Recent applications of deep learning methods on evolutionand contact-based protein structure prediction
DC Field | Value | Language |
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dc.contributor.author | Suh, D. | - |
dc.contributor.author | Lee, J.W. | - |
dc.contributor.author | Choi, S. | - |
dc.contributor.author | Lee, Y. | - |
dc.date.accessioned | 2023-03-08T10:54:33Z | - |
dc.date.available | 2023-03-08T10:54:33Z | - |
dc.date.issued | 2021-06 | - |
dc.identifier.issn | 1661-6596 | - |
dc.identifier.issn | 1422-0067 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/62397 | - |
dc.description.abstract | The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins’ 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug– target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI AG | - |
dc.title | Recent applications of deep learning methods on evolutionand contact-based protein structure prediction | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/ijms22116032 | - |
dc.identifier.bibliographicCitation | International Journal of Molecular Sciences, v.22, no.11 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000660229700001 | - |
dc.identifier.scopusid | 2-s2.0-85106973050 | - |
dc.citation.number | 11 | - |
dc.citation.title | International Journal of Molecular Sciences | - |
dc.citation.volume | 22 | - |
dc.type.docType | Review | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | 3D structure of proteins | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Drug discovery | - |
dc.subject.keywordAuthor | Protein sequence homology | - |
dc.subject.keywordAuthor | Structural bioinformatics | - |
dc.subject.keywordPlus | SECONDARY STRUCTURE PREDICTION | - |
dc.subject.keywordPlus | MULTIPLE SEQUENCE ALIGNMENT | - |
dc.subject.keywordPlus | TEMPLATE-BASED PREDICTION | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordPlus | BACKPROPAGATION | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | GENERATION | - |
dc.subject.keywordPlus | PROFILES | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalWebOfScienceCategory | Biochemistry & Molecular Biology | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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