Toolbox > Protein Structure > Prediction> Binding Sites

Binding Site Prediction and Docking

The interaction between proteins and other molecules is fundamental to all biological functions. In this section we include tools that can assist in prediction of interaction sites on protein surface and tools for predicting the structure of the intermolecular complex formed between two or more molecules (docking).

 

Pockets Identification
CASTp
Automatic Identification of pockets and cavities in proteins structure, and quantitation of their volumes using Delaunay triangulation. Available also as PyMOL plugin
PASS
Automatic identification of pockets on protein surface
Pocket-Finder
Automatic identification of pockets and cavities in proteins structure, and quantitation of their volumes.
PocketPicker
Grid-based technique for the analysis of protein pockets. PocketPicker available as a plugin for PyMOL
   
Binding Site Prediction
ConSurf Identification of functional regions in proteins by surface-mapping of phylogenetic information
CRESCENDO Identification protein interaction sites. It uses sequence conservation patterns in homologous proteins to distinguish between residues that are conserved due to structural restraints from those due to functional restraints.
   
Ligand Binding Sites
3DLigandSite The server utilizes protein-structure prediction to provide structural models of the binding site. Ligands bound to structures are superimposed onto the model and use to predict the binding site.
FINDSITE A threading-based method for ligand-binding site prediction and functional annotation based on binding-site similarity across superimposed groups of threading templates.
LIGSITEcsc
Prediction of binding site by pocket identification using the Connolly surface and degree of conservation
metaPocket A meta server for ligand-binding site prediction. metaPocket use LIGSITEcsc, PASS, Q-SiteFinder and SURFNET
Q-SiteFinder Prediction of binding site by pocket identification and use of hydrophobic (CH3) probes to rank most favorable binding sites
   
Protein-Protein Interaction Sites
cons-PPISP A consensus neural network method for predicting protein-protein interaction sites
HOMCOS A server to predict interacting protein pairs and interacting sites by homology modeling of complex structures
HotPOINT Prediction of protein interfaces using an empirical model
ISIS Prediction of interaction hotspots from sequence
KFC server Automated decision-tree approach to predicting protein-protein interaction hot spots
meta-PPISP A meta server for predicting protein-protein interaction sites. meta-PPISP is built on three individual web servers: cons-PPISP, PINUP, and Promate
ODA Identification of optimal surface patches with the lowest docking desolvation energy values
PINUP Protein binding site prediction with an empirical scoring function
PPI-Pred A combined support vector machine (SVM) approach with surface patch analysis to predict protein–protein binding sites
ProMate/ProMateus Predicts the location of potential protein-protein binding sites and lets the user add additional features
Protinfo PPC A server to predict interacting protein pairs and interacting sites by homology modeling of complex structures
SHARP2 Predicts the location of potential protein-protein binding sites based on structural information
Whiscy A combined surface conservation and structural information to predict protein-protein interfaces
   
Other Sites (DNA, RNA, Metals)
CHED Web server for predicting soft metal binding sites in proteins
DBD-Hunter A knowledge-based method for the prediction of DNA-protein interactions
DISPLAR Given the structure of a protein known to bind DNA, the method predicts residues that contact DNA using neural network method
iDBPs Predicts DNA binding proteins for proteins with known 3D structure.
PFplus
A tool for extracting and displaying positive electrostatic patches on protein surfaces which can be indicative of nucleic acid binding interfaces.
PPI-Pred A combined SVM approach with surface patch analysis to predict protein–DNA binding sites.
PreDs Prediction of dsDNA-binding site on protein surfaces, based on the shape of the molecular surface and the electrostatic potential on the surface.
PRINTR Prediction of RNA binding sites in proteins using SVM and profiles
   
Ligand Docking

Ligand docking referred to cases where small molecule (“ligand”) is being docked into much larger macromolecule ("target"). The following is partial list of docking software, focusing on free (at least for academic institutes) and/or popular docking tools. Several studies have shown that the performance of most docking tools is highly dependent on the particular characteristics of both the binding site and the ligand to be investigated, and the determination which method would be more suitable in a specific context is difficult. We encouraged you to check several docking methods to determine which one(s) work best for your system.

  Search Method Flexibility Scoring Function Cost  
AutoDock Stochastic (GA) Flexible ligand and partially flexible target AUTODOCK(empirical) Free
ArgusLab

Systematic

Flexible ligand X-Score based (empirical) Free
DOCK Systematic (IC) Flexible ligand DOCK 3.5 (force field) Free for academics
eHITS Systematic (RBD of fragments followed by reconstruction) Flexible ligand and partially flexible target HiTS_Score (empirical) Free for academics
FlexX Systematic (IC) Flexible ligand FlexX SF (empirical) Commercial  
FLIPDock Stochastic (GA) Flexible ligand and flexible target AUTODOCK (empirical) Free for academics
FRED Systematic (RBD) Flexible ligand ChemScore, PLP, ScreenScore, ChemGauss (empirical/consensus) Free for non-commercial projects
Glide

