Literature DB >> 31628659

Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.

Gabriele Macari1, Daniele Toti1, Fabio Polticelli2,3.   

Abstract

In the current "genomic era" the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibitors, allosteric regulators, etc.) is pivotal for protein functions in the vast majority of the cases and knowledge of the region where these processes take place is essential for protein function prediction and drug design. In this regard, computational methods represent essential tools to tackle this problem. A significant number of software tools have been developed in the last few years which exploit either protein sequence information, structure information or both. This review describes the most recent developments in protein function recognition and binding site prediction, in terms of both freely-available and commercial solutions and tools, detailing the main characteristics of the considered tools and providing a comparative analysis of their performance.

Keywords:  Binding region; Binding site recognition; Protein–ligand interactions; Software

Year:  2019        PMID: 31628659     DOI: 10.1007/s10822-019-00235-7

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  88 in total

1.  GRASP2: visualization, surface properties, and electrostatics of macromolecular structures and sequences.

Authors:  Donald Petrey; Barry Honig
Journal:  Methods Enzymol       Date:  2003       Impact factor: 1.600

2.  Automated tertiary structure prediction with accurate local model quality assessment using the IntFOLD-TS method.

Authors:  Liam J McGuffin; Daniel B Roche
Journal:  Proteins       Date:  2011-08-30

3.  Coupling between catalytic site and collective dynamics: a requirement for mechanochemical activity of enzymes.

Authors:  Lee-Wei Yang; Ivet Bahar
Journal:  Structure       Date:  2005-06       Impact factor: 5.006

Review 4.  Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening.

Authors:  Alasdair T R Laurie; Richard M Jackson
Journal:  Curr Protein Pept Sci       Date:  2006-10       Impact factor: 3.272

5.  Identifying and characterizing binding sites and assessing druggability.

Authors:  Thomas A Halgren
Journal:  J Chem Inf Model       Date:  2009-02       Impact factor: 4.956

6.  Toward Rational Design of Selective Molecularly Imprinted Polymers (MIPs) for Proteins: Computational and Experimental Studies of Acrylamide Based Polymers for Myoglobin.

Authors:  Mark V Sullivan; Sarah R Dennison; Georgios Archontis; Subrayal M Reddy; Joseph M Hayes
Journal:  J Phys Chem B       Date:  2019-06-21       Impact factor: 2.991

Review 7.  Implications of the small number of distinct ligand binding pockets in proteins for drug discovery, evolution and biochemical function.

Authors:  Jeffrey Skolnick; Mu Gao; Ambrish Roy; Bharath Srinivasan; Hongyi Zhou
Journal:  Bioorg Med Chem Lett       Date:  2015-02-03       Impact factor: 2.823

8.  Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites.

Authors:  Maxim Totrov
Journal:  BMC Bioinformatics       Date:  2011-02-15       Impact factor: 3.169

9.  FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions.

Authors:  Daniel B Roche; Maria T Buenavista; Liam J McGuffin
Journal:  PLoS One       Date:  2012-05-30       Impact factor: 3.240

10.  BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions.

Authors:  Jianyi Yang; Ambrish Roy; Yang Zhang
Journal:  Nucleic Acids Res       Date:  2012-10-18       Impact factor: 16.971

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  3 in total

1.  Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking.

Authors:  Matjaž Simončič; Miha Lukšič; Maksym Druchok
Journal:  J Mol Liq       Date:  2022-02-18       Impact factor: 6.165

2.  CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities.

Authors:  Jean-Rémy Marchand; Bernard Pirard; Peter Ertl; Finton Sirockin
Journal:  J Comput Aided Mol Des       Date:  2021-05-29       Impact factor: 3.686

3.  A Comprehensive Mapping of the Druggable Cavities within the SARS-CoV-2 Therapeutically Relevant Proteins by Combining Pocket and Docking Searches as Implemented in Pockets 2.0.

Authors:  Silvia Gervasoni; Giulio Vistoli; Carmine Talarico; Candida Manelfi; Andrea R Beccari; Gabriel Studer; Gerardo Tauriello; Andrew Mark Waterhouse; Torsten Schwede; Alessandro Pedretti
Journal:  Int J Mol Sci       Date:  2020-07-21       Impact factor: 5.923

  3 in total

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