dc.contributor.author | Laguillo-Gómez, Andrea | |
dc.contributor.author | Calvo, Enrique | |
dc.contributor.author | Martín-Cófreces, Noa | |
dc.contributor.author | Lozano-Prieto, Marta | |
dc.contributor.author | Sánchez-Madrid, Francisco | |
dc.contributor.author | Vazquez, Jesus | |
dc.date.accessioned | 2023-10-17T10:06:19Z | |
dc.date.available | 2023-10-17T10:06:19Z | |
dc.date.issued | 2023-09-15 | |
dc.identifier.citation | J Proteomics. 2023 Sep 15:287:104968. | es_ES |
dc.identifier.uri | http://hdl.handle.net/20.500.12105/16576 | |
dc.description.abstract | Open-search methods allow unbiased, high-throughput identification of post-translational modifications in proteins at an unprecedented scale. The performance of current open-search algorithms is diminished by experimental errors in the determination of the precursor peptide mass. In this work we propose a semi-supervised open search approach, called ReCom, that minimizes this effect by taking advantage of a priori known information from a reference database, such as Unimod or a database provided by the user. We present a proof-of-concept study using Comet-ReCom, an improved version of Comet-PTM. Comet-ReCom increased identification performance of Comet-PTM by 68%. This increased performance of Comet-ReCom to score the MS/MS spectrum comes in parallel with a significantly better assignation of the monoisotopic peak of the precursor peptide in the MS spectrum, even in cases of peptide coelution. Our data demonstrate that open searches using ultra-tolerant mass windows can benefit from using a semi-supervised approach that takes advantage from previous knowledge on the nature of protein modifications. SIGNIFICANCE: The present study introduces a novel approach to ultra-tolerant database search, which employs prior knowledge of post-translational modifications (PTMs) to improve identification of modified peptides. This method addresses the limitations related to experimental errors and precursor mass assignation of previous open-search methods. Thus, it enables the study of the biological significance of a wider variety of PTMs, including unknown or unexpected modifications that may have gone unnoticed using non-supervised search methods. | es_ES |
dc.description.sponsorship | This study was supported by competitive grants from the Spanish Ministry of Science, Innovation and
Universities (PGC2018-097019-B-I00, PID2021-122348NB-I00, PLEC2022-009235 and PLEC2022-009298),
the Instituto de Salud Carlos III (Fondo de Investigación Sanitaria grant PRB3 (PT17/0019/0003- ISCIIISGEFI / ERDF, ProteoRed), Comunidad de Madrid (IMMUNO-VAR, P2022/BMD-7333) and “la Caixa” Banking Foundation (project codes HR17-00247 and HR22-00253). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovación (MCIN) and the Pro CNIC Foundation),
and is a Severo Ochoa Center of Excellence (grant CEX2020-001041-S funded by
MICIN/AEI/10.13039/501100011033). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.type.hasVersion | VoR | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject.mesh | Tandem Mass Spectrometry | es_ES |
dc.subject.mesh | Peptides | es_ES |
dc.subject.mesh | Amino Acid Sequence | es_ES |
dc.subject.mesh | Proteins | es_ES |
dc.subject.mesh | Algorithms | es_ES |
dc.subject.mesh | Protein Processing, Post-Translational | es_ES |
dc.subject.mesh | Databases, Protein | es_ES |
dc.subject.mesh | Software | es_ES |
dc.title | ReCom: A semi-supervised approach to ultra-tolerant database search for improved identification of modified peptides. | es_ES |
dc.type | journal article | es_ES |
dc.rights.license | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.identifier.pubmedID | 37463622 | es_ES |
dc.format.volume | 287 | es_ES |
dc.format.page | 104968 | es_ES |
dc.identifier.doi | 10.1016/j.jprot.2023.104968 | es_ES |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (España) | es_ES |
dc.contributor.funder | Instituto de Salud Carlos III | es_ES |
dc.contributor.funder | Comunidad de Madrid (España) | es_ES |
dc.contributor.funder | Fundación La Caixa | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación (España) | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación. Centro de Excelencia Severo Ochoa (España) | es_ES |
dc.description.peerreviewed | Sí | es_ES |
dc.identifier.e-issn | 1876-7737 | es_ES |
dc.relation.publisherversion | 10.1016/j.jprot.2023.104968 | es_ES |
dc.identifier.journal | Journal of proteomics | es_ES |
dc.repisalud.orgCNIC | CNIC::Grupos de investigación::Proteómica cardiovascular | es_ES |
dc.repisalud.institucion | CNIC | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/PGC2018-097019-B-I00 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/PID2021-122348NB-I00 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/PLEC2022-009235 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/PLEC2022-009298 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/PT17/0019/0003-ISCIIISGEFI/ERDF | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/IMMUNO-VAR | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/P2022/BMD-7333 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/HR17-00247 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/MICIN/AEI/10.13039/501100011033/CEX2020-001041-S | es_ES |