Open AccessMining for Ligandable Cavities in RNA
Author(s) -
Jingru Xie,
Aaron T. Frank
Publication year - 2021
Publication title -
acs medicinal chemistry letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.065
H-Index - 66
ISSN - 1948-5875
DOI - 10.1021/acsmedchemlett.1c00068
Subject(s) - rna , virtual screening , decoy , computational biology , transactivation , nucleic acid structure , computer science , drug discovery , artificial intelligence , bioinformatics , chemistry , biology , biochemistry , transcription factor , gene , receptor
Identifying potential ligand binding cavities is a critical step in structure-based screening of biomolecular targets. Cavity mapping methods can detect such binding cavities; however, for ribonucleic acid (RNA) targets, determining which of the detected cavities are "ligandable" remains an unsolved challenge. In this study, we trained a set of machine learning classifiers to distinguish ligandable RNA cavities from decoy cavities. Application of our classifiers to two independent test sets demonstrated that we could recover ligandable cavities from decoys with an AUC > 0.83. Interestingly, when we applied our classifiers to a library of modeled structures of the HIV-1 transactivation response (TAR) element RNA, we found that several of the conformers that harbored cavities with high ligandability scores resembled known holo-TAR structures. On the basis of our results, we envision that our classifiers could find utility as a tool to parse RNA structures and prospectively mine for ligandable binding cavities and, in so doing, facilitate structure-based virtual screening efforts against RNA drug targets.