Abstract: With numerous infections and fatalities, COVID-19 has wreaked havoc around the globe. The main protease (Mpro), which cleaves the polyprotein to form non-structural proteins, thereby helping in the replication of SARS-CoV-2, appears as an attractive target for antiviral therapeutics. As FDA-approved drugs have shown effectiveness in targeting Mpro in previous SARS-CoV(s), molecular docking and virtual screening of existing antiviral, antimalarial, and protease inhibitor drugs were carried out against SARS-CoV-2 Mpro. Among 53 shortlisted drugs with binding energies lower than that of the crystal-bound inhibitor alpha-ketoamide 13 b (-6.7 kcal/mol), velpatasvir, glecaprevir, grazoprevir, baloxavir marboxil, danoprevir, nelfinavir, and indinavir (-9.1 to -7.5 kcal/mol) were the most significant on the list (hereafter referred to as the 53-list). Molecular dynamics (MD) simulations confirmed the stability of their Mpro complexes, with the MMPBSA binding free energy (DeltaGbind) ranging between -124 kJ/mol (glecaprevir) and -28.2 kJ/mol (velpatasvir). Despite having the lowest initial binding energy, velpatasvir exhibited the highest DeltaGbind value for escaping the catalytic site during the MD simulations, indicating its reduced efficacy, as observed experimentally. Available inhibition assay data adequately substantiated the computational forecast. Glecaprevir and nelfinavir (DeltaGbind = -95.4 kJ/mol) appear to be the most effective antiviral drugs against Mpro. Furthermore, the remaining FDA drugs on the 53-list can be worth considering, since some have already demonstrated antiviral activity against SARS-CoV-2. Hence, theoretical pKi (Ki = inhibitor constant) values for all 53 drugs were provided. Notably, DeltaGbind directly correlates with the average distance of the drugs from the His41-Cys145 catalytic dyad of Mpro, providing a roadmap for rapid screening and improving the inhibitor design against SARS-CoV-2 Mpro.

Abstract: Breast cancer is one of the most severe problems, and it is the primary cause of cancer-related death in females worldwide. The adverse effects and therapeutic resistance development are among the most potent clinical issues for potent medications for breast cancer treatment. The eugenol molecules have a significant affinity for breast cancer receptors. The aim of the study has been on the eugenol compounds, which has potent actions on Eralpha, PR, EGFR, CDK2, mTOR, ERBB2, c-Src, HSP90, and chemokines receptors inhibition. Initially, the drug-likeness property was examined to evaluate the anti-breast cancer activity by applying Lipinski's rule of five on 120 eugenol molecules. Further, structure-based virtual screening was performed via molecular docking, as protein-like interactions play a vital role in drug development. The 3D structure of the receptors has been acquired from the protein data bank and is docked with 87 3D PubChem and ZINC structures of eugenol compounds, and five FDA-approved anti-cancer drugs using AutoDock Vina. Then, the compounds were subjected to three replica molecular dynamic simulations run of 100 ns per system. The results were evaluated using root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein-ligand interactions to indicate protein-ligand complex stability. The results confirm that Eugenol cinnamaldehyde has the best docking score for breast cancer, followed by Aspirin eugenol ester and 4-Allyl-2-methoxyphenyl cinnamate. From the results obtained from in silico studies, we propose that the selected eugenols can be further investigated and evaluated for further lead optimization and drug development.

Abstract: Chronic pain determines a substantial burden on individuals, employers, healthcare systems, and society. Most of the affected patients report dissatisfaction with currently available treatments. There are only a few and poor therapeutic options-some therapeutic agents are an outgrowth of drugs targeting acute pain, while others have several serious side effects. One of the primary degradative enzymes for endocannabinoids, fatty acid amide hydrolase (FAAH) attracted attention as a significant molecular target for developing new therapies for neuropsychiatric and neurological diseases, including chronic pain. Using chemical graph mining, quantitative structure-activity relationship (QSAR) modeling, and molecular docking techniques we developed a multi-step screening protocol to identify repurposable drugs as FAAH inhibitors. After screening the DrugBank database using our protocol, 273 structures were selected, with five already approved drugs, montelukast, repaglinide, revefenacin, raloxifene, and buclizine emerging as the most promising repurposable agents for treating chronic pain. Molecular docking studies indicated that the selected compounds interact with the enzyme mostly non-covalently (except for revefenacin) through shape complementarity to the large substrate-binding pocket in the active site. A molecular dynamics simulation was employed for montelukast and revealed stable interactions with the enzyme. The biological activity of the selected compounds should be further confirmed by employing in vitro and in vivo studies.

