Direct-coupling analysis of residue coevolution captures native contacts across many protein families

By Faruck Morcos, Andrea Pagnani, Bryan Lunt, A. Bertolino, Debora Marks, Christopher Sander, Riccardo Zecchina, José N. Onuchic, Terence Hwa, Martin Weigt. 2011

1,436 citations recorded by the source corpus.

The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.

Subfields: 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, methodology): “Maximum entropy methods disentangle direct interactions from indirect interactions by inferring parameters of a posited generating distribution for the sequence family (Weigt et al., 2009; Marks et al., 2011; Morcos et al., 2011; Jones et al., 2011; Balakrishnan et al., 2011; Ekeberg et al., 2013b).”
Predicting multiple conformations via sequence clustering and AlphaFold2 (2023, positive): “…performance in the CASP14 competition 6 in part by advancing the state of the art for inferring patterns of interactions between related sequences in a multiple-sequence alignment (MSA), building on a long history of methods for inferring these patterns 7–10 , often called evolutionary couplings.”
Evolutionary-scale prediction of atomic level protein structure with a language model (2022, background): “[7] Faruck Morcos, Andrea Pagnani, Bryan Lunt, Arianna Bertolino, Debora S.”
CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, neutral): “This result is consistent with the previous observation on the low performance of the approaches based on covariance matrix [11].”
Modeling aspects of the language of life through transfer-learning protein sequences (2019, positive): “Ar-guably, the most important breakthrough for protein structure prediction over the last decade was a more efficient way of using evolutionary couplings [28 – 31].”
Revealing evolutionary constraints on proteins through sequence analysis (2018, methodology): “More generally, such one- and two-body descriptions have been very successfully employed via Direct Coupling Analysis (DCA) to identify strongly coupled residues that are in contact within a folded protein [36–38], to investigate folding [39], and to predict fitness [33, 40–45] and conformational ch”
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, methodology): “The Direct-Coupling Analysis (DCA) (Weigt et al. 2009; Morcos et al. 2011) has been proposed to disentangle such indirect effects from direct (i.”
Protein structure prediction from sequence variation (2012, methodology): “Similarly, another report 16 has demonstrated that three of the top 20 predicted contacts for an ATPase domain were false positives for the monomer but true positives for the multimer.”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, methodology): “Previous approaches ( Morcos et al., 2011 ; Jones et al., 2012 ) estimated v, w using an approximate moment matching approach ( Kamisetty et al., 2013 ) by inverting a generalized covariance matrix.”
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) (2019, methodology): “Contact predictionhas been extensively used instructure prediction 16,20,29,31 but previous work has also made residue distance predictions, 3,35 and these were used for structure prediction in CASP13 by other groups in distance geometry approaches.”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “Importantly, some of the false contacts identified by DeepContact are true homodimer contacts and others may be true in another conformation, particularly given the small distances between the residues of many of the false contacts (Figures 3A and 4B) (Hopf et al., 2012; Morcos et al., 2011; Toth-Pe”