Protein interaction networks revealed by proteome coevolution

By Qian Cong, Ivan V. Anishchenko, Sergey Ovchinnikov, David Baker. 2019

208 citations recorded by the source corpus.

Predicting protein pairs Biological function is driven by interaction between proteins. High-throughput experimental techniques have provided large datasets of protein interactions in several organisms; however, much combinatorial space remains uncharted. Cong et al. predict protein interfaces by identifying coevolving residues in aligned protein sequences (see the Perspective by Vajda and Emili). In comparison with gold-standard and negative control sets, they show that the accuracy is higher than for proteome-wide two-hybrid and mass spectrometry screens. The approach predicts 1618 protein interactions in Escherichia coli, 682 of which were unanticipated, and 911 interacting pairs in Mycobacterium tuberculosis, most of which had not been previously described. With an expected false-positive rate of between 10 and 20%, the predicted interactions and networks provide an excellent starting point for further study. Science, this issue p. 185; see also p. 120 A computational approach reveals hundreds of protein-protein interactions in Escherichia coli and Mycobacterium tuberculosis. Residue-residue coevolution has been observed across a number of protein-protein interfaces, but the extent of residue coevolution between protein families on the whole-proteome scale has not been systematically studied. We investigate coevolution between 5.4 million pairs of proteins in Escherichia coli and between 3.9 millions pairs in Mycobacterium tuberculosis. We find strong coevolution for binary complexes involved in metabolism and weaker coevolution for larger complexes playing roles in genetic information processing. We take advantage of this coevolution, in combination with structure modeling, to predict protein-protein interactions (PPIs) with an accuracy that benchmark studies suggest is considerably higher than that of proteome-wide two-hybrid and mass spectrometry screens. We identify hundreds of previously uncharacterized PPIs in E. coli and M. tuberculosis that both add components to known protein complexes and networks and establish the existence of new ones.

Subfields: Molecular Biology, Spectroscopy, Computational Biology & Bioinformatics

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Citations in the corpus

Rethinking Attention with Performers (2020, background): “Recently published approaches require large evolutionary sequence alignments, a bottleneck for applications to mammalian genomes (Cong et al., 2019).”
Accurate prediction of protein structures and interactions using a 3-track neural network (2021, neutral): “We tested the endto-end 3-track network on paired sequence alignments for complexes of known structures (32) (see Methods and table S4 for details) containing two (Fig.”
Improved prediction of protein-protein interactions using AlphaFold2 (2021, methodology): “We supplied four different types of MSAs to AF2: (1) the MSAs generated by using the default AF2 settings, (2) the top paired MSAs constructed using HHblits, described above, (3) both alignments together and finally, (4) the top paired and single-chain MSAs from HHblits to speed up predictions (only”
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers (2020, methodology): “Recently published approaches require large evolutionary sequence alignments, a bottleneck for applications to mammalian genomes [14].”
Protein complex prediction with AlphaFold-Multimer (2021, methodology): “In practice we use a weighted combination of pTM and ipTM as our model conﬁdence metric, in order to account for some intra-chain conﬁdence in our model ranking:”
Computed structures of core eukaryotic protein complexes (2021, unknown): “…ortholog sequences of the two proteins in many different species, joining them to create paired multiple sequence alignments (pMSAs), and then determining the extent to which changes in the sequences of orthologs for the first protein covary with ortholog sequence changes for the second ( 9 , 10 ).”
Towards a structurally resolved human protein interaction network (2021, methodology): “In addition, co-evolution-based information has been used to predict protein interactions and to guide structural docking for bacterial proteins 15 .”
Limits and potential of combined folding and docking (2021, methodology): “Several interaction databases exist [24,25], and methods, including co-evolution based methods [26], to predict interactions have been developed.”