pycofitness—Evaluating the fitness landscape of RNA and protein sequences
Abstract Motivation The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue...
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| Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2024-02, Vol.40 (2) |
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| container_title | Bioinformatics (Oxford, England) |
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| creator | Pucci, Fabrizio Zerihun, Mehari B Rooman, Marianne Schug, Alexander |
| description | Abstract
Motivation
The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue.
Results
We present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences. It is based on coevolution and, more specifically, on a popular inverse statistical approach, namely direct coupling analysis by pseudo-likelihood maximization. Its efficient implementation and user-friendly command line interface make it an easy-to-use tool even for researchers with no bioinformatics background. To illustrate its strengths, we present three applications in which pycofitness efficiently predicts the deleteriousness of genetic variants and the effect of mutations on protein fitness and thermodynamic stability.
Availability and implementation
https://github.com/KIT-MBS/pycofitness. |
| doi_str_mv | 10.1093/bioinformatics/btae074 |
| format | Article |
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Motivation
The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue.
Results
We present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences. It is based on coevolution and, more specifically, on a popular inverse statistical approach, namely direct coupling analysis by pseudo-likelihood maximization. Its efficient implementation and user-friendly command line interface make it an easy-to-use tool even for researchers with no bioinformatics background. To illustrate its strengths, we present three applications in which pycofitness efficiently predicts the deleteriousness of genetic variants and the effect of mutations on protein fitness and thermodynamic stability.
Availability and implementation
https://github.com/KIT-MBS/pycofitness.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btae074</identifier><identifier>PMID: 38335928</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Amino Acid Sequence ; Applications Note ; Bioinformatics ; Biological effects ; Coevolution ; Computational Biology ; Gene sequencing ; Genetic diversity ; Genetic variance ; Line interfaces ; Mutagenesis ; Mutation ; Proteins ; Ribonucleic acid ; RNA ; RNA - genetics ; Software</subject><ispartof>Bioinformatics (Oxford, England), 2024-02, Vol.40 (2)</ispartof><rights>The Author(s) 2024. Published by Oxford University Press. 2024</rights><rights>The Author(s) 2024. Published by Oxford University Press.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c485t-79ee13ce34345a98d3414fc6e0308609894ec890bf0735eedb505d4f9a586c3f3</citedby><cites>FETCH-LOGICAL-c485t-79ee13ce34345a98d3414fc6e0308609894ec890bf0735eedb505d4f9a586c3f3</cites><orcidid>0000-0002-0534-502X ; 0000-0003-2916-022X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10881095/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10881095/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,1598,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38335928$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ponty, Yann</contributor><creatorcontrib>Pucci, Fabrizio</creatorcontrib><creatorcontrib>Zerihun, Mehari B</creatorcontrib><creatorcontrib>Rooman, Marianne</creatorcontrib><creatorcontrib>Schug, Alexander</creatorcontrib><title>pycofitness—Evaluating the fitness landscape of RNA and protein sequences</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>Abstract
Motivation
The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue.
Results
We present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences. It is based on coevolution and, more specifically, on a popular inverse statistical approach, namely direct coupling analysis by pseudo-likelihood maximization. Its efficient implementation and user-friendly command line interface make it an easy-to-use tool even for researchers with no bioinformatics background. To illustrate its strengths, we present three applications in which pycofitness efficiently predicts the deleteriousness of genetic variants and the effect of mutations on protein fitness and thermodynamic stability.
Availability and implementation
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Motivation
The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue.
Results
We present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences. It is based on coevolution and, more specifically, on a popular inverse statistical approach, namely direct coupling analysis by pseudo-likelihood maximization. Its efficient implementation and user-friendly command line interface make it an easy-to-use tool even for researchers with no bioinformatics background. To illustrate its strengths, we present three applications in which pycofitness efficiently predicts the deleteriousness of genetic variants and the effect of mutations on protein fitness and thermodynamic stability.
Availability and implementation
https://github.com/KIT-MBS/pycofitness.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>38335928</pmid><doi>10.1093/bioinformatics/btae074</doi><orcidid>https://orcid.org/0000-0002-0534-502X</orcidid><orcidid>https://orcid.org/0000-0003-2916-022X</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Bioinformatics (Oxford, England), 2024-02, Vol.40 (2) |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford Journals Open Access Collection; PubMed Central; Alma/SFX Local Collection |
| subjects | Amino Acid Sequence Applications Note Bioinformatics Biological effects Coevolution Computational Biology Gene sequencing Genetic diversity Genetic variance Line interfaces Mutagenesis Mutation Proteins Ribonucleic acid RNA RNA - genetics Software |
| title | pycofitness—Evaluating the fitness landscape of RNA and protein sequences |
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