Structure-Based High-Throughput Virtual Screening and Molecular Dynamics Simulation for the Discovery of Novel SARS-CoV-2 NSP3 Mac1 Domain Inhibitors.

Since the emergence of SARS-CoV-2, many genetic variations within its genome have been identified, but only a few mutations have been found in nonstructural proteins (NSPs). Among this class of viral proteins, NSP3 is a multidomain protein with 16 different domains, and its largest domain is known as the macrodomain or Mac1 domain. In this study, we present a virtual screening campaign in which we computationally evaluated the NCI anticancer library against the NSP3 Mac1 domain, using Molegro Virtual Docker. The top hits with the best MolDock and Re-Rank scores were selected. The physicochemical analysis and drug-like potential of the top hits were analyzed using the SwissADME data server. The binding stability and affinity of the top NSC compounds against the NSP3 Mac1 domain were analyzed using molecular dynamics (MD) simulation, using Desmond software, and their interaction energies were analyzed using the MM/GBSA method. In particular, by applying subsequent computational filters, we identified 10 compounds as possible NSP3 Mac1 domain inhibitors. Among them, after the assessment of binding energies (ΔGbind) on the whole MD trajectories, we identified the four most interesting compounds that acted as strong binders of the NSP3 Mac1 domain (NSC-358078, NSC-287067, NSC-123472, and NSC-142843), and, remarkably, it could be further characterized for developing innovative antivirals against SARS-CoV-2.

Integrative bioinformatics analysis of ACS enzymes as candidate prognostic and diagnostic biomarkers in colon adenocarcinoma.

Background and purpose: Acyl-CoA synthetase (ACS) enzymes play an important role in the activation of fatty acids. While many studies have found correlations between the expression levels of ACS enzymes with the progression, growth, and survival of cancer cells, their role and expression patterns in colon adenocarcinoma are still greatly unknown and demand further investigation. Experimental approach: The expression data of colon adenocarcinoma samples were downloaded from the Cancer Genome Atlas (TCGA) database. Normalization and differential expression analysis were performed to identify differentially expressed genes (DEGs). Gene set enrichment analysis was applied to identify top enriched genes from ACS enzymes in cancer samples. Gene ontology and protein-protein interaction analyses were performed for the prediction of molecular functions and interactions. Survival analysis and receiver operating characteristic test (ROC) were performed to find potential prognostic and diagnostic biomarkers. Findings/Results: ACSL6 and ACSM5 genes demonstrated more significant differential expression and LogFC value compared to other ACS enzymes and also achieved the highest enrichment scores. Gene ontology analysis predicted the involvement of top DEGs in fatty acids metabolism, while protein-protein interaction network analysis presented strong interactions between ACSLs, ACSSs, ACSMs, and ACSBG enzymes with each other. Survival analysis suggested ACSM3 and ACSM5 as potential prognostic biomarkers, while the ROC test predicted stronger diagnostic potential for ACSM5, ACSS2, and ACSF2 genes. Conclusion and implications: Our findings revealed the expression patterns, prognostic, and diagnostic biomarker potential of ACS enzymes in colon adenocarcinoma. ACSM3, ACSM5, ACSS2, and ACSF2 genes are suggested as possible prognostic and diagnostic biomarkers.

Discovery of novel direct small-molecule inhibitors targeting HIF-2α using structure-based virtual screening, molecular dynamics simulation, and MM-GBSA calculations.

Hypoxia-inducible factors (HIFs) are the main regulatory factors implicated in the adaptation of cancer cells to hypoxic stress, which has provoked much interest as an attractive target for the design of promising chemotherapeutic agents. Since indirect HIF inhibitors (HIFIs) lead to the occurrence of various side effects, the need of the hour is to develop direct HIFIs, physically interacting with important functional domains within the HIF protein structure. Accordingly, in the present study, it was attempted to develop an exhaustive structure-based virtual screening (VS) process coupled with molecular docking, molecular dynamic (MD) simulation, and MM-GBSA calculations for the identification of novel direct inhibitors against the HIF-2α subunit. For this purpose, a focused library of over 200,000 compounds from the NCI database was used for VS against the PAS-B domain of the target protein, HIF-2α. This domain was suggested to be a possible ligand-binding site, which is characterized by a large internal hydrophobic cavity, unique to the HIF-2α subunit. The top-ranked compounds, NSC106416, NSC217021, NSC217026, NSC215639, and NSC277811 with the best docking scores were taken up for the subsequent in silico ADME properties and PAINS filtration. The selected drug-like hits were employed for carrying out MD simulation which was followed by MM-GBSA calculations to retrieve the candidates showing the highest in silico binding affinity towards the PAS-B domain of HIF-2α. The analysis of results indicated that all molecules, except the NSC277811, fulfilled necessary drug-likeness properties. Four selected drug-like candidates, NSC106416, NSC217021, NSC217026, and NSC215639 were found to expose the stability profiles within the cavity located inside the PAS-B domain of HIF-2α over simulation time. Finally, the results of the MM-GBSA rescoring method were indicative of the highest binding affinity of NSC217026 for the binding site of the HIF-2α PAS-B domain among selected final hits. Consequently, the hit NSC217026 could serve as a promising scaffold for further optimization toward the design of direct HIF-2α inhibitors for cancer therapy.

Expression analysis of HIF-3α as a potent prognostic biomarker in various types of human cancers: a case of meta-analysis.

Background and purpose: Hypoxia-inducible factors (HIFs) are transcription factors that get activated and stabilized in the heterodimerized form under hypoxic conditions. many studies have reported the importance of the HIF-1α and HIF-2α activity in biological pathways of hypoxic cancer cells. However, the importance of HIF-3α in a variety of cancers remains unknown. Experimental approach: The expression profile of 13 different types of cancer samples from the Cancer Genome Atlas (TCGA) database were subjected to normalization, and differential gene expression analysis was performed using computational algorithms by R programming. Receiver operating characteristic tests and survival analyses were carried out for HIF-α subunits in different cancers. Findings / Results: The expression status of HIF-3α was notably less in all cancer samples in contrast to their adjacent normal tissues. The expression degree of HIF-1α varied among distinct types of cancer and the expression degree of HIF-2α was lower in nearly all types of cancers. HIF-3α had very weak diagnostic potential, while HIF-2α had better diagnostic potential in most types of cancers compared to HIF-1α. Patients who had a higher level of HIF-3α had better survival, while the higher expression level of HIF-1α and HIF-2α were associated with worse survival in many types of cancers. Conclusion and implications: Our findings showed that each HIF-α subunit had a unique heterogeneous expression pattern in different classes of cancers. The expression level of each HIF-α subunit correlated differently with the stages, tumor sizes, and survival rate of patients from different classes of TCGA cancers.