Exploring the HIV-1 Rev Recognition Element (RRE)-Rev Inhibitory Capacity and Antiretroviral Action of Benfluron Analogs.

Human immunodeficiency virus-type 1 (HIV-1) remains one of the leading contributors to the global burden of disease, and novel antiretroviral agents with alternative mechanisms are needed to cure this infection. Here, we describe an exploratory attempt to optimize the antiretroviral properties of benfluron, a cytostatic agent previously reported to exhibit strong anti-HIV activity likely based on inhibitory actions on virus transcription and Rev-mediated viral RNA export. After obtaining six analogs designed to modify the benzo[c]fluorenone system of the parent molecule, we examined their antiretroviral and toxicity properties together with their capacity to recognize the Rev Recognition Element (RRE) of the virus RNA and inhibit the RRE-Rev interaction. The results indicated that both the benzo[c] and cyclopentanone components of benfluron are required for strong RRE-Rev target engagement and antiretroviral activity and revealed the relative impact of these moieties on RRE affinity, RRE-Rev inhibition, antiviral action and cellular toxicity. These data provide insights into the biological properties of the benzo[c]fluorenone scaffold and contribute to facilitating the design of new anti-HIV agents based on the inhibition of Rev function.

Corrigendum to "Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2″ [Computational and Structural Biotechnology Journal 19 (2021) 424-438].

[This corrects the article DOI: 10.1016/j.csbj.2020.12.028.].

Rational Development of Bacterial Ureases Inhibitors.

Urease, an enzyme that catalyzes the hydrolysis of urea, is a virulence factor of various pathogenic bacteria. In particular, Helicobacter pylori, that colonizes the digestive tract and Proteus spp., that can infect the urinary tract, are related to urease activity. Therefore, urease inhibitors are considered as potential therapeutics against these infections. This review describes current knowledge of the structures, activity, and biological importance of bacterial ureases. Moreover, the structure-based design of several classes of bacterial urease inhibitors is presented and discussed. Phosphinic and phosphonic acids were applied as transition-state analogues, while Michael acceptors and ebselen derivatives were applied as covalent binders of cysteine residue. This review incorporates bacterial urease inhibitors from literature published between 2008 and 2021.

Discovery and characterization of small molecule SIRT3-specific inhibitors as revealed by mass spectrometry.

Sirtuins play a prominent role in several cellular processes and are implicated in various diseases. The understanding of biological roles of sirtuins is limited because of the non-availability of small molecule inhibitors, particularly the specific inhibitors directed against a particular SIRT. We performed a high-throughput screening of pharmacologically active compounds to discover novel, specific, and selective sirtuin inhibitor. Several unique in vitro sirtuin inhibitor pharmacophores were discovered. Here, we present the discovery of novel chemical scaffolds specific for SIRT3. We have demonstrated the in vitro activity of these compounds using label-free mass spectroscopy. We have further validated our results using biochemical, biophysical, and computational studies. Determination of kinetic parameters shows that the SIRT3 specific inhibitors have a moderately longer residence time, possibly implying high in vivo efficacy. The molecular docking results revealed the differential selectivity pattern of these inhibitors against sirtuins. The discovery of specific inhibitors will improve the understanding of ligand selectivity in sirtuins, and the binding mechanism as revealed by docking studies can be further exploited for discovering selective and potent ligands targeting sirtuins.

Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2.

