dc.contributor.author | Popov, Petr | |
dc.contributor.author | Kalinin, Roman | |
dc.contributor.author | Buslaev, Pavel | |
dc.contributor.author | Kozlovskii, Igor | |
dc.contributor.author | Zaretckii, Mark | |
dc.contributor.author | Karlov, Dmitry | |
dc.contributor.author | Gabibov, Alexander | |
dc.contributor.author | Stepanov, Alexey | |
dc.date.accessioned | 2024-03-14T12:15:28Z | |
dc.date.available | 2024-03-14T12:15:28Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Popov, P., Kalinin, R., Buslaev, P., Kozlovskii, I., Zaretckii, M., Karlov, D., Gabibov, A., & Stepanov, A. (2024). Unraveling viral drug targets : a deep learning-based approach for the identification of potential binding sites. <i>Briefings in Bioinformatics</i>, <i>25</i>(1), Article bbad459. <a href="https://doi.org/10.1093/bib/bbad459" target="_blank">https://doi.org/10.1093/bib/bbad459</a> | |
dc.identifier.other | CONVID_197394052 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/93901 | |
dc.description.abstract | The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promising solution by efficiently reducing the number of candidates. In this study, we propose a structure- and deep learning-based approach that identifies vulnerable regions in viral proteins corresponding to drug binding sites. Our approach takes into account the protein dynamics, accessibility and mutability of the binding site and the putative mechanism of action of the drug. We applied this technique to validate drug targeting toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein S. Our findings reveal a conformation- and oligomer-specific glycan-free binding site proximal to the receptor binding domain. This site comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with candidate drug molecules bound to the potential binding sites indicate an equilibrium shifted toward the inactive conformation compared with drug-free simulations. Small molecules targeting this binding site have the potential to prevent the closed-to-open conformational transition of Spike, thereby allosterically inhibiting its interaction with human angiotensin-converting enzyme 2 receptor. Using a pseudotyped virus-based assay with a SARS-CoV-2 neutralizing antibody, we identified a set of hit compounds that exhibited inhibition at micromolar concentrations. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Oxford University Press (OUP) | |
dc.relation.ispartofseries | Briefings in Bioinformatics | |
dc.rights | CC BY 4.0 | |
dc.subject.other | cryptic binding sites learning | |
dc.subject.other | SARS-CoV-2 | |
dc.subject.other | Spike glycoprotein S | |
dc.title | Unraveling viral drug targets : a deep learning-based approach for the identification of potential binding sites | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-202403142415 | |
dc.contributor.laitos | Kemian laitos | fi |
dc.contributor.laitos | Department of Chemistry | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.relation.issn | 1467-5463 | |
dc.relation.numberinseries | 1 | |
dc.relation.volume | 25 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2023 the Authors | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.grantnumber | 311031 | |
dc.subject.yso | SARS-CoV-2-virus | |
dc.subject.yso | proteiinit | |
dc.subject.yso | lääkkeet | |
dc.subject.yso | lääkehoito | |
dc.subject.yso | koronavirukset | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p38845 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p4332 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p1077 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p10851 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p29062 | |
dc.rights.url | https://creativecommons.org/licenses/by/4.0/ | |
dc.relation.doi | 10.1093/bib/bbad459 | |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Suomen Akatemia | fi |
jyx.fundingprogram | Academy Project, AoF | en |
jyx.fundingprogram | Akatemiahanke, SA | fi |
jyx.fundinginformation | A.G. was supported by Russian Scientific Foundation project No. 17-74-30019; I.K.was supported by Russian Scientific Foundation project No. 22-74-10098; P.B. was supported by the Academy of Finland (Grant 311031) and thank the CSC–IT Center for Science (Espoo, Finland) for computational resources (https://research.csc.fi/-/puhti). | |
dc.type.okm | A1 | |