Literature DB >> 25330972

Methods for predicting protein-ligand binding sites.

Zhong-Ru Xie1, Ming-Jing Hwang.   

Abstract

Ligand binding is required for many proteins to function properly. A large number of bioinformatics tools have been developed to predict ligand binding sites as a first step in understanding a protein's function or to facilitate docking computations in virtual screening based drug design. The prediction usually requires only the three-dimensional structure (experimentally determined or computationally modeled) of the target protein to be searched for ligand binding site(s), and Web servers have been built, allowing the free and simple use of prediction tools. In this chapter, we review the underlying concepts of the methods used by various tools, and discuss their different features and the related issues of ligand binding site prediction. Some cautionary notes about the use of these tools are also provided.

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Year:  2015        PMID: 25330972     DOI: 10.1007/978-1-4939-1465-4_17

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


  13 in total

1.  Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites.

Authors:  Sambit K Mishra; Gaurav Kandoi; Robert L Jernigan
Journal:  Proteins       Date:  2019-06-22

2.  Identification of promising multi-targeting inhibitors of obesity from Vernonia amygdalina through computational analysis.

Authors:  Oludare M Ogunyemi; Gideon A Gyebi; Ibrahim M Ibrahim; Adewale M Esan; Charles O Olaiya; Mohameed M Soliman; Gaber El-Saber Batiha
Journal:  Mol Divers       Date:  2022-02-18       Impact factor: 2.943

Review 3.  Drug Design by Pharmacophore and Virtual Screening Approach.

Authors:  Deborah Giordano; Carmen Biancaniello; Maria Antonia Argenio; Angelo Facchiano
Journal:  Pharmaceuticals (Basel)       Date:  2022-05-23

4.  Prediction of DNA-Binding Protein-Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature.

Authors:  Wei Wang; Yu Zhang; Dong Liu; HongJun Zhang; XianFang Wang; Yun Zhou
Journal:  Front Bioeng Biotechnol       Date:  2022-04-20

5.  Development of a machine learning method to predict membrane protein-ligand binding residues using basic sequence information.

Authors:  M Xavier Suresh; M Michael Gromiha; Makiko Suwa
Journal:  Adv Bioinformatics       Date:  2015-01-31

6.  HotSpot Wizard 2.0: automated design of site-specific mutations and smart libraries in protein engineering.

Authors:  Jaroslav Bendl; Jan Stourac; Eva Sebestova; Ondrej Vavra; Milos Musil; Jan Brezovsky; Jiri Damborsky
Journal:  Nucleic Acids Res       Date:  2016-05-12       Impact factor: 16.971

7.  Protein ligand-specific binding residue predictions by an ensemble classifier.

Authors:  Xiuzhen Hu; Kai Wang; Qiwen Dong
Journal:  BMC Bioinformatics       Date:  2016-11-17       Impact factor: 3.169

8.  DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.

Authors:  Samuel Godfrey Hendrix; Kuan Y Chang; Zeezoo Ryu; Zhong-Ru Xie
Journal:  Int J Mol Sci       Date:  2021-05-24       Impact factor: 5.923

9.  Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms.

Authors:  Jhih-Wei Jian; Pavadai Elumalai; Thejkiran Pitti; Chih Yuan Wu; Keng-Chang Tsai; Jeng-Yih Chang; Hung-Pin Peng; An-Suei Yang
Journal:  PLoS One       Date:  2016-08-11       Impact factor: 3.240

10.  Importance of Fluctuating Amino Acid Residues in Folding and Binding of Proteins.

Authors:  Renganathan Senthil; Singaravelu Usha; Konda Mani Saravanan
Journal:  Avicenna J Med Biotechnol       Date:  2019 Oct-Dec
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