The current life-threatening and tenacious pandemic eruption of coronavirus disease in 2019 (COVID-19) has posed a significant global hazard concerning high mortality rate, economic meltdown, and everyday life distress. The rapid spread of COVID-19 demands countermeasures to combat this deadly virus. Currently, there are no drugs approved by the FDA to treat COVID-19. Therefore, discovering small molecule therapeutics for treating COVID-19 infection is essential. So far, only a few small molecule inhibitors are reported for coronaviruses. There is a need to expand the small chemical space of coronaviruses inhibitors by adding potent and selective scaffolds with anti-COVID activity. In this context, the huge antiviral chemical space already available can be analysed using cheminformatic and machine learning to unearth new scaffolds. We created three specific datasets called "antiviral dataset" (N = 38,428) "drug-like antiviral dataset" (N = 20,963) and "anticorona dataset" (N = 433) for this purpose. We analyzed the 433 molecules of "anticorona dataset" for their scaffold diversity, physicochemical distributions, principal component analysis, activity cliffs, R-group decomposition, and scaffold mapping. The scaffold diversity of the "anticorona dataset" in terms of Murcko scaffold analysis demonstrates a thorough representation of diverse chemical scaffolds. However, physicochemical descriptor analysis and principal component analysis demonstrated negligible drug-like features for the "anticorona dataset" molecules. The "antiviral dataset" and "drug-like antiviral dataset" showed low scaffold diversity as measured by the Gini coefficient. The hierarchical clustering of the "antiviral dataset" against the "anticorona dataset" demonstrated little molecular similarity. We generated a library of frequent fragments and polypharmacological ligands targeting various essential viral proteins such as main protease, helicase, papain-like protease, and replicase polyprotein 1ab. Further structural and chemical features of the "anticorona dataset" were compared with SARS-CoV-2 repurposed drugs, FDA-approved drugs, natural products, and drugs currently in clinical trials. Using machine learning tool DCA (DMax Chemistry Assistant), we converted the "anticorona dataset" into an elegant hypothesis with significant functional biological relevance. Machine learning analysis uncovered that FDA approved drugs, Tizanidine HCl, Cefazolin, Raltegravir, Azilsartan, Acalabrutinib, Luliconazole, Sitagliptin, Meloxicam (Mobic), Succinyl sulfathiazole, Fluconazole, and Pranlukast could be repurposed as effective drugs for COVID-19. Fragment-based scaffold analysis and R-group decomposition uncovered pyrrolidine and the indole molecular scaffolds as the potent fragments for designing and synthesizing the novel drug-like molecules for targeting SARS-CoV-2. This comprehensive and systematic assessment of small-molecule viral therapeutics' entire chemical space realised critical insights to potentially privileged scaffolds that could aid in enrichment and rapid discovery of efficacious antiviral drugs for COVID-19.

Epigenetic drug discovery: systematic assessment of chemical space.

Aim: The druggability of epigenetic targets has prompted researchers to develop small-molecule therapeutics. However, no systematic assessment has ever been done to investigate the chemical space of epigenetic modulators. Herein, we report a comprehensive chemoinformatic analysis of epigenetic ligands from EpiDBase, HEMD, ChEMBL and PubChem databases. Results: Nearly, 0.45 × 106 ligands were analyzed for assay interference compounds, target profiling, drug-like properties and hit prioritization. After eliminating approximately 96,000 problematic compounds, the remaining 0.36 × 106 compounds were studied for their physicochemical distributions, principal component analysis and hit prioritization. More than 30% of assay interference compounds were determined for many proteins. Conclusion: This systematic assessment of epigenetic ligands will help in the enrichment of screening libraries with high-quality compounds and thus, the generation of efficacious drug candidates.

EpiDBase: a manually curated database for small molecule modulators of epigenetic landscape.

We have developed EpiDBase (www.epidbase.org), an interactive database of small molecule ligands of epigenetic protein families by bringing together experimental, structural and chemoinformatic data in one place. Currently, EpiDBase encompasses 5784 unique ligands (11 422 entries) of various epigenetic markers such as writers, erasers and readers. The EpiDBase includes experimental IC(50) values, ligand molecular weight, hydrogen bond donor and acceptor count, XlogP, number of rotatable bonds, number of aromatic rings, InChIKey, two-dimensional and three-dimensional (3D) chemical structures. A catalog of all epidbase ligands based on the molecular weight is also provided. A structure editor is provided for 3D visualization of ligands. EpiDBase is integrated with tools like text search, disease-specific search, advanced search, substructure, and similarity analysis. Advanced analysis can be performed using substructure and OpenBabel-based chemical similarity fingerprints. The EpiDBase is curated to identify unique molecular scaffolds. Initially, molecules were selected by removing peptides, macrocycles and other complex structures and then processed for conformational sampling by generating 3D conformers. Subsequent filtering through Zinc Is Not Commercial (ZINC: a free database of commercially available compounds for virtual screening) and Lilly MedChem regular rules retained many distinctive drug-like molecules. These molecules were then analyzed for physicochemical properties using OpenBabel descriptors and clustered using various methods such as hierarchical clustering, binning partition and multidimensional scaling. EpiDBase provides comprehensive resources for further design, development and refinement of small molecule modulators of epigenetic markers.

Structure, dynamics, and interaction of Mycobacterium tuberculosis (Mtb) DprE1 and DprE2 examined by molecular modeling, simulation, and electrostatic studies.

