Phylogenetic inference of changes in amino acid propensities with single-position resolution
Fitness conferred by the same allele may differ between genotypes and environments, and these differences shape variation and evolution. Changes in amino acid propensities at protein sites over the course of evolution have been inferred from sequence alignments statistically, but the existing method...
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description | Fitness conferred by the same allele may differ between genotypes and environments, and these differences shape variation and evolution. Changes in amino acid propensities at protein sites over the course of evolution have been inferred from sequence alignments statistically, but the existing methods are data-intensive and aggregate multiple sites. Here, we develop an approach to detect individual amino acids that confer different fitness in different groups of species from combined sequence and phylogenetic data. Using the fact that the probability of a substitution to an amino acid depends on its fitness, our method looks for amino acids such that substitutions to them occur more frequently in one group of lineages than in another. We validate our method using simulated evolution of a protein site under different scenarios and show that it has high specificity for a wide range of assumptions regarding the underlying changes in selection, while its sensitivity differs between scenarios. We apply our method to the env gene of two HIV-1 subtypes, A and B, and to the HA gene of two influenza A subtypes, H1 and H3, and show that the inferred fitness changes are consistent with the fitness differences observed in deep mutational scanning experiments. We find that changes in relative fitness of different amino acid variants within a site do not always trigger episodes of positive selection and therefore may not result in an overall increase in the frequency of substitutions, but can still be detected from changes in relative frequencies of different substitutions. |
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Changes in amino acid propensities at protein sites over the course of evolution have been inferred from sequence alignments statistically, but the existing methods are data-intensive and aggregate multiple sites. Here, we develop an approach to detect individual amino acids that confer different fitness in different groups of species from combined sequence and phylogenetic data. Using the fact that the probability of a substitution to an amino acid depends on its fitness, our method looks for amino acids such that substitutions to them occur more frequently in one group of lineages than in another. We validate our method using simulated evolution of a protein site under different scenarios and show that it has high specificity for a wide range of assumptions regarding the underlying changes in selection, while its sensitivity differs between scenarios. We apply our method to the env gene of two HIV-1 subtypes, A and B, and to the HA gene of two influenza A subtypes, H1 and H3, and show that the inferred fitness changes are consistent with the fitness differences observed in deep mutational scanning experiments. We find that changes in relative fitness of different amino acid variants within a site do not always trigger episodes of positive selection and therefore may not result in an overall increase in the frequency of substitutions, but can still be detected from changes in relative frequencies of different substitutions.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1009878</identifier><identifier>PMID: 35180226</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Amino Acid Substitution ; Amino acids ; Amino Acids - genetics ; Biology and Life Sciences ; Computer and Information Sciences ; Env gene ; Env protein ; Evolution ; Evolution, Molecular ; Experiments ; Fitness ; Genotypes ; HIV ; Human immunodeficiency virus ; Humans ; Influenza ; Influenza A ; Influenza, Human - genetics ; Medicine and Health Sciences ; Mutation ; Phylogenetics ; Phylogeny ; Physical Sciences ; Physiological aspects ; Physiological research ; Positive selection ; Proteins ; Reproductive fitness ; Research and Analysis Methods ; Sequence Alignment ; Simulated evolution ; Simulation ; Standard deviation</subject><ispartof>PLoS computational biology, 2022-02, Vol.18 (2), p.e1009878-e1009878</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Klink et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Changes in amino acid propensities at protein sites over the course of evolution have been inferred from sequence alignments statistically, but the existing methods are data-intensive and aggregate multiple sites. Here, we develop an approach to detect individual amino acids that confer different fitness in different groups of species from combined sequence and phylogenetic data. Using the fact that the probability of a substitution to an amino acid depends on its fitness, our method looks for amino acids such that substitutions to them occur more frequently in one group of lineages than in another. We validate our method using simulated evolution of a protein site under different scenarios and show that it has high specificity for a wide range of assumptions regarding the underlying changes in selection, while its sensitivity differs between scenarios. We apply our method to the env gene of two HIV-1 subtypes, A and B, and to the HA gene of two influenza A subtypes, H1 and H3, and show that the inferred fitness changes are consistent with the fitness differences observed in deep mutational scanning experiments. We find that changes in relative fitness of different amino acid variants within a site do not always trigger episodes of positive selection and therefore may not result in an overall increase in the frequency of substitutions, but can still be detected from changes in relative frequencies of different substitutions.