Literature DB >> 34432238

Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods with a Focus on FunFOLD3.

Danielle Allison Brackenridge1, Liam James McGuffin2.   

Abstract

Proteins are essential molecules with a diverse range of functions; elucidating their biological and biochemical characteristics can be difficult and time consuming using in vitro and/or in vivo methods. Additionally, in vivo protein-ligand binding site elucidation is unable to keep place with current growth in sequencing, leaving the majority of new protein sequences without known functions. Therefore, the development of new methods, which aim to predict the protein-ligand interactions and ligand-binding site residues directly from amino acid sequences, is becoming increasingly important. In silico prediction can utilise either sequence information, structural information or a combination of both. In this chapter, we will discuss the broad range of methods for ligand-binding site prediction from protein structure and we will describe our method, FunFOLD3, for the prediction of protein-ligand interactions and ligand-binding sites based on template-based modelling. Additionally, we will describe the step-by-step instructions using the FunFOLD3 downloadable application along with examples from the Critical Assessment of Techniques for Protein Structure Prediction (CASP) where FunFOLD3 has been used to aid ligand and ligand-binding site prediction. Finally, we will introduce our newer method, FunFOLD3-D, a version of FunFOLD3 which aims to improve template-based protein-ligand binding site prediction through the integration of docking, using AutoDock Vina.
© 2021. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Critical Assessment of Techniques for Protein Structure Prediction (CASP); Docking; Ligand-binding site prediction; Protein–ligand interactions; Template-based modelling

Mesh:

Substances:

Year:  2021        PMID: 34432238     DOI: 10.1007/978-1-0716-1665-9_3

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  43 in total

1.  Identifying and characterizing binding sites and assessing druggability.

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

2.  EasyMIFS and SiteHound: a toolkit for the identification of ligand-binding sites in protein structures.

Authors:  Dario Ghersi; Roberto Sanchez
Journal:  Bioinformatics       Date:  2009-09-29       Impact factor: 6.937

3.  Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere.

Authors:  Jian Yu; Yong Zhou; Isao Tanaka; Min Yao
Journal:  Bioinformatics       Date:  2009-10-21       Impact factor: 6.937

4.  In silico Identification and Characterization of Protein-Ligand Binding Sites.

Authors:  Daniel Barry Roche; Liam James McGuffin
Journal:  Methods Mol Biol       Date:  2016

5.  Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

Authors:  John A Capra; Roman A Laskowski; Janet M Thornton; Mona Singh; Thomas A Funkhouser
Journal:  PLoS Comput Biol       Date:  2009-12-04       Impact factor: 4.475

6.  3DLigandSite: predicting ligand-binding sites using similar structures.

Authors:  Mark N Wass; Lawrence A Kelley; Michael J E Sternberg
Journal:  Nucleic Acids Res       Date:  2010-05-31       Impact factor: 16.971

7.  The FunFOLD2 server for the prediction of protein-ligand interactions.

Authors:  Daniel B Roche; Maria T Buenavista; Liam J McGuffin
Journal:  Nucleic Acids Res       Date:  2013-06-12       Impact factor: 16.971

8.  FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins.

Authors:  Daniel B Roche; Stuart J Tetchner; Liam J McGuffin
Journal:  BMC Bioinformatics       Date:  2011-05-16       Impact factor: 3.307

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.  Fpocket: an open source platform for ligand pocket detection.

Authors:  Vincent Le Guilloux; Peter Schmidtke; Pierre Tuffery
Journal:  BMC Bioinformatics       Date:  2009-06-02       Impact factor: 3.169

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