The enzymes decaprenylphosphoryl-ß-D-ribose oxidase (DprE1) and decaprenylphosphoryl-ß-D-ribose-2-epimerase (DprE2) catalyze epimerization of decaprenylphosporyl ribose (DPR) todecaprenylphosporyl arabinose (DPA) and are critical for the survival of Mtb. Crystal structures of DprE1 so far reported display significant disordered regions and no structural information is known for DprE2. We used homology modeling, protein threading, molecular docking and dynamics studies to investigate the structural and dynamic features of Mtb DprE1 and DprE2 and DprE1-DprE2 complex. A three-dimensional model for DprE2 was generated using the threading approach coupled with ab initio modeling. A 50 ns simulation of DprE1 and DprE2 revealed the overall stability of the structures. Principal Component Analysis (PCA) demonstrated the convergence of sampling in both DprE1 and DprE2. In DprE1, residues in the 269-330 area showed considerable fluctuation in agreement with the regions of disorder observed in the reported crystal structures. In DprE2, large fluctuations were detected in residues 95-113, 146-157, and 197-226. The study combined docking and MD simulation studies to map and characterize the key residues involved in DprE1-DprE2 interaction. A 60 ns MD simulation for DprE1-DprE2 complex was also performed. Analysis of data revealed that the docked complex is stabilized by H-bonding, hydrophobic and ionic interactions. The key residues of DprE1 involved in DprE1-DprE2 interactions belong to the disordered region. We also examined the docked complex of DprE1-BTZ043 to investigate the binding pocket of DprE1 and its interactions with the inhibitor BTZ043. In summary, we hypothesize that DprE1-DprE2 interaction is crucial for the synthesis of DPA and DprE1-DprE2 complex may be a new therapeutic target amenable to pharmacological validation. The findings have important implications in tuberculosis (TB) drug discovery and will facilitate drug development efforts against TB.

Discovery of Mycobacterium tuberculosis α-1,4-glucan branching enzyme (GlgB) inhibitors by structure- and ligand-based virtual screening.

GlgB (α-1,4-glucan branching enzyme) is the key enzyme involved in the biosynthesis of α-glucan, which plays a significant role in the virulence and pathogenesis of Mycobacterium tuberculosis. Because α-glucans are implicated in the survival of both replicating and non-replicating bacteria, there exists an exigent need for the identification and development of novel inhibitors for targeting enzymes, such as GlgB, involved in this pathway. We have used the existing structural information of M. tuberculosis GlgB for high throughput virtual screening and molecular docking. A diverse database of 330,000 molecules was used for identifying novel and efficacious therapeutic agents for targeting GlgB. We also used three-dimensional shape as well as two-dimensional similarity matrix methods to identify diverse molecular scaffolds that inhibit M. tuberculosis GlgB activity. Virtual hits were generated after structure and ligand-based screening followed by filters based on interaction with human GlgB and in silico pharmacokinetic parameters. These hits were experimentally evaluated and resulted in the discovery of a number of structurally diverse chemical scaffolds that target M. tuberculosis GlgB. Although a number of inhibitors demonstrated in vitro enzyme inhibition, two compounds in particular showed excellent inhibition of in vivo M. tuberculosis survival and its ability to get phagocytosed. This work shows that in silico docking and three-dimensional chemical similarity could be an important therapeutic approach for developing inhibitors to specifically target the M. tuberculosis GlgB enzyme.

SMMRNA: a database of small molecule modulators of RNA.

We have developed SMMRNA, an interactive database, available at http://www.smmrna.org, with special focus on small molecule ligands targeting RNA. Currently, SMMRNA consists of ∼770 unique ligands along with structural images of RNA molecules. Each ligand in the SMMRNA contains information such as Kd, Ki, IC50, ΔTm, molecular weight (MW), hydrogen donor and acceptor count, XlogP, number of rotatable bonds, number of aromatic rings and 2D and 3D structures. These parameters can be explored using text search, advanced search, substructure and similarity-based analysis tools that are embedded in SMMRNA. A structure editor is provided for 3D visualization of ligands. Advance analysis can be performed using substructure and OpenBabel-based chemical similarity fingerprints. Upload facility for both RNA and ligands is also provided. The physicochemical properties of the ligands were further examined using OpenBabel descriptors, hierarchical clustering, binning partition and multidimensional scaling. We have also generated a 3D conformation database of ligands to support the structure and ligand-based screening. SMMRNA provides comprehensive resource for further design, development and refinement of small molecule modulators for selective targeting of RNA molecules.