Stochastic (hierarchical filters and MC)

Flexible ligand GlideScore (empirical) and force field energy Commercial  
GOLD Stochastic (GA) Flexible ligand and partially flexible target GoldScore, ChemScore (empirical), ASP (knowledge based) Commercial  
ICM Stochastic (MC) Flexible ligand and partially flexible target ICM SF (empirical) Commercial  
ParDOCK Stochastic (MC) Rigid BAPPL (empirical) Web Server  
PLANTS Stochastic (ACO) Flexible ligand and partially flexible target

CHEMPLP, PLP (empirical)

Free for academics
Surflex Systematic (IC/MA) Flexible ligand Hammerhead based (empirical) Commercial
           
Binding Affinity Prediction and Scoring Functions
BAPPL computing binding free energy of a non-metallo protein-ligand complex using an all atom energy based empirical scoring function
BAPPL-Z Binding affinity prediction of protein-ligand complex containing Zinc
DrugScore Knowledge-based scoring functions. DrugScore enables you to score protein-ligand complexes of your interest and to visualize the per-atom score contributions.
FoldX Protein-protein binding energy calculations
gCOMBINE A java-written graphical user interface for performing comparative binding energy analysis
GFscore A general non-linear consensus scoring function for high-throughput docking
PEARLS Computing small molecule ligand-protein, ligand-nucleic acid, protein-nucleic acid and ligand-protein-nucleic acid interaction energies
PreDDICTA Calculates the Drug-DNA interaction energy
   
Compound Databases for Virtual Screening
ZINC Free database of commercially-available compounds for virtual screening
   
Ligand Entrance and Exit Channels
SLITHER A web server for generating contiguous conformations of substrate molecules entering into deep active sites of proteins or migrating across membrane transporters
   
Protein-Protein Docking
Predicting the structure of protein–protein complexes using docking approaches is a difficult problem whose major challenges include identifying correct solutions, and properly dealing with molecular flexibility and conformational changes. The proper treatment of flexibility in protein–protein docking is still an active field of research. You first should analyzed your proteins in order to define their conformational space (check our flexibility analsis tools list) and then choose the most suitable method for your docking problem.
  Search Algorithm Scoring Parameters Re-scoring, Ranking, Filtering and Refinement Cost
3D-Dock Suite Global rigid search: FFT Shape complementarity and electrostatics Re-scoring and clustering. Refinement of interface side-chains Free
3D-Garden Global rigid search in ensamble

Shape complementarity and Lennard–Jones potential

Side chain and backbone dihedral refinement

Web Server

Bigger Global rigid search. Soft surface representation by bit-mapping method Shape complementarity and favorable residue's contacts Re-scoring with multiple filters (electrostatic, hydrophobic, side chain contacts)

Free for academics

Integrated in Chemera
ClusPro Global rigid search: FFT correlation approach using the programs DOT or ZDOCK   Re-scoring (desolvation and electrostatic energies) and clustering Web Server
DOT Global rigid search: FFT Shape complementarity, electrostatics and VDW None Free for academics
Escher NG Global rigid search Shape complementarity, hydrogen bonds and electrostatic None

Free

Integrated in VEGA
GRAMM Global rigid search: FFT. smooth protein surface representation for soft docking Shape complementarity and Lennard-Jones potential Clustering of conformations Free
GRAMM-X Global rigid search: FFT. smooth protein surface representation for soft docking Shape complementarity and Lennard-Jones potential minimization and re-scoring with multiple filters Web Server
HEX Global rigid search: Fourier correlation of spherical harmonics Shape complementarity None Web Server
source code is available for download
HADDOCK Global rigid search Electrostatic ,VDW and desolvation energy terms MD simulated annealing refinement . Filtering based on external data. clustering and re-ranking Web server
source code is available for download
ICM Global rigid search: Monte Carlo Empirical scoring function Clustering and selection of conformations. Refinement of interface side-chains and re-scoring Commercial
MolFit Global rigid search: FFT Shape complementarity Clustering of good solutions, filtering using a priori information and small, local rigid rotations around selected conformations Free
PatchDock Global rigid search Shape complementarity and atomic desolvation energy Clustering of conformations

Web Serve

source code is available for download
PyDock Global rigid search: FFT Shape complementarity rescoring by binding electrostatics and desolvation energy Web Server
RosettaDock

Local rigid search: Monte Carlo with low and high resolution structure representation levels

Different scoring parameters for the different resolutions

 

Web Server

source code is available by license from RosettaCommons.
 
ZDOCK Global rigid search: FFT Shape complementarity, desolvation energy, and electrostatics. Energy minimization and re-scoring Free for academics
   
Post Docking Re-scoring, Ranking, Filtering and Refinement
FireDock Refinement and re-scoring of rigid-body protein-protein docking solutions
FunHunt Classifier of correct protein-protein complex orientations
RosettaDock Local docking. Useful for refining top-ranked models from global searches by other docking methods.