Softwares for Molecular Docking

Lokesh P. TripathiNCBS

17 December 2007

Molecular Docking

• Attempt to predict structures of an intermolecular complex between two or more molecules– Receptor-ligand (or drug)– Enzyme-substrate– Protein-DNA (or RNA)– Protein-protein

Brief History of Docking

• Crick (1953) suggested that complementarity in helical coils could be modelled as knobs fitting into holes

• DOCK (Kuntz, 1982) pioneered the field of molecular docking

• GRID (Goodford, 1985) too became a part of many subsequent softwares

General considerations

• Molecular representations– Abstract or atoms– Fixed or flexible

• Juxtaposition of molecules– Interactive or automated– Search algorithm to create conformations

• Evaluating complementarity (ranking)– Scoring function– Force field energy functions

Search Algorithms

• Potentially several ways of putting two molecules together; possibilities increase exponentially with size of molecules involved

• Attempt to locate the most stable state in the energy landscape

• Broadly two types: 1) full solution space search; 2) guided search through solution space

Search Algorithms•Random

– Genetic algorithms– Monte Carlo methods– Tabu search

•Systematic– Fragment-based methods– Point complementary methods– Distance geometry methods– Database

•Simulation– Molecular dynamics– Energy minimisation

•Multiple methods Algorithms

Docking Softwares

Virtual screening De novo designAutoDock LUDI

DOCK GRIDFlexX/E MCSSSLIDE SMoG

Surflex GrowMolICM SPROUT

GOLD

Random methods

• Sample the conformation space by making single change to a ligand or a population of ligands

• Alteration performed at each step and accepted or rejected based on a predetermined probability function

• Include Monte Carlo (MC) methods; Genetic Algorithm (GA) methods; Tabu search methods

Monte Carlo methods• Use a simple energy function

• Makes random moves and accepting or rejecting based on Boltzmann probability function

• More efficient in stepping over energy barriers, allowing more complete searches of conformation space

• PRODOCK, MC-DOCK, ICM, DockVision, QXP, GLIDE; too slow for extensive flexible docking

Energy global minimum conformers generated by Monte Carlo method

Genetic Algorithm methods• Apply ideas of genetics and evolution in

docking

• Start with an initial population of random ligand conformers wrt protein, each defined by a set of variables called genes

• Genetic operators (mutations, crossovers) applied to sample conformation space till optimal population is derived

• AUTODOCK, GOLD, DIVALI, DARWIN; too slow for extensive flexible docking

Autodock

• Suite of automated docking tools• Designed to predict how small molecules

(ligands drug candidates) bind to a receptor; AMBER force field

• Three constituent programs-Autotors- define torsions in the ligand-Autogrid- calculate grids-Autodock- docking tool-AutoDockTools (ADT)- GUI to facilitate above and other modules accompanying AutoDock

Autodock Lamarckian GA

• LGA encompasses a “genotypic” and “phenotypic” phase i.e. genetic operations and energy function to be optimised

• Energy minimisation performed after “genotypic” changes and these “phenotypic” changes mapped back onto “genes” (by changing ligand coordinates.

• Most efficient and reliable of random methods

Autodock Grid maps

• Pre-calculated • Grid for each atom type

(e.g. C, H, O, N)• Consists of 3D lattice of

regularly spaced points, surrounding and centered on region of interest in the macromolecule

• Typical spacing is 0.375 Å• Probe atom placed at each

grid point and energy calculated

GOLD

• Genetic Optimisation and Ligand Docking, uses multiple subpopulations of ligand

• Force-field based scoring function, includes three terms: H-bonding term, intermolecular dispersion potential, intramolecular potential

• 71% success in identifying experimental binding mode in 100 protein complexes

Tabu Search methods

• Impose restrictions preventing searches from repeating already explored conformations

• New conformation is compared to the previous ones based on RMSD values which determine acceptance

