Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

By Yang Liu, Perry Palmedo, Qing Ye, Bonnie Berger, Jian Peng. 2017

113 citations recorded by the source corpus.

SUMMARY While genes are defined by sequence, in biological systems a protein’s function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferring structural constraints on macromolecules. Still, many proteins of interest lack sufficient numbers of related sequences, leading to noisy, error-prone residue-residue contact predictions. Here we introduce DeepContact, a convolutional neural network (CNN)-based approach that discovers co-evolutionary motifs and leverages these patterns to enable accurate inference of contact probabilities, particularly when few related sequences are available. DeepContact significantly improves performance over previous methods, including in the CASP12 blind contact prediction task where we achieved top performance with another CNN-based approach. Moreover, our tool converts hard-to-interpret coupling scores into probabilities, moving the field toward a consistent metric to assess contact prediction across diverse proteins. Through substantially improving the precision-recall behavior of contact prediction, DeepContact suggests we are near a paradigm shift in template-free modeling for protein structure prediction.

Subfields: Molecular Biology, Model Architectures, Software Engineering

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Citations in the corpus

Evolutionary-scale prediction of atomic level protein structure with a language model (2022, background): “[9] Yang Liu, Perry Palmedo, Qing Ye, Bonnie Berger, and Jian Peng.”
Evolutionary context-integrated deep sequence modeling for protein engineering (2020, background): “It has been shown that the magnitudes of eij terms can accurately predict protein contacts [10] and 3D structures [45] .”
End-to-end differentiable learning of protein structure (2018, background): “In contrast, modern co-evolution methods leverage learning and multi-scale representations to substantially improve performance (Liu et al., 2017; Wang et al., 2016).”
Assessing the accuracy of contact predictions in CASP13 (2019, background): “After 2010, when first papers on correlated mutations were published, a number of refinements took place, such as combining a set of nonoverlapping contact predictions in a consensus approach and especially the application of various supervised deep neural network based approaches,(42) further impro”
Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13 (2019, background): “Due to its significant importance to protein structure prediction, the problem of contact-map prediction has drawn an increasing amount of attention, and as a result, several new approaches have been proposed within the last decade.(10-21) Initial studies on protein contact prediction focused on the”
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization (2023, unknown): “These are necessary to (1) tackle new tasks, like protein–ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model’s capacity to generalize to unseen regions of fold space.”