MM-ISMSA – Scoring Function for Protein-Protein and Protein-Ligand Docking and Molecular Dynamics

MM-ISMSA

:: DESCRIPTION

MM-ISMSA is an ultrafast and accurate scoring function for protein-protein and protein-ligand docking

::DEVELOPER

Unidad de Bioinformatica CBMSO

:: SCREENSHOTS

:: REQUIREMENTS

Windows / Linux
Python
PyMOL

:: DOWNLOAD

MM-ISMSA

:: MORE INFORMATION

Citation

Javier Klett, Alfonso Núñez-Salgado, Helena G. Dos Santos, Álvaro Cortés-Cabrera, Almudena Perona, Rubén Gil-Redondo, David Abia, Federico Gago, and Antonio Morreale
MM-ISMSA: an ultra-fast and accurate scoring function for protein-protein docking.
J Chem Theory Comput. 2012 Sep 11;8(9):3395-3408

PLIC – Protein-ligand Interaction Clusters

PLIC

:: DESCRIPTION

PLIC is a database of protein-ligand interaction clusters.

::DEVELOPER

Chandra lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Anand P, Nagarajan D, Mukherjee S, Chandra N.
PLIC: protein-ligand interaction clusters.
Database (Oxford). 2014 Apr 23;2014(0):bau029. doi: 10.1093/database/bau029. PMID: 24763918; PMCID: PMC3998096.

GLoSA 2.2 – Protein-ligand and Protein-protein Interactions

GLoSA 2.2

:: DESCRIPTION

G-LoSA (Graph-based Local Structure Alignment) is an efficient computational method to align protein local structures in a sequence order independent way and to provide the GA-score (between 0 and 1), a size-independent quantity of structural similarity for a given local structure pair.

::DEVELOPER

Wonpil Im Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
C++ Compiler

:: DOWNLOAD

G-LoSA

:: MORE INFORMATION

Citation

Lee HS, Im W.
G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures.
Methods Mol Biol. 2017;1611:97-108. doi: 10.1007/978-1-4939-7015-5_8. PMID: 28451974.

PLATINUM – Protein–Ligand ATtractions Investigation NUMerically

PLATINUM

:: DESCRIPTION

PLATINUM is designed for calculation of hydrophobic properties of molecules and their match or mismatch in receptor–ligand complexes

::DEVELOPER

Laboratory of biomolecular modeling.

:: SCREENSHOTS

N/A

::REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Bioinformatics. 2009 May 1;25(9):1201-2. doi: 10.1093/bioinformatics/btp111. Epub 2009 Feb 25.
PLATINUM: a web tool for analysis of hydrophobic/hydrophilic organization of biomolecular complexes.
Pyrkov TV1, Chugunov AO, Krylov NA, Nolde DE, Efremov RG.

PTGLtools – Visualization of Protein-Ligand Graphs software package

PTGLtools

:: DESCRIPTION

PTGLtools (PTGLgraphComputation , formerly labeled VPLG) uses a graph-based model to describe the structure of proteins on the super-secondary structure level. A protein-ligand graph is computed from the atomic coordinates in a PDB file and the secondary structure assignments of the DSSP algorithm. In this graph, vertices represent secondary structure elements (SSEs, e.g. usually alpha helices and beta sheets) or ligand molecules while the edges model contacts and spatial relations between them.

::DEVELOPER

Molecular Bioinformatics MolBI

:: SCREENSHOTS

::REQUIREMENTS

Windows/Linux/MacOsX
Java

:: DOWNLOAD

PTGLtools

:: MORE INFORMATION

Citation

The new Protein Topology Graph Library web server.
Schäfer T, Scheck A, Bruneß D, May P, Koch I.
Bioinformatics. 2015 Oct 6. pii: btv574.

TargetS – Predictor for Targeting Protein-ligand Binding Sites

TargetS

:: DESCRIPTION

TargetS is a new ligand-specific template-free predictor for targeting protein-ligand binding sites from primary sequences.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation:

IEEE/ACM Trans Comput Biol Bioinform. 2013 Jul-Aug;10(4):994-1008. doi: 10.1109/TCBB.2013.104.
Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering.
Dong-Jun Yu, Jun Hu, Jing Yang, Hong-Bin Shen, Jinhui Tang, and Jing-Yu Yang,

OSML – Predicting Protein-Ligand Binding Sites

OSML

:: DESCRIPTION

OSML is a query-driven dynamic machine learning model for predicting protein-ligand binding sites

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation:

Dong-Jun Yu, Jun Hu, Hong-Bin Shen et al.,
Constructing Query-Driven Dynamic Machine Learning Model with Application to Protein-Ligand Binding Sites Prediction,
IEEE Trans Nanobioscience. 2015 Jan;14(1):45-58. doi: 10.1109/TNB.2015.2394328.

GalaxyDock 2 – Protein-ligand Docking program

GalaxyDock 2

:: DESCRIPTION

GalaxyDock is a protein-ligand docking program that allows flexibility of pre-selected side-chains of ligand using Conformational Space Annealing.

::DEVELOPER

Lab of Computational Biology and Biomolecular Engineering, Seoul National University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

GalaxyDock

:: MORE INFORMATION

Citation

J Comput Chem. 2013 Nov 15;34(30):2647-56. doi: 10.1002/jcc.23438. Epub 2013 Sep 24.
GalaxyDock2: protein-ligand docking using beta-complex and global optimization.
Shin WH1, Kim JK, Kim DS, Seok C.

J Chem Inf Model. 2012 Dec 21;52(12):3225-32. doi: 10.1021/ci300342z. Epub 2012 Dec 12.
GalaxyDock: protein-ligand docking with flexible protein side-chains.
Shin WH, Seok C.

MGA-Glide 1.0 – Grid-based Protein-ligand Docking software

MGA-Glide 1.0

:: DESCRIPTION

MGA-Glide is a novel deep conformational search method for grid-based protein-ligand docking software.

::DEVELOPER

Akiyama Laboratory , Tokyo Institute of Technology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

MGA-Glide

:: MORE INFORMATION

Citation

Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem.
Ban T, Ohue M, Akiyama Y.
Comput Biol Chem. 2018 Apr;73:139-146. doi: 10.1016/j.compbiolchem.2018.02.008.

P2Rank 2.0.1 – Protein-Ligand Binding Site prediction

P2Rank 2.0.1

:: DESCRIPTION

P2Rank is a machine learning based method for prediction of ligand binding sites from protein structure. P2Rank uses Random Forests classifier to infer ligandability of local chemical neighborhoods near the protein surface which are represented by specific near-surface points and described by aggregating physico-chemical features projected on those points from neighboring protein atoms. The points with high predicted ligandability are clustered and ranked to obtain the resulting list of binding site predictions.

::DEVELOPER

SIRET Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux
JRE 8 (Java 1.8) or JRE 11 (Java 11)
PyMOL

:: DOWNLOAD

P2Rank

:: MORE INFORMATION

Citation:

P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure.
Krivák R, Hoksza D.
J Cheminform. 2018 Aug 14;10(1):39. doi: 10.1186/s13321-018-0285-8.