• PRO-LEADS

Systematic Search methods

• Attempt to explore all degrees of freedom in a molecule

• Can be divided into three types: conformational search methods, fragmentation methods, and database methods

Conformational Search methods

• Brute force or shotgun methods of docking

• All rotatable bonds in ligand rotated through 360°till in fixed increments till all possible combinations generated and evaluated

• Number of structures generated increases exponentially with number of rotatable bonds- combinatorial explosion

Fragmentation Search methods

• Incrementally grow ligand into the active site, by docking several fragments into the active site followed by covalent-linking to recreate the initial ligand

• Rigid core-fragment of the ligand is docked first followed by addition of flexible regions

• DOCK, FlexX, LUDI, ADAM, Hammerhead

DOCK

Methodology

FlexX• Base fragment is picked up and docked using

“pose-clustering” algorithm

• Clustering algorithm is implemented to merge similar ligand transformations into active site

• Flexible fragments are added incrementally using MIMUMBA and evaluated using overlap function, followed by energy calculations till the ligand is completely built

• Final evaluation through Böhm’s scoring function that includes H-bonds, ionic, aromatic and lipophilic terms

Database methods• Tackle combinatorial explosion by using

libraries of pregenerated conformations to deal with ligand flexibility

• FLOG generates and docks conformational libraries called Flexibases using distance geometry

• EUDOC uses conformational searches of ligands to generate different structures, which are placed into receptor active-site followed by energy evaluation

Scoring• Essential to rank the ligand conformations

determined by the search algorithms

• Scoring function must be able to distinguish between true binding modes and others

• Speed and accuracy are most desirable

• Three major classes: force-field based; empirical; knowledge-based

Force-field based Scoring• Quantify sum of two energies-interaction

energy between receptor-ligand; internal energy of the ligand

• Consist of van der Waals (Lennard-Jones potential) + electrostatic energy terms (Coulombic function)

• Do not include solvation and entropic terms

• GoldScore, G-SCORE, D-SCORE, AMBER, CHARRM, GROMOS

Empirical Scoring • Designed to reproduce experimental data;

binding energy can be approximated by sum of individual uncorrelated terms

• Experimentally determined binding energies used to quantify individual terms

• Easy computation, but non-versatile due to dependence on experimental datasets

• ChemScore, Böhm’s scoring function, F-Score, X-Score

Knowledge-based Scoring• Statistically derived principles that aim to

replicate experimentally determined structures

• Employ simple interactions to screen large databases

• Dependent on information available in preexisting datasets

• DrugScore, SMoG score, Potential of Mean force (PMF)

Consensus Scoring

• Combines information from different scoring schemes to compensate for individual limitations

• Correlation of individual scoring systems may be a problem

• X-SCORE combines functions from PMF, ChemScore, PMF with FlexX

Protein-protein Docking

• Prediction of protein complex structure given individual components’ structures

• Huge number of degrees of freedom; docking largely performed as rigid body docking

• Z-DOCK, a Fast Fourier Transform-based rigid body docking program, is one of the most accurate programs as rated in Critical Assessment of Predicted Interactions (CAPRI)

Docking- strengths and limitations

• Most available softwares are able to “predict” known protein-bound conformations with an accuracy of 1.5-2 Å; 70-80% success rate

• Scoring function- major limitation factor due to simplifications and assumptions

• Solvation effects, quality of crystallographic data

Comparing Docking softwares in difficult

• Several studies compare docking programs but conclusions of general applicability are not evident

• Minor differences in methodology can have significant impact on success rates of various docking programs

• Cole et al., 2005 PROTEINS 60, 325-332 provide a list of recommendations in assessing docking programs

Docking softwares’

representations in

citations

Docking Softwares- Citations per year

Challenges

• Predicting structures of multi-domain, multi- subunit protein complexes

• Prediction and specificity in protein-nucleic acid interactions

• Protein-docking with backbone flexibility