Briefings in Bioinformatics, volume 25, issue 1

Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites

Petr Popov ^{1, 2}

Roman Kalinin ³

Pavel Buslaev ⁴

Igor Kozlovskii ^{1, 5}

Mark Zaretckii ^{1, 5}

Dmitry Karlov ^{6, 7}

Alexander Gabibov ³

Alexey Stepanov ⁸

Hide authors affiliations Show authors affiliations: 8 affiliations

Tetra-d , Rheinweg 9, Schaffhausen, 8200 , Switzerland |

School of Science , Constructor University Bremen gGmbH, 28759, Bremen , Germany |

M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences , Moscow, 117997 , Russia |

⁴

Nanoscience Center and Department of Chemistry, University of Jyväskylä , 40014, Jyväskylä , Finland |

⁵

School of Science, Constructor University Bremen gGmbH , 28759, Bremen , Germany |

⁶

School of Pharmacy , Medical Biology Centre, , Street, Belfast, BT9 7BL Northern Ireland , U.K |

⁷

Queen’s University Belfast , Medical Biology Centre, , Street, Belfast, BT9 7BL Northern Ireland , U.K |

⁸

Department of Chemistry, The Scripps Research Institute , 10550 North Torrey Pines Road MB-10, La Jolla, 92037, CA , USA |

Publication type: Journal Article

Publication date: 2023-11-22

Oxford University Press

Journal: Briefings in Bioinformatics

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Impact factor: 9.5

ISSN: 14675463, 14774054

DOI: 10.1093/bib/bbad459

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Molecular Biology

Information Systems

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.

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Popov P. et al. Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites // Briefings in Bioinformatics. 2023. Vol. 25. No. 1.

GOST all authors (up to 50) Copy

Popov P., Kalinin R., Buslaev P., Kozlovskii I., Zaretckii M., Karlov D., Gabibov A., Stepanov A. Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites // Briefings in Bioinformatics. 2023. Vol. 25. No. 1.

RIS |

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RIS Copy

TY - JOUR

DO - 10.1093/bib/bbad459

UR - https://doi.org/10.1093/bib/bbad459

TI - Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites

T2 - Briefings in Bioinformatics

AU - Popov, Petr

AU - Kalinin, Roman

AU - Buslaev, Pavel

AU - Kozlovskii, Igor

AU - Zaretckii, Mark

AU - Karlov, Dmitry

AU - Gabibov, Alexander

AU - Stepanov, Alexey

PY - 2023

DA - 2023/11/22 00:00:00

PB - Oxford University Press

IS - 1

VL - 25

SN - 1467-5463

SN - 1477-4054

ER -

BibTex

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BibTex Copy

@article{2023_Popov,

author = {Petr Popov and Roman Kalinin and Pavel Buslaev and Igor Kozlovskii and Mark Zaretckii and Dmitry Karlov and Alexander Gabibov and Alexey Stepanov},

title = {Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites},

journal = {Briefings in Bioinformatics},

year = {2023},

volume = {25},

publisher = {Oxford University Press},

month = {nov},

url = {https://doi.org/10.1093/bib/bbad459},

number = {1},

doi = {10.1093/bib/bbad459}

}

Found error?

Publisher

Oxford University Press

Journal

Briefings in Bioinformatics

Quartile SCImago

Quartile WOS

Impact factor

9.5

ISSN

14675463 (Print)
14774054 (Electronic)