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Finding Druggable Sites in Proteins using TACTICS

View ORCID ProfileDaniel J. Evans, View ORCID ProfileRemy A. Yovanno, View ORCID ProfileSanim Rahman, David W. Cao, View ORCID ProfileMorgan Q. Beckett, View ORCID ProfileMilan H. Patel, Afif F. Bandak, View ORCID ProfileAlbert Y. Lau
doi: https://doi.org/10.1101/2021.02.21.432120
Daniel J. Evans
1Department of Biophysics and Biophysical Chemistry Johns Hopkins University School of Medicine Baltimore, MD 21205, USA
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Remy A. Yovanno
1Department of Biophysics and Biophysical Chemistry Johns Hopkins University School of Medicine Baltimore, MD 21205, USA
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  • ORCID record for Remy A. Yovanno
Sanim Rahman
1Department of Biophysics and Biophysical Chemistry Johns Hopkins University School of Medicine Baltimore, MD 21205, USA
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David W. Cao
1Department of Biophysics and Biophysical Chemistry Johns Hopkins University School of Medicine Baltimore, MD 21205, USA
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Morgan Q. Beckett
2Department of Biochemistry and Molecular Biology Johns Hopkins Bloomberg School of Public Health Baltimore, MD 21205, USA
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  • ORCID record for Morgan Q. Beckett
Milan H. Patel
1Department of Biophysics and Biophysical Chemistry Johns Hopkins University School of Medicine Baltimore, MD 21205, USA
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  • ORCID record for Milan H. Patel
Afif F. Bandak
1Department of Biophysics and Biophysical Chemistry Johns Hopkins University School of Medicine Baltimore, MD 21205, USA
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Albert Y. Lau
1Department of Biophysics and Biophysical Chemistry Johns Hopkins University School of Medicine Baltimore, MD 21205, USA
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  • ORCID record for Albert Y. Lau
  • For correspondence: alau@jhmi.edu
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Abstract

Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (Trajectory-based Analysis of Conformations To Identify Cryptic Sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://doi.org/10.5281/zenodo.4538911

  • https://github.com/Albert-Lau-Lab/tactics_protein_analysis

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 21, 2021.
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Finding Druggable Sites in Proteins using TACTICS
Daniel J. Evans, Remy A. Yovanno, Sanim Rahman, David W. Cao, Morgan Q. Beckett, Milan H. Patel, Afif F. Bandak, Albert Y. Lau
bioRxiv 2021.02.21.432120; doi: https://doi.org/10.1101/2021.02.21.432120
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Finding Druggable Sites in Proteins using TACTICS
Daniel J. Evans, Remy A. Yovanno, Sanim Rahman, David W. Cao, Morgan Q. Beckett, Milan H. Patel, Afif F. Bandak, Albert Y. Lau
bioRxiv 2021.02.21.432120; doi: https://doi.org/10.1101/2021.02.21.432120

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