Sequence co-evolution gives 3D contacts and structures of protein complexes

By Thomas A. Hopf, Charlotta P I Schärfe, João P. G. L. M. Rodrigues, Anna G. Green, Oliver Kohlbacher, Christopher Sander, Alexandre M. J. J. Bonvin, Debora Marks. 2014

496 citations recorded by the source corpus.

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Computational Biology & Bioinformatics

Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “To each possible amino-acid sequence (a1, …, aL) consisting of L amino acids or gaps (L denotes the alignment width), a quantitative phenotype φ(a1, …, aL) is assigned.”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “…applied to protein complex assembly and interactions, disordered region structure prediction, RNA structure prediction, and mutagenesis analysis (Ovchinnikov et al., 2014; Uguzzoni et al., 2017; Hopf et al., 2014, 2017; Toth-Petroczy et al., 2016; De Leonardis et al., 2015; Weinreb et al., 2016).”
Large-scale determination of previously unsolved protein structures using evolutionary information (2015, methodology): “…from such predictions have been used to model a wide range of unknown protein structures ( Hayat et al., 2014 ; Wickles et al., 2014 ; Abriata, 2015 ; Antala et al., 2015 ; Hopf et al., 2015 ; Tian et al., 2015 ) and protein–protein complexes ( Ovchinnikov et al., 2014 ; Hopf et al., 2014 ).”
Assessment of Contact Predictions in CASP12: Co-Evolution and Deep Learning Coming of Age (2017, background): “Both the While a minimum number of 0.3 to 1 non-redundant sequences per residue was suggested to be the cutoff to effectively use coevolution data, 16,45,46 good predictions with precision above 50% are observed at alignment depths around L/10 already.”
Origins of coevolution between residues distant in protein 3D structures (2017, background): “…in using direct couplings to infer residue – residue contacts for protein structure prediction (6 – 9), protein – protein complex prediction (10 – 12), identification of specific interaction partners among two sets of paralogous proteins (13), resolution of ambiguities in protein NMR spectra…”
Large-Scale Conformational Transitions and Dimerization Are Encoded in the Amino-Acid Sequences of Hsp70 Chaperones (2015, unknown): “This technique not only can reliably predict the protein native structure [45,48–52], but it can also predict the presence of multiple conformers of the same proteins [44,48,53], homo-multimerization [44,48] and protein-protein interactions [54,55].”
Limits and potential of combined folding and docking (2021, methodology): “In principle, the same type of methods used for predicting the structure of a single protein can predict the interaction between two proteins [9,10].”