3525

4254

5130

5991

6956

7609

8176

8697

9220

9757

1026

3

1081

7

1121

1

13 22 46 75 87 110

136

155

196

236

290

343

408

526

717

908

PDB Stati sti csX-Ray NMR CryoEM


Virtual screening

Lead design

LigandIC50 / Ki

2D Structures


Docking

In situ design





Virtual screening

Lead design

LigandIC50 / Ki

2D Structures


Docking

In situ design




Molecular docking

Protein-ligand docking is a computational method that mimics the binding of a ligand to a protein to form a complex.

It predicts the pose of the molecule in the binding site and calculates a score representing the strength of the binding.

Protein

Binding site

Docking

Ligand

Complex

How does it work ?

Protein-ligand docking software works in two different steps

Scoring function

Calculates a score or binding affinity for a

particular pose

Forcefield-basedEmpirical

Knowledge-based potentials

Search algorithm

Generates a large number of poses of a molecule in

the active site

GeneticLamarckian

Simulated annealing

Performs automated docking with full acyclic ligand flexibility, partial cyclic ligand flexibility and partial protein flexibility in and around

active site.

Scoring: includes H-bonding term, pairwise dispersion potential (hydrophobic interactions),

molecular and mechanics term for internal energy.

Example (1)

“Crystal structures of Thermus thermophilus and Bacillus subtilis type 2 IPP isomerases were combined to generate an almost completemodel of the FMN-bound structure of the enzyme. In contrast to previous studies, positions of flexible loops were obtained and carefullyanalyzed by molecular dynamics. Docking simulations find a unique putative binding site for the IPP substrate.”

Example (2)

“A total of 1,990 compounds from the National Cancer Institute (NCI) diversity set with nonredundant structures have been tested to inhibit cancer cell lines with unknown mechanism. Cancer inhibition through EGFR-TK is one of the mechanisms of these compounds. In this work, we performed receptor-based virtual screening against the NCI diversity database. Using two different docking algorithms, AutoDock and Gold, combined with subsequent post-docking analyses, we found eight candidate compounds with high scoring functions that all bind to the ATP-competitive site of the kinase. None of these compounds belongs to the main group of the currently known EGFR-TK inhibitors. Binding mode analyses revealed that the way these compounds complexed with EGFR-TK differs from quinazoline inhibitor binding and the interaction mainly involves hydrophobic interactions. Our results suggest that these molecules could be developed as novel lead compounds in anti-cancer drug design.”


Virtual screening

Lead design

LigandIC50 / Ki

2D Structures


Docking

In situ design




Fragment-based drug design

Fragment-based drug design is the screening of libraries of fragments with low chemical complexity.

The fragments usually bind the protein target with low affinity (high mM). The fragments selected for follow-up are then optimized by addition of chemical moieties or linked together with the aim of obtaining a highly

potent drug or inhibitor.

Examples (1)

“The search for new drugs is plagued by high attrition rates at all stages in research and development. Chemists have an opportunity to tackle this problem because attrition can be traced back, in part, to the quality of the chemical leads. Fragment-based drug discovery (FBDD) is a new approach, increasingly used in the pharmaceutical industry, for reducing attrition and providing leads for previously intractable biological targets. FBDD identifies low-molecular-weight ligands (~150 Da) that bind to biologically important macromolecules. The three-dimensional experimental binding mode of these fragments is determined using X-ray crystallography or NMR spectroscopy, and is used to facilitate their optimization into potent molecules with drug like properties. Compared with high-throughput-screening, the fragment approach requires fewer compounds to be screened, and, despite the lower initial potency of the screening hits, offers more efficient and fruitful optimization campaigns.”

“X-ray crystallography is an established technique for ligand screening in fragment-based drug-design projects, but the required manual handling steps – soaking crystals with ligand and the subsequent harvesting – are tedious and limit the throughput of the process. Here, an alternative approach is reported: crystallization plates are pre-coated with potential binders prior to protein crystallization and X-ray diffraction is performed directly ‘in situ’ (or in-plate). Its performance is demonstrated on distinct and relevant therapeutic targets currently being studied for ligand screening by X-ray crystallography using either a bending-magnet beamline or a rotating-anode generator. The possibility of using DMSO stock solutions of the ligands to be coated opens up a route to screening most chemical libraries.”

Examples (2)

The future is now …

Deriving information on molecular systems

without really synthesizing them !

Quantum Mechanics (QM)Computational Chemistry

Theoretical Chemistry

Molecular Mechanics (MM)Molecular ModelingMolecular Dynamics

Hybrid QM/MMSimulations

Computational Biology

The ONIOM method

𝐸 (𝑂𝑁𝐼𝑂𝑀 ,𝑅𝑒𝑎𝑙 )=𝐸 (𝑙𝑜𝑤 ,𝑟𝑒𝑎𝑙 )−𝐸 (𝑙𝑜𝑤 ,𝑚𝑜𝑑𝑒𝑙)+𝐸(h h𝑖𝑔 ,𝑚𝑜𝑑𝑒𝑙)

S. Dapprich, et al. 1999 THEOCHEM. 461-462: 1

Inside the mechanism

“Here, we report an integrated quantum mechanics/molecular mechanics (QM/MM) study of the bioorganometallic reaction pathway of the reduction of (E)-4-hydroxy-3-methylbut-2-enyl pyrophosphate (HMBPP) into the so called universal terpenoid precursors isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), promoted by the IspH enzyme. Dehydroxylation of HMBPP is triggered by a proton transfer from Glu126 to the OH group of HMBPP. The reaction pathway is completed by competitive proton transfer from the terminal phosphate group to the C2 or C4 atom of HMBPP.”

Mechanism-based drug design

“Development of novel influenza neuraminidase inhibitors is critical for preparedness against influenza outbreaks. Knowledge of the neuraminidase enzymatic mechanism and transition state analogue, 2-deoxy-2,3-didehydro-N-acetylneuraminic acid, contributed to the development of the first generation anti-neuraminidase drugs, zanamivir and oseltamivir. However, lack of evidence regarding influenza neuraminidase key catalytic residues has limited strategies for novel neuraminidase inhibitor design. Here, we confirm that influenza neuraminidase conserved Tyr406 is the key catalytic residue that may function as a nucleophile; thus, mechanism-based covalent inhibition of influenza neuraminidase was conceived. Crystallographic studies reveal that 2a,3ax-difluoro-N-acetylneuraminic acid forms a covalent bond with influenza neuraminidase Tyr406 and the compound was found to possess potent anti-influenza activity against both influenza A and B viruses. Our results address many unanswered questions about the influenza neuraminidase catalytic mechanism and demonstrate that covalent inhibition of influenza neuraminidase is a promising and novel strategy for the development of next-generation influenza drugs.”

Acknowledgment

• Computational Molecular Systems Biology team

Dr. M. LensinkDr. R. BlosseyDr. J. BouckaertDr. T. DumychDr. E.-M. KrammerIr. G. Brysbaert

• Fundings

Documents

Structural biology and drug design as simple as this … de Ruyck Jérôme 30/11/2015 Lille - France