Deep learning-based advances in protein structure prediction

dc.contributor.authorPakhrin, Subash C.
dc.contributor.authorShrestha, Bikash
dc.contributor.authorAdhikari, Badri
dc.contributor.authorKC, Dukka B.
dc.date.accessioned2021-06-13T17:00:52Z
dc.date.available2021-06-13T17:00:52Z
dc.date.issued2021-05-24
dc.descriptionOpen Accessen_US
dc.description.abstractObtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.en_US
dc.identifier.citationPakhrin, S. C., Shrestha, B., Adhikari, B., & KC, D. B. (2021). Deep learning-based advances in protein structure prediction. International Journal of Molecular Sciences, 22(11) doi:10.3390/ijms22115553en_US
dc.identifier.issn1661-6596
dc.identifier.issn1422-0067
dc.identifier.urihttps://doi.org/10.3390/ijms22115553
dc.identifier.urihttps://soar.wichita.edu/handle/10057/20819
dc.language.isoen_USen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofseriesInternational Journal of Molecular Sciences;Vol. 22, Iss 11
dc.rights.holderCopyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en_US
dc.subjectProtein structure predictionen_US
dc.subjectDeep learningen_US
dc.subjectProtein contact map predictionen_US
dc.subjectProtein distance predictionen_US
dc.subjectProtein quality assessmenten_US
dc.titleDeep learning-based advances in protein structure predictionen_US
dc.typeArticleen_US
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