Softwares for Molecular Docking

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Softwares for Molecular Docking

Lokesh P. Tripathi

NCBS17 December 2007


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Molecular Docking

Attempt to predict structures of an

intermolecular complex betweentwo or more molecules

Receptor-ligand (or drug) Enzyme-substrate

Protein-DNA (or RNA) Protein-protein


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Brief History of Docking

Crick (1953) suggested that complementarity in

helical coils could be modelled as knobs fittinginto holes

DOCK (Kuntz, 1982) pioneered the field of

molecular docking

GRID (Goodford, 1985) too became a part of

many subsequent softwares


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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


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Search Algorithms

Potentially several ways of putting two

molecules together; possibilities increaseexponentially with size of molecules involved

Attempt to locate the most stable state in theenergy landscape

Broadly two types: 1) full solution spacesearch; 2) guided search through solutionspace


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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


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Docking Softwares

Vi r t ual scr eeni ng De novo desi gn

AutoDock LUDIDOCK GRID

FlexX/E MCSSSLIDE SMoG

Surflex GrowMol

ICM SPROUT

GOLD


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Random methods

Sample the conformation space by making

single change to a ligand or a population ofligands

Alteration performed at each step andaccepted or rejected based on apredetermined probability function

Include Monte Carlo (MC) methods; Genetic

Algorithm (GA) methods; Tabu search methods


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Monte Carlo methods Use a simple energy function

Makes random moves and accepting or rejecting basedon Boltzmann probability function

More efficient in stepping over energy barriers,allowing more complete searches of conformationspace

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


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Energy global minimum conformers generated by Monte

Carlo method


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Genetic Algorithm methods Apply ideas of genetics and evolution in

docking

Start with an initial population of randomligand conformers wrt protein, each defined by

a set ofvariables called genes

Genetic operators (mutations, crossovers)

applied to sample conformation space tilloptimal population is derived

AUTODOCK, GOLD, DIVALI, DARWIN; too slowfor extensive flexible docking


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Autodock

Suite of automated docking tools

Designed to predict how small molecules(ligands drug candidates) bind to areceptor; AMBER force field

Three constituent programs-Autotors- define torsions in the ligand-Autogrid- calculate grids

-Autodock- docking tool-AutoDockTools (ADT)- GUI to facilitate aboveand other modules accompanying AutoDock


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Autodock Lamarckian GA

LGA encompasses a genotypic andphenotypic phase i.e. genetic operationsand energy function to be optimised

Energy minimisation performed aftergenotypic changes and these phenotypicchanges mapped back onto genes (by

changing ligand coordinates.

Most efficient and reliable of random methods


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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 Probe atom placed at each

grid point and energy

calculated


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GOLD

Genetic Optimisation and Ligand Docking, uses

multiple subpopulations of ligand

Force-field based scoring function, includesthree terms: H-bonding term, intermoleculardispersion potential, intramolecular potential

71% success in identifying experimentalbinding mode in 100 protein complexes


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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 determineacceptance

PRO-LEADS


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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 databasemethods


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Conformational Search methods

Brute force or shotgun methods of docking

All rotatable bonds in ligand rotated through360till in fixed increments till all possible

combinations generated and evaluated

Number of structures generated increasesexponentially with number of rotatable bonds-combinatorial explosion


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Fragmentation Search methods

Incrementally grow ligand into the active site,

by docking several fragments into the activesite followed by covalent-linking to recreatethe initial ligand

Rigid core-fragment of the ligand is dockedfirst followed by addition of flexible regions

DOCK, FlexX, LUDI, ADAM, Hammerhead


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DOCK

Methodology


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FlexX Base fragment is picked up and docked using

pose-clustering algorithm

Clustering algorithm is implemented to mergesimilar ligand transformations into active site

Flexible fragments are added incrementallyusing MIMUMBA and evaluated using overlapfunction, followed by energy calculations till

the ligand is completely built

Final evaluation through Bhms scoring

function that includes H-bonds, ionic, aromaticand lipophilic terms


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Database methods

Tackle combinatorial explosion by usinglibraries ofpregenerated conformations to deal

with ligand flexibility

FLOG generates and docks conformational

libraries called Flexibases using distancegeometry

EUDOC uses conformational searches of ligandsto generate different structures, which areplaced into receptor active-site followed byenergy evaluation


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Scoring

Essential to rank the ligand conformationsdetermined 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


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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


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Empirical Scoring Designed to reproduce experimental data;

binding energy can be approximated by sum ofindividual uncorrelated terms

Experimentally determined binding energiesused to quantify individual terms

Easy computation, but non-versatile due todependence on experimental datasets

ChemScore, Bhms scoring function, F-Score,X-Score


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Knowledge-based Scoring

Statistically derived principles that aim toreplicate experimentally determined structures

Employ simple interactions to screen large

databases

Dependent on information available in

preexisting datasets

DrugScore, SMoG score, Potential of Meanforce (PMF)


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Consensus Scoring

Combines information from different scoring

schemes to compensate for individuallimitations

Correlation of individual scoring systems maybe a problem

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


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Protein-protein Docking

Prediction of protein complex structure givenindividual 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 mostaccurate programs as rated in CriticalAssessment of Predicted Interactions (CAPRI)


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Docking- strengths and limitations

Most available softwares are able to predict

known protein-bound conformations with anaccuracy of1.5-2 ; 70-80% success rate

Scoring function- major limitation factor dueto simplifications and assumptions

Solvation effects, quality of crystallographicdata


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Comparing Docking softwares in difficult

Several studies compare docking programs but

conclusions of general applicability are not

evident

Minor differences in methodology can havesignificant impact on success rates of various

docking programs

Cole et al., 2005 PROTEINS 60, 325-332 provide

a list of recommendations in assessing dockingprograms

Docki ng sof t ar es


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Docki ng sof t war es

r epr esent at i ons i n

c i ta t ions

D ki S ft Cit ti


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Docking Softwares- Citations per year


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Challenges

Predicting structures of multi-domain, multi-

subunit protein complexes

Prediction and specificity in protein-nucleic

acid interactions

Protein-docking with backbone flexibility

Softwares for Molecular Docking

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