Multiple Profile Models Extract Features from Protein Sequence Data and Resolve Functional Diversity of Very Different Protein Families.

Autor:	Vicedomini R; CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Sorbonne Université, 4 place Jussieu, 75005 Paris, France.; Institut des Sciences du Calcul et des Données, Sorbonne Université, Paris, France., Bouly JP; CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Sorbonne Université, 4 place Jussieu, 75005 Paris, France.; CNRS, Institut de Biologie Physico-Chimique, Laboratory of Chloroplast Biology and Light Sensing in Microalgae - UMR7141, Sorbonne Université, Paris, France., Laine E; CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Sorbonne Université, 4 place Jussieu, 75005 Paris, France., Falciatore A; CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Sorbonne Université, 4 place Jussieu, 75005 Paris, France.; CNRS, Institut de Biologie Physico-Chimique, Laboratory of Chloroplast Biology and Light Sensing in Microalgae - UMR7141, Sorbonne Université, Paris, France., Carbone A; CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Sorbonne Université, 4 place Jussieu, 75005 Paris, France.; Institut Universitaire de France, Paris 75005, France.
Jazyk:	angličtina
Zdroj:	Molecular biology and evolution [Mol Biol Evol] 2022 Apr 10; Vol. 39 (4).
DOI:	10.1093/molbev/msac070
Abstrakt:	Functional classification of proteins from sequences alone has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of the evolution of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple profile models whose construction explores evolutionary information in available databases, and a novel definition of a representation space in which to analyze sequences with multiple profile models combined together. ProfileView classifies protein families by enriching known functional groups with new sequences and discovering new groups and subgroups. We validate ProfileView on seven classes of widespread proteins involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. ProfileView agrees with the large set of functional data collected for these proteins from the literature regarding the organization into functional subgroups and residues that characterize the functions. In addition, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of novel biological functions. On protein families with complex domain architecture, ProfileView functional classification reconciles domain combinations, unlike phylogenetic reconstruction. ProfileView proves to outperform the functional classification approach PANTHER, the two k-mer-based methods CUPP and eCAMI and a neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter. (© The Author(s) 2022. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)
Databáze:	MEDLINE
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