Finding Druggable Sites in Proteins Using TACTICS-Reference-Cited by-同舟云学术

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Finding Druggable Sites in Proteins Using TACTICS

Published:2021-06-07 Issue:6 Volume:61 Page:2897-2910
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Evans Daniel J.¹^ORCID,Yovanno Remy A.¹,Rahman Sanim¹,Cao David W.¹,Beckett Morgan Q.²,Patel Milan H.¹,Bandak Afif F.¹,Lau Albert Y.¹^ORCID

Affiliation:

1. Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States

2. Department of Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.1c00204

Reference85 articles.

1. Cryptic binding sites on proteins: definition, detection, and druggability

2. Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations

3. Exploring the structural origins of cryptic sites on proteins

4. Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites

5. CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SARS-CoV-2 3CLPro Dihedral Angles Reveal Allosteric Signaling;2024-05-22

2. SAPocket: Finding pockets on protein surfaces with a focus towards position and voxel channels;Expert Systems with Applications;2023-10

3. Construction of Histone–Protein Complex Structures by Peptide Growing;International Journal of Molecular Sciences;2023-09-07

4. Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods;Journal of Chemical Information and Modeling;2023-08-21

5. Structure-based neural network protein–carbohydrate interaction predictions at the residue level;Frontiers in Bioinformatics;2023-06-20

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