Challenges in structural modeling of RNA-protein interactions

被引：4

作者：

Liu, Xudong ^{[1
]}

Duan, Yingtian ^{[1
]}

Hong, Xu ^{[1
]}

Xie, Juan ^{[1
]}

Liu, Shiyong ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Phys, Wuhan 430074, Hubei, Peoples R China

来源：

CURRENT OPINION IN STRUCTURAL BIOLOGY | 2023年 / 81卷

基金：

中国国家自然科学基金;

关键词：

WEB SERVER; RESIDUE CONTACTS; FORCE-FIELD; DOCKING; PREDICTION; BINDING; DYNAMICS; COMPLEX; PSEUDOURIDINE; ACCURACY;

D O I：

10.1016/j.sbi.2023.102623

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

In the past few years, the number of RNA-binding proteins (RBP) and RNA-RBP interactions has increased significantly. Here, we review recent developments in the methodology for protein-RNA and protein-protein complex structure modeling with deep learning and co-evolution, as well as discuss the challenges and opportunities for building a reliable approach for protein-RNA complex structure modelling. Protein Data bank (PDB) and Cross-linking immunoprecipitation (CLIP) data could be combined together and used to infer 2D geometry of protein-RNA interactions by deep learning.

引用

页数：8

共 81 条

[1] CONFOLD2: improved contact-driven ab initio protein structure modeling [J].

Adhikari, Badri ;

Cheng, Jianlin .

BMC BIOINFORMATICS, 2018, 19

[2] Protein-RNA Docking Using ICM [J].

Arnautova, Yelena A. ;

Abagyan, Ruben ;

Totrov, Maxim .

JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2018, 14 (09) :4971-4984

[3]

Baek M, 2022, BIORXIV

[4] Deep learning and protein structure modeling [J].

Baek, Minkyung ;

Baker, David .

NATURE METHODS, 2022, 19 (01) :13-14

[5] Accurate prediction of protein structures and interactions using a three-track neural network [J].

Baek, Minkyung ;

DiMaio, Frank ;

Anishchenko, Ivan ;

Dauparas, Justas ;

Ovchinnikov, Sergey ;

Lee, Gyu Rie ;

Wang, Jue ;

Cong, Qian ;

Kinch, Lisa N. ;

Schaeffer, R. Dustin ;

Millan, Claudia ;

Park, Hahnbeom ;

Adams, Carson ;

Glassman, Caleb R. ;

DeGiovanni, Andy ;

Pereira, Jose H. ;

Rodrigues, Andria V. ;

van Dijk, Alberdina A. ;

Ebrecht, Ana C. ;

Opperman, Diederik J. ;

Sagmeister, Theo ;

Buhlheller, Christoph ;

Pavkov-Keller, Tea ;

Rathinaswamy, Manoj K. ;

Dalwadi, Udit ;

Yip, Calvin K. ;

Burke, John E. ;

Garcia, K. Christopher ;

Grishin, Nick V. ;

Adams, Paul D. ;

Read, Randy J. ;

Baker, David .

SCIENCE, 2021, 373 (6557) :871-+

[6] Binding of the Bacteriophage P22 N-Peptide to the boxB RNA Motif Studied by Molecular Dynamics Simulations [J].

Bahadur, Ranjit P. ;

Kannan, Srinivasaraghavan ;

Zacharias, Martin .

BIOPHYSICAL JOURNAL, 2009, 97 (12) :3139-3149

[7] A protein-RNA docking benchmark (I): Nonredundant cases [J].

Barik, Amita ;

Nithin, C. ;

Manasa, P. ;

Bahadur, Ranjit Prasad .

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2012, 80 (07) :1866-1871

[8] Protein-assisted RNA fragment docking (RnaX) for modeling RNA-protein interactions using ModelX [J].

Blanco, Javier Delgado ;

Radusky, Leandro G. ;

Cianferoni, Damiano ;

Serrano, Luis .

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2019, 116 (49) :24568-24573

[9] MODOMICS: a database of RNA modification pathways. 2017 update [J].

Boccaletto, Pietro ;

Machnicka, Magdalena A. ;

Purta, Elzbieta ;

Piatkowski, Pawe ;

Baginski, Blazej ;

Wirecki, Tomasz K. ;

de Crecy-Lagard, Valerie ;

Ross, Robert ;

Limbach, Patrick A. ;

Kotter, Annika ;

Helm, Mark ;

Bujnicki, Janusz M. .

NUCLEIC ACIDS RESEARCH, 2018, 46 (D1) :D303-D307

[10] Improved prediction of protein-protein interactions using AlphaFold2 [J].

Bryant, P. ;

Pozzati, G. ;

Elofsson, A. .

NATURE COMMUNICATIONS, 2022, 13 (01)

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