An official website of the United States government
Cryptic pockets are of growing interest as potential drug targets, particularly to control protein-nucleic acid interactions that often occur via flat surfaces. However, it remains unclear whether cryptic pockets contribute to protein function or if they are merely happenstantial features that can easily be evolved away to achieve drug resistance. Here, we explore whether a cryptic pocket in the Interferon Inhibitory Domain (IID) of viral protein 35 (VP35) of Zaire ebolavirus aids its ability to bind double-stranded RNA (dsRNA). We use simulations and experiments to study the relationship between cryptic pocket opening and dsRNA binding of the IIDs of two other filoviruses, Reston and Marburg. These homologs have nearly identical structures but block different interferon pathways due to different affinities for blunt ends and backbone of the dsRNA. Simulations and thiol-labeling experiments demonstrate that the homologs have varying probabilities of pocket opening. Subsequent dsRNA-binding assays suggest that closed conformations preferentially bind dsRNA blunt ends while open conformations prefer binding the backbone. Point mutations that modulate pocket opening proteins further confirm this preference. These results demonstrate the open cryptic pocket has a function, suggesting cryptic pockets are under selective pressure and may be difficult to evolve away to achieve drug resistance.
more »
« less
This content will become publicly available on May 1, 2026
Computer-Aided Discovery of Natural Compounds Targeting the ADAR2 dsRBD2-RNA Interface and Computational Modeling of Full-Length ADAR2 Protein Structure
Mesothelioma is a rare and aggressive cancer linked to asbestos exposure and characterized by rapid metastasis and poor prognosis. Inhibition of adenosine deaminase acting on dsRNA 2 (ADAR2) RNA binding but not ADAR2 editing has shown antitumor effects in mesothelioma. Natural compounds from the Traditional Chinese Medicine (TCM) database were docked to the RNA-binding interface of ADAR2’s second dsRNA binding domain (dsRBD2), and their drug-likeness and predicted safety were assessed. Eight ligands (ZINC000085597263, ZINC000085633079, ZINC000014649947, ZINC000034512861, ZINC000070454124, ZINC000085594944, ZINC000085633008, and ZINC000095909822) showed high binding affinity to dsRBD2 from molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) calculations. Protein–ligand interactions were analyzed to identify key residues contributing to these binding affinities. Molecular dynamics (MD) simulations of dsRBD–ligand–RNA complexes revealed that four compounds (ZINC000085597263, ZINC000085633079, ZINC000014649947, and ZINC000034512861) had negative binding affinities to dsRBD2 in the presence of the RNA substrate GluR-2. Key residues, including Val164, Met165, Lys209, and Lys212, were crucial for ligand binding, even with RNA present, suggesting these compounds could inhibit dsRBD2’s RNA-binding function. The predicted biological activities of these compounds indicate potential anticancer properties, particularly for the treatment of mesothelioma. These compounds are structurally similar to known anti-mesothelioma agents or anticancer drugs, highlighting their therapeutic potential. Current mesothelioma treatments are limited. Optimization of these compounds, alone or in combination with current therapeutics, has potential for mesothelioma treatment. Additionally, five high-quality full-length ADAR2 models were developed. These models provide insights into ADAR2 function, mutation impacts, and potential areas for protein engineering to enhance stability, RNA-binding specificity, or protein interactions, particularly concerning dimerization or complex formation with other proteins and RNAs.
more »
« less
- Award ID(s):
- 2216567
- PAR ID:
- 10636624
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- International Journal of Molecular Sciences
- Volume:
- 26
- Issue:
- 9
- ISSN:
- 1422-0067
- Page Range / eLocation ID:
- 4075
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Monkeypox (now Mpox), a zoonotic disease caused by the monkeypox virus (MPXV) is an emerging threat to global health. In the time span of only six months, from May to October 2022, the number of MPXV cases breached 80,000 and many of the outbreaks occurred in locations that had never previously reported MPXV. Currently there are no FDA-approved MPXV-specific vaccines or treatments, therefore, finding drugs to combat MPXV is of utmost importance. The A42R profilin-like protein of the MPXV is involved in cell development and motility making it a critical drug target. A42R protein is highly conserved across orthopoxviruses, thus A42R inhibitors may work for other family members. This study sought to identify potential A42R inhibitors for MPXV treatment using computational approaches. The energy minimized 3D structure of the A42R profilin-like protein (PDB ID: 4QWO) underwent virtual screening using a library of 36,366 compounds from Traditional Chinese Medicine (TCM), AfroDb, and PubChem databases as well as known inhibitor tecovirimat via AutoDock Vina. A total of seven compounds comprising PubChem CID: 11371962, ZINC000000899909, ZINC000001632866, ZINC000015151344, ZINC000013378519, ZINC000000086470, and ZINC000095486204, predicted to have favorable binding were shortlisted. Molecular docking suggested that all seven proposed compounds have higher binding affinities to A42R (–7.2 to –8.3 kcal/mol) than tecovirimat (–6.7 kcal/mol). This was corroborated by MM/PBSA calculations, with tecovirimat demonstrating the highest binding free energy of –68.694 kJ/mol (lowest binding affinity) compared to the seven shortlisted compounds that ranged from –73.252 to –97.140 kJ/mol. Furthermore, the 7 compounds in complex with A42R demonstrated higher stability than the