Abstract: The pharmacological inhibition of the bacterial collagenases (BC) enzymes is considered a promising strategy to block the virulence of the bacteria without targeting the selection mechanism leading to drug resistance. The chemical structures of the Clostridium perfringens collagenase A (ColA) inhibitors were analyzed using Bemis-Murcko skeletons, Murcko frameworks, the type of plain rings, and docking studies. The inhibitors were classified based on their structural architecture and various scoring methods were implemented to predict the probability of new compounds to inhibit ColA and other BC. The analyses indicated that all compounds contain at least one aromatic ring, which is often a nitrobenzene fragment. 2-Nitrobenzene based compounds are, on average, more potent BC inhibitors compared to those derived from 4-nitrobenzene. The molecular descriptors MDEO-11, AATS0s, ASP-0, and MAXDN were determined as filters to identify new BC inhibitors and highlighted the necessity for a compound to contain at least three primary oxygen atoms. The DrugBank database was virtually screened using the developed methods. A total of 100 compounds were identified as potential BC inhibitors, of which, 10 are human approved drugs. Benzthiazide, entacapone, and lodoxamide were chosen as the best candidates for in vitro testing based on their pharmaco-toxicological profile.

Abstract: The COVID-19 pandemic caused by SARS-CoV-2 is unprecedented in recent memory owing to the non-stop escalation in number of infections and deaths in almost every country of the world. The lack of treatment options further worsens the scenario, thereby necessitating the exploration of already existing US FDA-approved drugs for their effectiveness against COVID-19. In the present study, we have performed virtual screening of nutraceuticals available from DrugBank against 14 SARS-CoV-2 proteins. Molecular docking identified several inhibitors, two of which, rutin and NADH, displayed strong binding affinities and inhibitory potential against SARS-CoV-2 proteins. Further normal model-based simulations were performed to gain insights into the conformational transitions in proteins induced by the drugs. The computational analysis in the present study paves the way for experimental validation and development of multi-target guided inhibitors to fight COVID-19.

Abstract: The emergence of drug-resistant lice, acari, and their associated pathogens (APs) is associated with economic losses; thus, it is essential to find new appropriate therapeutic approaches. In the present study, a subtractive proteomics approach was used to predict suitable therapeutics against these vectors and their infectious agents. We found 9701 proteins in the lice (Pediculus humanus var. corporis) and acari (Ixodes scapularis, Leptotrombidium deliense), and 4822 proteins in the proteomes of their APs (Babesia microti, Borreliella mayonii, Borrelia miyamotoi, Borrelia recurrentis, Rickettsia prowazekii, Orientia tsutsugamushi str. Boryong) that were non-homologous to host proteins. Among these non-homologous proteins, 365 proteins of lice and acari, and 630 proteins of APs, were predicted as essential proteins. Twelve unique essential proteins were predicted to be involved in four unique metabolic pathways of lice and acari, and 103 unique proteins were found to be involved in 75 unique metabolic pathways of APs. The sub cellular localization analysis of 115 unique essential proteins of lice and acari and their APs revealed that 61 proteins were cytoplasmic, 42 as membrane-bound proteins and 12 proteins with multiple localization. The druggability analysis of the identified 73 cytoplasmic and multiple localization essential proteins revealed 22 druggable targets and 51 novel drug targets that participate in unique pathways of lice and acari and their APs. Further, the predicted 42 membrane bound proteins could be potential vaccine candidates. Screening of useful inhibitors against these novel targets may result in finding novel compounds efficient for the control of these parasites.

