CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction

Fusong Ju, Jianwei Zhu (), Bin Shao, Lupeng Kong, Tie-Yan Liu, Wei-Mou Zheng and Dongbo Bu ()
Additional contact information
Fusong Ju: Institute of Computing Technology, Chinese Academy of Sciences
Jianwei Zhu: Microsoft Research Asia
Bin Shao: Microsoft Research Asia
Lupeng Kong: Institute of Computing Technology, Chinese Academy of Sciences
Tie-Yan Liu: Microsoft Research Asia
Wei-Mou Zheng: University of Chinese Academy of Sciences
Dongbo Bu: Institute of Computing Technology, Chinese Academy of Sciences

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predicting protein structure. Most existing approaches adopt an indirect strategy, i.e., inferring residue co-evolution based on some hand-crafted features, say, a covariance matrix, calculated from multiple sequence alignment (MSA) of target protein. This indirect strategy, however, cannot fully exploit the information carried by MSA. Here, we report an end-to-end deep neural network, CopulaNet, to estimate residue co-evolution directly from MSA. The key elements of CopulaNet include: (i) an encoder to model context-specific mutation for each residue; (ii) an aggregator to model residue co-evolution, and thereafter estimate inter-residue distances. Using CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrate that CopulaNet can predict protein structure with improved accuracy and efficiency. This study represents a step toward improved end-to-end prediction of inter-residue distances and protein tertiary structures.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-021-22869-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22869-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-22869-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().