Proteome-scale discovery of protein interactions with residue-level resolution using sequence coevolution

By Anna G. Green, Hadeer Elhabashy, Kelly P. Brock, Rohan Maddamsetti, Oliver Kohlbacher, Debora Marks. 2019

6 citations recorded by the source corpus.

The majority of protein interactions in most organisms are unknown, and experimental methods for determining protein interactions can yield divergent results. Here we use an orthogonal, purely computational method based on sequence coevolution to discover protein interactions at large scale. In the model organism Escherichia coli, 53% of protein pairs in the proteome are eligible for our method given currently available sequenced genomes. When assaying the entire cell envelope proteome, which is understudied due to experimental challenges, we found 620 likely interactions and their predicted structures, increasing the space of known interactions by 529. Our results show that genomic sequencing data can be used to predict and resolve protein interactions to atomic resolution at large scale. Predictions and code are freely available at https://marks.hms.harvard.edu/ecolicomplex and https://github.com/debbiemarkslab/EVcouplings

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Citations in the corpus

Protein complex prediction with AlphaFold-Multimer (2021, methodology): “Various heuristic methods exist to perform this pairing [27, 28, 29, 12].”
Protein design and variant prediction using autoregressive generative models (2021, methodology): “…years generative models of these natural protein sequences have been powerful tools for the prediction of the ﬁ rst 3D fold from sequences alone 21,22 , to generally more 3D structures and conformational plasticity 23,24 , protein interactions 25 – 28 , and most recently, mutation effects 29 – 34 .”