scispace - formally typeset
Open AccessJournal ArticleDOI

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
More filters
Posted ContentDOI

Protein complex prediction with AlphaFold-Multimer

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.
Posted Content

Rethinking Attention with Performers

TL;DR: Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear space and time complexity, without relying on any priors such as sparsity or low-rankness are introduced.
Journal ArticleDOI

Accurate prediction of protein structures and interactions using a three-track neural network

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.

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.
References
More filters
Journal ArticleDOI

Basic Local Alignment Search Tool

TL;DR: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score.
Journal ArticleDOI

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Journal ArticleDOI

Database resources of the National Center for Biotechnology Information

TL;DR: In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI’s website.
Journal ArticleDOI

STRING v10: protein–protein interaction networks, integrated over the tree of life

TL;DR: H hierarchical and self-consistent orthology annotations are introduced for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution in the STRING database.
Related Papers (5)