Origins of coevolution between residues distant in protein 3D structures

By Ivan V. Anishchenko, Sergey Ovchinnikov, Hetunandan Kamisetty, David Baker. 2017

177 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “Contacts can be inferred through evolutionary correlations between residues in a sequence family (Marks et al., 2011; Anishchenko et al., 2017).”
Predicting multiple conformations via sequence clustering and AlphaFold2 (2023, positive): “As a starting point, we selected 628 proteins 48–150 amino acids in length from a database of MSAs associated with crystal structures 30 (Methods).”
Prediction of multiple conformational states by combining sequence clustering with AlphaFold2 (2022, methodology): “We applied our method to an existing database of MSAs associated with crystal structures 30 to aim to detect novel conformational states in known protein families.”
Assessing the accuracy of contact predictions in CASP13 (2019, background): “28-33 A significant advance took place when various approaches focused on addressing the transitivity problem in residue contact signals, such as the direct coupling analysis, 34-38 sparse inverse covariance methods, 37,39 and network decomposition approaches.”
Protein language model embeddings for fast, accurate, alignment-free protein structure prediction (2021, methodology): “Direct Coupling Analysis (DCA) sharpens this signal (Anishchenko et al., 2017) either through pseudolikelihood maximization (Balakrishnan et al., 2011; Seemayer et al., 2014) or through sparse inverse covariance estimation (Jones et al., 2011).”
Inter-residue, inter-protein and inter-family coevolution: bridging the scales (2017, unknown): “[34,39].”
Direct coupling analysis of epistasis in allosteric materials (2018, background): “In the context of allostery, there is no statistical evidence for the existence of long-range direct couplings that would reveal allosteric channels [22], in apparent contradiction with the existence of extended sectors reported in [15] and the observation of long-range epistasis [6].”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “However, a recent study found that a substantial proportion of these false positive result from structural variation among homologs or from homo-oligomeric contacts, complicating the question of which couplings are true positives [168].”
Deep-learning contact-map guided protein structure prediction in CASP13 (2019, background): “49 This case demonstrates that false positive contacts may lead to incorrect local folding even though high quality templates are”
SaProt: Protein Language Modeling with Structure-aware Vocabulary (2024, background): “Through self-supervised training on vast amounts of protein 1D residue sequences, PLMs have proven highly proficient in capturing long-range residue correlations, i.e., co-evolution (Anishchenko et al., 2017; Rao et al., 2020).”
Unified framework for modeling multivariate distributions in biological sequences (2019, methodology): “The test was performed on a diverse set of proteins with at least 1K effective sequences from (Anishchenko et al., 2017).”
Coevolutionary Analysis of Protein Subfamilies by Sequence Reweighting (2019, background): “Similarly, organisms with different genetic backgrounds and evolving in different environments will be subject to different fitness landscapes, thus not necessarily requiring the exact same structural and functional features, while still maintaining the same overall fold and function [20,21].”