</description><subject>Amino Acid Substitution</subject><subject>Amino acids</subject><subject>Amino Acids - genetics</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Env gene</subject><subject>Env protein</subject><subject>Evolution</subject><subject>Evolution, Molecular</subject><subject>Experiments</subject><subject>Fitness</subject><subject>Genotypes</subject><subject>HIV</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Influenza</subject><subject>Influenza A</subject><subject>Influenza, Human - genetics</subject><subject>Medicine and Health Sciences</subject><subject>Mutation</subject><subject>Phylogenetics</subject><subject>Phylogeny</subject><subject>Physical Sciences</subject><subject>Physiological aspects</subject><subject>Physiological research</subject><subject>Positive selection</subject><subject>Proteins</subject><subject>Reproductive fitness</subject><subject>Research and Analysis Methods</subject><subject>Sequence Alignment</subject><subject>Simulated evolution</subject><subject>Simulation</subject><subject>Standard deviation</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqVkktv1DAQxyMEoqXwDRBE4lIOWfxI_LggVVWBlSpAPG5IluNMsl5l7dROgH57HDatuqgX5INHM7_5j2c8WfYcoxWmHL_Z-ik43a8GU9sVRkgKLh5kx7iqaMFpJR7esY-yJzFuEUqmZI-zI1phgQhhx9mPz5vr3nfgYLQmt66FAM5A7tvcbLTrICZnrnfW-Vwb2-RD8AO4aEebQr_suMmjdV0PxeBnp3d5gOj7aTafZo9a3Ud4ttwn2fd3F9_OPxSXn96vz88uC8MYHgvSVBUzgpZVDagkgpcgGKplDUSWTMiW1kgYaBArK9lw0NowyphgnNW8aVt6kr3c6w69j2oZTFSElQgTJDBPxHpPNF5v1RDsTodr5bVVfx0-dEqHNIEelJGES8IIkQ0tEaOy1hWV3AA3LVABSevtUm2qd9AYcGPQ_YHoYcTZjer8TyUxSrIoCZwuAsFfTRBHtbPRQN9rB36a302RxIKzMqGv_kHv726hOp0aSJ_oU10zi6ozJquSYSFoolb3UOk0sLPGO2ht8h8kvD5ISMwIv8dOTzGq9dcv_8F-PGTLPWuCjzFAezs7jNS82zdNqnm31bLbKe3F3bnfJt0sM_0DZ7b1Ag</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Klink, Galya V</creator><creator>Kalinina, Olga V</creator><creator>Bazykin, Georgii A</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-9445-477X</orcidid><orcidid>https://orcid.org/0000-0003-2334-2751</orcidid><orcidid>https://orcid.org/0000-0001-8466-6958</orcidid></search><sort><creationdate>20220201</creationdate><title>Phylogenetic inference of changes in amino acid propensities with single-position resolution</title><author>Klink, Galya V ; Kalinina, Olga V ; Bazykin, Georgii A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c661t-2d556c8345be042874e860b9be294689f3b08ced06459d7eaac63668676b7dff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Amino Acid Substitution</topic><topic>Amino acids</topic><topic>Amino Acids - 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Changes in amino acid propensities at protein sites over the course of evolution have been inferred from sequence alignments statistically, but the existing methods are data-intensive and aggregate multiple sites. Here, we develop an approach to detect individual amino acids that confer different fitness in different groups of species from combined sequence and phylogenetic data. Using the fact that the probability of a substitution to an amino acid depends on its fitness, our method looks for amino acids such that substitutions to them occur more frequently in one group of lineages than in another. We validate our method using simulated evolution of a protein site under different scenarios and show that it has high specificity for a wide range of assumptions regarding the underlying changes in selection, while its sensitivity differs between scenarios. We apply our method to the env gene of two HIV-1 subtypes, A and B, and to the HA gene of two influenza A subtypes, H1 and H3, and show that the inferred fitness changes are consistent with the fitness differences observed in deep mutational scanning experiments. We find that changes in relative fitness of different amino acid variants within a site do not always trigger episodes of positive selection and therefore may not result in an overall increase in the frequency of substitutions, but can still be detected from changes in relative frequencies of different substitutions.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35180226</pmid><doi>10.1371/journal.pcbi.1009878</doi><orcidid>https://orcid.org/0000-0002-9445-477X</orcidid><orcidid>https://orcid.org/0000-0003-2334-2751</orcidid><orcidid>https://orcid.org/0000-0001-8466-6958</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Amino Acid Substitution Amino acids Amino Acids - genetics Biology and Life Sciences Computer and Information Sciences Env gene Env protein Evolution Evolution, Molecular Experiments Fitness Genotypes HIV Human immunodeficiency virus Humans Influenza Influenza A Influenza, Human - genetics Medicine and Health Sciences Mutation Phylogenetics Phylogeny Physical Sciences Physiological aspects Physiological research Positive selection Proteins Reproductive fitness Research and Analysis Methods Sequence Alignment Simulated evolution Simulation Standard deviation |
title | Phylogenetic inference of changes in amino acid propensities with single-position resolution |
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