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dc.contributor.authorMartínez-Martínez, Mónicaes_ES
dc.contributor.authorCoscolín, Cristinaes_ES
dc.contributor.authorSantiago, Gerardes_ES
dc.contributor.authorChow, Jenniferes_ES
dc.contributor.authorStogios, Peter J.es_ES
dc.contributor.authorBargiela, Rafaeles_ES
dc.contributor.authorGertler, Christophes_ES
dc.contributor.authorNavarro-Fernández, Josées_ES
dc.contributor.authorBollinger, Alexanderes_ES
dc.contributor.authorThies, Stephanes_ES
dc.contributor.authorMéndez-García, Celiaes_ES
dc.contributor.authorPopovic, Anaes_ES
dc.contributor.authorBrown, Greges_ES
dc.contributor.authorChernikova, Tatyanaes_ES
dc.contributor.authorGarcía-Moyano, Antonioes_ES
dc.contributor.authorBjerga, Gro E. K.es_ES
dc.contributor.authorPérez-García, Pablo-
dc.contributor.authorHai, Tran-
dc.contributor.authorPozo, Mercedes V. del-
dc.contributor.authorStokke, Runar-
dc.contributor.authorSteen, Ida H.-
dc.contributor.authorCui, Hong-
dc.contributor.authorXu, Xiaohui-
dc.contributor.authorNocek, Boguslaw P.-
dc.contributor.authorAlcaide, María-
dc.contributor.authorDistaso, Marco-
dc.contributor.authorMesa, Victoria-
dc.contributor.authorPeláez, Ana I.-
dc.contributor.authorSánchez, Jesús-
dc.contributor.authorBuchholz, Patrick C. F.-
dc.contributor.authorPleiss, Jürgen-
dc.contributor.authorFernández-Guerra, Antonio-
dc.contributor.authorGlöckner, Frank O.-
dc.contributor.authorGolyshina, Olga V.-
dc.contributor.authorYakimov, Michail M.-
dc.contributor.authorSavchenko, Alexei-
dc.contributor.authorJaeger, Karl-Erich-
dc.contributor.authorYakunin, Alexander F.-
dc.contributor.authorStreit, Wolfgang R.-
dc.contributor.authorGolyshin, Peter N.-
dc.contributor.authorGuallar, Victor-
dc.contributor.authorFerrer, Manuel-
dc.contributor.authorINMARE Consortium-
dc.date.accessioned2018-06-19T09:42:17Z-
dc.date.available2018-06-19T09:42:17Z-
dc.date.issued2017-11-28-
dc.identifier.citationACS Chemical Biology 13(1): 225-234 (2018)es_ES
dc.identifier.issn1554-8929-
dc.identifier.urihttp://hdl.handle.net/10261/166601-
dc.description.abstractEsterases receive special attention because of their wide distribution in biological systems and environments and their importance for physiology and chemical synthesis. The prediction of esterases’ substrate promiscuity level from sequence data and the molecular reasons why certain such enzymes are more promiscuous than others remain to be elucidated. This limits the surveillance of the sequence space for esterases potentially leading to new versatile biocatalysts and new insights into their role in cellular function. Here, we performed an extensive analysis of the substrate spectra of 145 phylogenetically and environmentally diverse microbial esterases, when tested with 96 diverse esters. We determined the primary factors shaping their substrate range by analyzing substrate range patterns in combination with structural analysis and protein–ligand simulations. We found a structural parameter that helps rank (classify) the promiscuity level of esterases from sequence data at 94% accuracy. This parameter, the active site effective volume, exemplifies the topology of the catalytic environment by measuring the active site cavity volume corrected by the relative solvent accessible surface area (SASA) of the catalytic triad. Sequences encoding esterases with active site effective volumes (cavity volume/SASA) above a threshold show greater substrate spectra, which can be further extended in combination with phylogenetic data. This measure provides also a valuable tool for interrogating substrates capable of being converted. This measure, found to be transferred to phosphatases of the haloalkanoic acid dehalogenase superfamily and possibly other enzymatic systems, represents a powerful tool for low-cost bioprospecting for esterases with broad substrate ranges, in large scale sequence data sets.es_ES
dc.description.sponsorshipC.C. thanks the Spanish Ministry of Economy, Industry and Competitiveness for a Ph.D. fellowship (Grant BES-2015-073829). This project received funding from the European Union’s Horizon 2020 research and innovation program [Blue Growth: Unlocking the potential of Seas and Oceans] under grant agreement no. 634486 (project acronym INMARE). This research was also supported by the European Community Projects MAGICPAH (FP7-KBBE-2009-245226), ULIXES (FP7-KBBE-2010-266473), and KILLSPILL (FP7-KBBE2012-312139) and grants BIO2011-25012, PCIN-2014-107, BIO2014-54494-R, and CTQ2016-79138-R from the Spanish Ministry of Economy, Industry and Competitiveness. The present investigation was also funded by the Spanish Ministry of Economy, Industry and Competitiveness within the ERA NET IB2, grant no. ERA-IB-14-030 (MetaCat), the UK Biotechnology and Biological Sciences Research Council (BBSRC), grant no. BB/M029085/1, and the German Research Foundation (FOR1296). R.B. and P.N.G. acknowledge the support of the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via the Welsh Government. O.V.G. and P.N.G. acknowledge the support of the Centre of Environmental Biotechnology Project funded by the European Regional Development Fund (ERDF) through the Welsh Government. A.Y. and A.S. gratefully acknowledge funding from Genome Canada (2009-OGI-ABC-1405) and the NSERC Strategic Network grant IBN. A.I.P. was supported by the Counseling of Economy and Employment of the Principality of Asturias, Spain (Grant FC-15-GRUPIN14-107). V.G. acknowledges the joint BSC-CRG-IRB Research Program in Computational Biology. The authors gratefully acknowledge financial support provided by the European Regional Development Fund (ERDF).es_ES
dc.description.sponsorshipWe acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).es_ES
dc.language.isoenges_ES
dc.publisherAmerican Chemical Societyes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/634486es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/245226es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/266473es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/312139es_ES
dc.relationMINECO/ICTI2013-2016/PCIN-2014-107es_ES
dc.relationMINECO/ICTI2013-2016/BIO2014-54494-Res_ES
dc.relationMINECO/ICTI2013-2016/CTQ2016-79138-Res_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rightsopenAccesses_ES
dc.titleDeterminants and prediction of esterase substrate promiscuity patternses_ES
dc.typeArtículoes_ES
dc.identifier.doi10.1021/acschembio.7b00996-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1021/acschembio.7b00996es_ES
dc.identifier.e-issn1554-8937-
dc.rights.licensehttp://pubs.acs.org/page/policy/authorchoice_termsofuse.htmles_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.contributor.funderEuropean Commissiones_ES
dc.contributor.funderMinisterio de Economía, Industria y Competitividad (España)es_ES
dc.contributor.funderBiotechnology and Biological Sciences Research Council (UK)es_ES
dc.contributor.funderGerman Research Foundationes_ES
dc.contributor.funderNatural Sciences and Engineering Research Council of Canadaes_ES
dc.contributor.funderPrincipado de Asturiases_ES
dc.contributor.funderCSIC - Unidad de Recursos de Información Científica para la Investigación (URICI)-
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000038es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000268es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100001659es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
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