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

By Fusong Ju, Jianwei Zhu, Bin Shao, Lupeng Kong, Tie-Yan Liu, Weinan Zheng, Dongbo Bu. 2020

72 citations recorded by the source corpus.

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. Protein structure prediction is a challenge. A new deep learning framework, CopulaNet, is a major step forward toward end-to-end prediction of inter-residue distances and protein tertiary structures with improved accuracy and efficiency.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021, methodology): “…have been used to predict inter-residue contacts, distance and/or orientations (Adhikari and Cheng, 2018; Yang et al., 2020; Senior et al., 2020; Ju et al., 2021), that are subsequently transformed into additional constraints or differentiable energy terms for protein structure optimization.”
SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding (2021, background): “In [2, 48, 30, 17], deep networks are introduced to predict inter-residue contacts, distance, and/or orientations, which are then transformed into additional restraints or differentiable energy terms for structure optimization.”