Protein interaction networks revealed by proteome coevolution.
TLDR
A computational approach reveals 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.Abstract:
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.read more
Citations
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Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek,Frank DiMaio,Ivan Anishchenko,Justas Dauparas,Sergey Ovchinnikov,Gyu Rie Lee,Jue Wang,Qian Cong,Lisa N. Kinch,R. Dustin Schaeffer,Claudia Millán,Hahnbeom Park,Carson Adams,Caleb R. Glassman,Andy DeGiovanni,Jose Henrique Pereira,Andria V. Rodrigues,Alberdina A. van Dijk,Ana C. Ebrecht,Diederik J. Opperman,Theo Sagmeister,Christoph Buhlheller,Christoph Buhlheller,Tea Pavkov-Keller,Manoj K. Rathinaswamy,Udit Dalwadi,Calvin K. Yip,John E. Burke,K. Christopher Garcia,Nick V. Grishin,Paul D. Adams,Paul D. Adams,Randy J. Read,David Baker +33 more
TL;DR: In this article, a three-track network is proposed to combine information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level.
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Protein complex prediction with AlphaFold-Multimer
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TL;DR: In this article, an AlphaFold model trained specifically for multimeric inputs of known stoichiometry was proposed, which significantly increases the accuracy of predicted multimimeric interfaces over input-adapted single-chain AlphaFolds.
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Journal ArticleDOI
Accurate prediction of protein structures and interactions using a three-track neural network
B. M.,DiMaio F,Anishchenko I,Dauparas J,O. S.,Lee Gr,Wang J,Cong Q,Kinch Ln,Schaeffer Rd,Millan C,Park H,Adams C,Glassman Cr,DeGiovanni A,Pereira Jh,Rodrigues Av,AA van Dijk,Ebrecht Ac,Opperman Dj,Sagmeister T,Buhlheller C,Pavkov-Keller T,Rathinaswamy Mk,Dalwadi U,Yi Ck,Burke Je,G. Kc,Grishina Nv,Adamson Pd,Read Rj,Baker D +31 more
TL;DR: A three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure.
Journal ArticleDOI
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.
Pablo Gainza,Freyr Sverrisson,Federico Monti,Emanuele Rodolà,Davide Boscaini,Michael M. Bronstein,Michael M. Bronstein,Bruno E. Correia +7 more
TL;DR: MaSIF (molecular surface interaction fingerprinting) is presented, a conceptual framework based on a geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions that will lead to improvements in the understanding of protein function and design.
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