Abstract: MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA-small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.

Abstract: Persistent infection with high-risk types Human Papillomavirus could cause diseases including cervical cancers and oropharyngeal cancers. Nonetheless, so far there is no effective pharmacotherapy for treating the infection from high-risk HPV types, and hence it remains to be a severe threat to the health of female. Based on drug repositioning strategy, we trained and benchmarked multiple machine learning models so as to predict potential effective antiviral drugs for HPV infection in this work. Through optimizing models, measuring models' predictive performance using 182 pairs of antiviral-target interaction dataset which were all approved by the United States Food and Drug Administration, and benchmarking different models' predictive performance, we identified the optimized Support Vector Machine and K-Nearest Neighbor classifier with high precision score were the best two predictors (0.80 and 0.85 respectively) amongst classifiers of Support Vector Machine, Random forest, Adaboost, Naive Bayes, K-Nearest Neighbors, and Logistic regression classifier. We applied these two predictors together and successfully predicted 57 pairs of antiviral-HPV protein interactions from 864 pairs of antiviral-HPV protein associations. Our work provided good drug candidates for anti-HPV drug discovery. So far as we know, we are the first one to conduct such HPV-oriented computational drug repositioning study.

Abstract: OBJECTIVE: The Angiotensin 1 converting enzyme (ACE1) gene plays a critical role in regulating blood pressure and thus, it has become a major therapeutic target of antihypertensives. Single nucleotide polymorphisms (SNPs) occurring within a gene most especially at the functional segment of the genes alter the structure-function relationship of that gene. RESULTS: Our study revealed that five nsSNPs of the ACE1 gene were found to be potentially deleterious and damaging and they include rs2229839, rs14507892, rs12709442, and rs4977 at point mutations P351R, R953Q, I1018T, F1051V, and T1187M. The protein stability predictive tools revealed that all the nsSNPs decreased stability of the protein and the Consurf server which estimates the evolutionary conservation profile of a protein showed that three mutants were in the highly conserved region. In conclusion, this study predicted potential druggable deleterious mutants that can be further explored to understand the pathological basis of cardiovascular disease.

Abstract: CONTEXT: Baiying Qinghou as a traditional Chinese medicine decoction shows anticancer property on laryngeal squamous cell carcinoma. However, little is known about the precise mechanism of Baiying Qinghou detection against laryngeal squamous cell carcinoma. OBJECTIVE: This study was aimed to explore potential mechanism of therapeutic actions of Baiying Qinghou decoction on laryngeal squamous cell carcinoma. MATERIALS AND METHODS: The active chemical components of Baiying Qinghou decoction were predicted, followed by integrated analysis of network pharmacology and molecular docking approach. The network pharmacology approach included target protein prediction, protein-protein interaction network construction and functional enrichment analysis. RESULTS: Sitosterol and quercetin were predicted to be the overlapped active ingredients among three Chinese herbs of Baiying Qinghou decoction. The target proteins were closely associated with response to chemical, response to drug related biological process and cancer related pathways such as PI3K-Akt signaling, HIF-1 signaling and Estrogen signaling pathway. The target proteins of TP53, EGFR, PTGS2, NOS3 and IL1B as the key nodes in PPI network were cross-validated, among which EGFR, IL1B, NOS3 and TP53 were significantly correlated with the prognosis of patients with laryngeal squamous cell carcinoma. Finally, the binding modes of EGFR, IL1B, NOS3 and TP53 with quercetin were visualized. DISCUSSION AND CONCLUSION: Quercetin of Baiying Qinghou decoction showed therapeutic effect against laryngeal squamous cell carcinoma by regulating TP53, EGFR, NOS3 and IL1B involved with drug resistance and PI3K-AKT signaling pathway. TP53, EGFR, NOS3 and IL1B may be the candidate targets for the treatment of laryngeal squamous cell carcinoma.