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Mining for Ligandable Cavities in RNA.

ABSTRACT: 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.

SUBMITTER: Xie J

PROVIDER: S-EPMC8201482 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Mining for Ligandable Cavities in RNA.

Xie Jingru J Frank Aaron T AT

ACS medicinal chemistry letters 20210601 6

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 demo ...[more]

PMID: 34141071

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Project description:Rationale: There is poor understanding about protective immunity and the pathogenesis of cavitation in patients with tuberculosis.Objectives: To map pathophysiological pathways at anatomically distinct positions within the human tuberculosis cavity.Methods: Biopsies were obtained from eight predetermined locations within lung cavities of patients with multidrug-resistant tuberculosis undergoing therapeutic surgical resection (n = 14) and healthy lung tissue from control subjects without tuberculosis (n = 10). RNA sequencing, immunohistochemistry, and bacterial load determination were performed at each cavity position. Differentially expressed genes were normalized to control subjects without tuberculosis, and ontologically mapped to identify a spatially compartmentalized pathophysiological map of the cavity. In silico perturbation using a novel distance-dependent dynamical sink model was used to investigate interactions between immune networks and bacterial burden, and to integrate these identified pathways.Measurements and Main Results: The median (range) lung cavity volume on positron emission tomography/computed tomography scans was 50 cm3 (15-389 cm3). RNA sequence reads (31% splice variants) mapped to 19,049 annotated human genes. Multiple proinflammatory pathways were upregulated in the cavity wall, whereas a downregulation "sink" in the central caseum-fluid interface characterized 53% of pathways including neuroendocrine signaling, calcium signaling, triggering receptor expressed on myeloid cells-1, reactive oxygen and nitrogen species production, retinoic acid-mediated apoptosis, and RIG-I-like receptor signaling. The mathematical model demonstrated that neuroendocrine, protein kinase C-θ, and triggering receptor expressed on myeloid cells-1 pathways, and macrophage and neutrophil numbers, had the highest correlation with bacterial burden (r > 0.6), whereas T-helper effector systems did not.Conclusions: These data provide novel insights into host immunity to Mycobacterium tuberculosis-related cavitation. The pathways defined may serve as useful targets for the design of host-directed therapies, and transmission prevention interventions.

Dataset Information

Mining for Ligandable Cavities in RNA.

Publications

Mining for Ligandable Cavities in RNA.

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