Literature DB >> 27862282

A non-redundant protein-RNA docking benchmark version 2.0.

Chandran Nithin1, Sunandan Mukherjee1, Ranjit Prasad Bahadur1.   

Abstract

We present an updated version of the protein-RNA docking benchmark, which we first published four years back. The non-redundant protein-RNA docking benchmark version 2.0 consists of 126 test cases, a threefold increase in number compared to its previous version. The present version consists of 21 unbound-unbound cases, of which, in 12 cases, the unbound RNAs are taken from another complex. It also consists of 95 unbound-bound cases where only the protein is available in the unbound state. Besides, we introduce 10 new bound-unbound cases where only the RNA is found in the unbound state. Based on the degree of conformational change of the interface residues upon complex formation the benchmark is classified into 72 rigid-body cases, 25 semiflexible cases and 19 full flexible cases. It also covers a wide range of conformational flexibility including small side chain movement to large domain swapping in protein structures as well as flipping and restacking in RNA bases. This benchmark should provide the docking community with more test cases for evaluating rigid-body as well as flexible docking algorithms. Besides, it will also facilitate the development of new algorithms that require large number of training set. The protein-RNA docking benchmark version 2.0 can be freely downloaded from http://www.csb.iitkgp.ernet.in/applications/PRDBv2. Proteins 2017; 85:256-267.
© 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Keywords:  conformation change; protein-RNA complex; protein-RNA docking; protein-RNA interaction; protein-RNA interface

Mesh:

Substances:

Year:  2016        PMID: 27862282     DOI: 10.1002/prot.25211

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  12 in total

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Authors:  Yumeng Yan; Di Zhang; Pei Zhou; Botong Li; Sheng-You Huang
Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

2.  RPpocket: An RNA-Protein Intuitive Database with RNA Pocket Topology Resources.

Authors:  Rui Yang; Haoquan Liu; Liu Yang; Ting Zhou; Xinyao Li; Yunjie Zhao
Journal:  Int J Mol Sci       Date:  2022-06-21       Impact factor: 6.208

3.  A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences.

Authors:  Ankita Agarwal; Kunal Singh; Shri Kant; Ranjit Prasad Bahadur
Journal:  Comput Struct Biotechnol J       Date:  2022-06-17       Impact factor: 6.155

4.  The HDOCK server for integrated protein-protein docking.

Authors:  Yumeng Yan; Huanyu Tao; Jiahua He; Sheng-You Huang
Journal:  Nat Protoc       Date:  2020-04-08       Impact factor: 13.491

Review 5.  Molecular Modeling Applied to Nucleic Acid-Based Molecule Development.

Authors:  Arne Krüger; Flávia M Zimbres; Thales Kronenberger; Carsten Wrenger
Journal:  Biomolecules       Date:  2018-08-27

6.  Protein-assisted RNA fragment docking (RnaX) for modeling RNA-protein interactions using ModelX.

Authors:  Javier Delgado Blanco; Leandro G Radusky; Damiano Cianferoni; Luis Serrano
Journal:  Proc Natl Acad Sci U S A       Date:  2019-11-15       Impact factor: 11.205

7.  A structure-based model for the prediction of protein-RNA binding affinity.

Authors:  Chandran Nithin; Sunandan Mukherjee; Ranjit Prasad Bahadur
Journal:  RNA       Date:  2019-08-08       Impact factor: 4.942

8.  RNAmining: A machine learning stand-alone and web server tool for RNA coding potential prediction.

Authors:  Thaís A R Ramos; Nilbson R O Galindo; Raúl Arias-Carrasco; Cecília F da Silva; Vinicius Maracaja-Coutinho; Thaís G do Rêgo
Journal:  F1000Res       Date:  2021-04-26

Review 9.  Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes.

Authors:  Chandran Nithin; Pritha Ghosh; Janusz M Bujnicki
Journal:  Genes (Basel)       Date:  2018-08-25       Impact factor: 4.096

10.  An account of solvent accessibility in protein-RNA recognition.

Authors:  Sunandan Mukherjee; Ranjit Prasad Bahadur
Journal:  Sci Rep       Date:  2018-07-12       Impact factor: 4.379

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