A42R-tecovirimat complex when subjected to 100 ns molecular dynamics simulations. The protein-ligand interaction maps generated using LigPlot+ suggested that residues Met1, Glu3, Trp4, Ile7, Arg127, Val128, Thr131, and Asn133 are important for binding. These seven compounds were adequately profiled to be potential antivirals via PASS predictions and structural similarity searches. All seven potential lead compounds were scored Pa > Pi for antiviral activity while ZINC000001632866 and ZINC000015151344 were predicted as poxvirus inhibitors with Pa values of 0.315 and 0.215, and Pi values of 0.052 and 0.136, respectively. Further experimental validations of the identified lead compounds are required to corroborate their predicted activity. These seven identified compounds represent solid footing for development of antivirals against MPXV and other orthopoxviruses.more » « less
-
Abstract RNA‐protein interactions play essential roles in regulating gene expression. While some RNA‐protein interactions are “specific”, that is, the RNA‐binding proteins preferentially bind to particular RNA sequence or structural motifs, others are “non‐RNA specific.” Deciphering the protein‐RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein‐RNA interfaces, there is a need for computational methods to identify RNA‐binding residues in proteins. While most of the existing computational methods for predicting RNA‐binding residues in RNA‐binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner‐specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner‐specific protein‐RNA interface prediction tools, PS‐PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA‐specificity metric (RSM), for quantifying the RNA‐specificity of the RNA binding residues predicted by such tools. Our results show that the RNA‐binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner‐agnostic metrics, RNA partner‐specific methods are outperformed by the state‐of‐the‐art partner‐agnostic methods. We conjecture that either (a) the protein‐RNA complexes in PDB are not representative of the protein‐RNA interactions in nature, or (b) the current methods for partner‐specific prediction of RNA‐binding residues in proteins fail to account for the differences in RNA partner‐specific versus partner‐agnostic protein‐RNA interactions, or both.more » « less
-
Abstract In this study, the binding of multimodal chromatographic ligands to the IgG1 FCdomain were studied using nuclear magnetic resonance and molecular dynamics simulations. Nuclear magnetic resonance experiments carried out with chromatographic ligands and a perdeuterated15N‐labeled FCdomain indicated that while single‐mode ion exchange ligands interacted very weakly throughout the FCsurface, multimodal ligands containing negatively charged and aromatic moieties interacted with specific clusters of residues with relatively high affinity, forming distinct binding regions on the FC. The multimodal ligand‐binding sites on the FCwere concentrated in the hinge region and near the interface of the CH2 and CH3 domains. Furthermore, the multimodal binding sites were primarily composed of positively charged, polar, and aliphatic residues in these regions, with histidine residues exhibiting some of the strongest binding affinities with the multimodal ligand. Interestingly, comparison of protein surface property data with ligand interaction sites indicated that the patch analysis on FCcorroborated molecular‐level binding information obtained from the nuclear magnetic resonance experiments. Finally, molecular dynamics simulation results were shown to be qualitatively consistent with the nuclear magnetic resonance results and to provide further insights into the binding mechanisms. An important contribution to multimodal ligand‐FCbinding in these preferred regions was shown to be electrostatic interactions and π–π stacking of surface‐exposed histidines with the ligands. This combined biophysical and simulation approach has provided a deeper molecular‐level understanding of multimodal ligand–FCinteractions and sets the stage for future analyses of even more complex biotherapeutics.more » « less
-
Abstract Dozens of impactful methods that predict intrinsically disordered regions (IDRs) in protein sequences that interact with proteins and/or nucleic acids were developed. Their training and assessment rely on the IDR‐level binding annotations, while the equivalent structure‐trained methods predict more granular annotations of binding amino acids (AA). We compiled a new benchmark dataset that annotates binding AA in IDRs and applied it to complete a first‐of‐its‐kind assessment of predictions of the disordered binding residues. We evaluated a representative collection of 14 methods, used several hundred low‐similarity test proteins, and focused on the challenging task of differentiating these binding residues from other disordered AA and considering ligand type‐specific predictions (protein–protein vs. protein–nucleic acid interactions). We found that current methods struggle to accurately predict binding IDRs among disordered residues; however, better‐than‐random tools predict disordered binding residues significantly better than binding IDRs. We identified at least one relatively accurate tool for predicting disordered protein‐binding and disordered nucleic acid‐binding AA. Analysis of cross‐predictions between interactions with protein and nucleic acids revealed that most methods are ligand‐type‐agnostic. Only two predictors of the nucleic acid‐binding IDRs and two predictors of the protein‐binding IDRs can be considered as ligand‐type‐specific. We also discussed several potential future directions that would move this field forward by producing more accurate methods that target the prediction of binding residues, reduce cross‐predictions, and cover a broader range of ligand types.more » « less
