Utilize este identificador para referenciar este registo:
https://hdl.handle.net/10316/108631
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Melo, Rita | - |
dc.contributor.author | Fieldhouse, Robert | - |
dc.contributor.author | Melo, André | - |
dc.contributor.author | Correia, João D. G. | - |
dc.contributor.author | Cordeiro, Maria Natália D. S. | - |
dc.contributor.author | Gümüş, Zeynep H. | - |
dc.contributor.author | Costa, Joaquim | - |
dc.contributor.author | Bonvin, Alexandre M. J. J. | - |
dc.contributor.author | Moreira, Irina S. | - |
dc.date.accessioned | 2023-09-06T08:35:48Z | - |
dc.date.available | 2023-09-06T08:35:48Z | - |
dc.date.issued | 2016-07-27 | - |
dc.identifier.issn | 1422-0067 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/108631 | - |
dc.description.abstract | Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set. | pt |
dc.description.sponsorship | Marie Skłodowska-Curie Individual Fellowship MSCA-IF-2015 (MEMBRANEPROT 659826); FCT Investigator program—IF/00578/2014 and Center for Basic and Translational Research on Disorders of the Digestive System, Rockefeller University, through the generosity of the Leona M. and Harry B. Helmsley Charitable Trust and start-up funds of the Icahn School of Medicine at Mount Sinai. | pt |
dc.language.iso | eng | pt |
dc.publisher | MDPI | pt |
dc.relation | SFRH/BPD/97650/2013 | pt |
dc.relation | UID/Multi/04349/2013 | pt |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID/NEU/04539/2013/PT | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | protein-protein interfaces | pt |
dc.subject | hot-spots | pt |
dc.subject | machine learning | pt |
dc.subject | Solvent Accessible Surface Area (SASA) | pt |
dc.subject | evolutionary sequence conservation | pt |
dc.subject.mesh | Algorithms | pt |
dc.subject.mesh | Computational Biology | pt |
dc.subject.mesh | Databases, Protein | pt |
dc.subject.mesh | Humans | pt |
dc.subject.mesh | Protein Conformation | pt |
dc.subject.mesh | Protein Interaction Domains and Motifs | pt |
dc.subject.mesh | Protein Interaction Mapping | pt |
dc.subject.mesh | Proteins | pt |
dc.subject.mesh | Machine Learning | pt |
dc.title | A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces | pt |
dc.type | article | - |
degois.publication.firstPage | 1215 | pt |
degois.publication.issue | 8 | pt |
degois.publication.title | International Journal of Molecular Sciences | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/ijms17081215 | pt |
degois.publication.volume | 17 | pt |
dc.date.embargo | 2016-07-27 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.project.grantno | Centre for Nuclear Sciences and Technologies | - |
crisitem.project.grantno | CNC. IBILI | - |
crisitem.author.orcid | 0000-0003-2970-5250 | - |
Aparece nas coleções: | I&D CNC - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
A-machine-learning-approach-for-hotspot-detection-at-proteinprotein-interfacesInternational-Journal-of-Molecular-Sciences.pdf | 527.34 kB | Adobe PDF | Ver/Abrir |
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