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dc.contributor.author
Huang, Tsun Tsao  
dc.contributor.author
Marcos, María Laura  
dc.contributor.author
Hwang, Jenn Kang  
dc.contributor.author
Echave, Julián  
dc.date.available
2018-01-18T15:04:04Z  
dc.date.issued
2014-04  
dc.identifier.citation
Huang, Tsun Tsao; Marcos, María Laura; Hwang, Jenn Kang; Echave, Julián; A mechanistic stress model of protein evolution accounts for site-specific evolutionary rates and their relationship with packing density and flexibility; BioMed Central; BMC Evolutionary Biology; 14; 78; 4-2014; 1-9  
dc.identifier.issn
1471-2148  
dc.identifier.uri
http://hdl.handle.net/11336/33774  
dc.description.abstract
BACKGROUND: Protein sites evolve at different rates due to functional and biophysical constraints. It is usually considered that the main structural determinant of a site’s rate of evolution is its Relative Solvent Accessibility (RSA). However, a recent comparative study has shown that the main structural determinant is the site’s Local Packing Density (LPD). LPD is related with dynamical flexibility, which has also been shown to correlate with sequence variability. Our purpose is to investigate the mechanism that connects a site’s LPD with its rate of evolution. RESULTS: We consider two models: an empirical Flexibility Model and a mechanistic Stress Model. The Flexibility Model postulates a linear increase of site-specific rate of evolution with dynamical flexibility. The Stress Model, introduced here, models mutations as random perturbations of the protein’s potential energy landscape, for which we use simple Elastic Network Models (ENMs). To account for natural selection we assume a single active conformation and use basic statistical physics to derive a linear relationship between site-specific evolutionary rates and the local stress of the mutant’s active conformation. We compare both models on a large and diverse dataset of enzymes. In a protein-by-protein study we found that the Stress Model outperforms the Flexibility Model for most proteins. Pooling all proteins together we show that the Stress Model is strongly supported by the total weight of evidence. Moreover, it accounts for the observed nonlinear dependence of sequence variability on flexibility. Finally, when mutational stress is controlled for, there is very little remaining correlation between sequence variability and dynamical flexibility. CONCLUSIONS: We developed a mechanistic Stress Model of evolution according to which the rate of evolution of a site is predicted to depend linearly on the local mutational stress of the active conformation. Such local stress is proportional to LPD, so that this model explains the relationship between LPD and evolutionary rate. Moreover, the model also accounts for the nonlinear dependence between evolutionary rate and dynamical flexibility.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
BioMed Central  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
Protein Evolution  
dc.subject
Site-Specific Substitution Rate  
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Local Packing Density  
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Elastic Network Model  
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Flexibility  
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Stress  
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Mean Square Fluctuation  
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Mean Local Mutational Stress  
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Otras Ciencias Químicas  
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Ciencias Químicas  
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CIENCIAS NATURALES Y EXACTAS  
dc.title
A mechanistic stress model of protein evolution accounts for site-specific evolutionary rates and their relationship with packing density and flexibility  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2018-01-16T18:03:06Z  
dc.journal.volume
14  
dc.journal.number
78  
dc.journal.pagination
1-9  
dc.journal.pais
Reino Unido  
dc.journal.ciudad
Londres  
dc.description.fil
Fil: Huang, Tsun Tsao. National Chiao Tung University; República de China  
dc.description.fil
Fil: Marcos, María Laura. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Hwang, Jenn Kang. National Chiao Tung University; República de China  
dc.description.fil
Fil: Echave, Julián. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.journal.title
BMC Evolutionary Biology  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-14-78  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1186/1471-2148-14-78