Literature DB >> 31086984

Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models.

Jiahua He1, Huanyu Tao1, Sheng-You Huang1.   

Abstract

MOTIVATION: Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB).
RESULTS: Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions.
AVAILABILITY AND IMPLEMENTATION: http://huanglab.phys.hust.edu.cn/mprdock/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Mesh:

Substances:

Year:  2019        PMID: 31086984     DOI: 10.1093/bioinformatics/btz388

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  4 in total

1.  Methods for Molecular Modelling of Protein Complexes.

Authors:  Tejashree Rajaram Kanitkar; Neeladri Sen; Sanjana Nair; Neelesh Soni; Kaustubh Amritkar; Yogendra Ramtirtha; M S Madhusudhan
Journal:  Methods Mol Biol       Date:  2021

2.  Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains.

Authors:  Anastasis Oulas; Maria Zanti; Marios Tomazou; Margarita Zachariou; George Minadakis; Marilena M Bourdakou; Pavlos Pavlidis; George M Spyrou
Journal:  PLoS One       Date:  2021-01-26       Impact factor: 3.240

3.  Conformational variability in proteins bound to single-stranded DNA: A new benchmark for new docking perspectives.

Authors:  Dominique Mias-Lucquin; Isaure Chauvot de Beauchene
Journal:  Proteins       Date:  2021-10-14

4.  Exploring the Binding Mechanism and Dynamics of EndoMS/NucS to Mismatched dsDNA.

Authors:  Yanjun Zhang; Shengyou Huang
Journal:  Int J Mol Sci       Date:  2019-10-17       Impact factor: 5.923

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.