Abstract: Nearly all FDA approved drugs and bioactive small molecules exert their effects by binding to and modulating proteins. Consequently, understanding how small molecules interact with proteins at an molecular level is a central challenge of modern chemical biology and drug development. Complementary to structure-guided approaches, chemoproteomics has emerged as a method capable of high-throughput identification of proteins covalently bound by small molecules. To profile noncovalent interactions, established chemoproteomic workflows typically incorporate photoreactive moieties into small molecule probes, which enable trapping of small molecule-protein interactions (SMPIs). This strategy, termed photoaffinity labelling (PAL), has been utilized to profile an array of small molecule interactions, including for drugs, lipids, metabolites, and cofactors. Herein we describe the discovery of photocrosslinking chemistries, including a comparison of the strengths and limitations of implementation of each chemotype in chemoproteomic workflows. In addition, we highlight key examples where photoaffinity labelling has enabled target deconvolution and interaction site mapping.

Abstract: Microbial biofilms play a critical role in environmental biotechnology and associated applications. Biofilm production can be enhanced by inhibiting the function of proteins that negatively regulate their formation. With this objective, an in silico approach was adopted to identify competitive inhibitors of eight biofilm-antagonistic proteins, namely AbrB and SinR (from Bacillus subtilis) and AmrZ, PDE (EAL), PslG, RetS, ShrA and TpbA (from Pseudomonas aeruginosa). Fifteen inhibitors that structurally resembled the natural ligand of each protein were shortlisted using ligand-based and structure-based virtual screening. The top four inhibitors obtained from molecular docking using Autodock Vina were further docked using SwissDock and DOCK 6.9 to obtain a consensus hit for each protein based on different scoring functions. Further analysis of the protein-ligand complexes revealed that these top inhibitors formed significant non-covalent interactions with their respective protein binding sites. The eight protein-ligand complexes were then subjected to molecular dynamics simulations for 30 ns using GROMACS. RMSD and radius of gyration values of 0.1-0.4 nm and 1.0-3.5 nm, respectively, along with hydrogen bond formation throughout the trajectory indicated that all the complexes remained stable, compact and intact during the simulation period. Binding energy values between -20 and -77 kJ/mol obtained from MM-PBSA calculations further confirmed the high affinities of the eight inhibitors for their respective receptors. The outcome of this study holds great promise to enhance biofilms that are central to biotechnological processes associated with microbial electrochemical technologies, wastewater treatment, bioremediation and the industrial production of value-added products.

Abstract: Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.

Abstract: As key inflammatory biomarkers C-reactive protein (CRP) and interleukin-6 (IL6) play an important role in the pathogenesis of non-inflammatory diseases, including specific cancers, such as breast cancer (BC). Previous genome-wide association studies (GWASs) have neither explained the large proportion of genetic heritability nor provided comprehensive understanding of the underlying regulatory mechanisms. We adopted an integrative genomic network approach by incorporating our previous GWAS data for CRP and IL6 with multi-omics datasets, such as whole-blood expression quantitative loci, molecular biologic pathways, and gene regulatory networks to capture the full range of genetic functionalities associated with CRP/IL6 and tissue-specific key drivers (KDs) in gene subnetworks. We applied another systematic genomics approach for BC development to detect shared gene sets in enriched subnetworks across BC and CRP/IL6. We detected the topmost significant common pathways across CRP/IL6 (e.g., immune regulatory; chemokines and their receptors; interferon gamma, JAK-STAT, and ERBB4 signaling), several of which overlapped with BC pathways. Further, in gene-gene interaction networks enriched by those topmost pathways, we identified KDs-both well-established (e.g., JAK1/2/3, STAT3) and novel (e.g., CXCR3, CD3D, CD3G, STAT6)-in a tissue-specific manner, for mechanisms shared in regulating CRP/IL6 and BC risk. Our study may provide robust, comprehensive insights into the mechanisms of CRP/IL6 regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for associated disorders, such as BC.

Abstract: BACKGROUND: COVID-2019 pandemic lead to a raised interest on the development of new treatments through Artificial Intelligence (AI). AIM: to carry out a systematic review on the development of repurposed drugs against COVID-2019 through the application of AI. METHODS: The Systematic Reviews and Meta-Analyses (PRISMA) checklist was applied. KEYWORDS: ["Artificial intelligence" and (COVID or SARS) and (medicine or drug)]. Databases: PubMed((R)), DOAJ and SciELO. Cochrane Library was additionally screened to identify previous published reviews on the same topic. RESULTS: From the 277 identified records [PubMed((R)) (n = 157); DOAJ (n = 119) and SciELO (n = 1)], 27 studies were included. Among other, the selected studies on new treatments against COVID-2019 were classified, as follows: studies with in-vitro and/or clinical data; association of known drugs; and other studies related to repurposing of drugs. CONCLUSION: Diverse potentially repurposed drugs against COVID-2019 were identified. The repurposed drugs were mainly from antivirals, antibiotics, anticancer, anti-inflammatory, and Angiotensin-converting enzyme 2 (ACE2) groups, although diverse other pharmacologic groups were covered. AI was a suitable tool to quickly analyze large amounts of data or to estimate drug repurposing against COVID-2019.

Abstract: BACKGROUND: Higher mortality of COVID-19 patients with lung disease is a formidable challenge for the health care system. Genetic association between COVID-19 and various lung disorders must be understood to comprehend the molecular basis of comorbidity and accelerate drug development. METHODS: Lungs tissue-specific neighborhood network of human targets of SARS-CoV-2 was constructed. This network was integrated with lung diseases to build a disease-gene and disease-disease association network. Network-based toolset was used to identify the overlapping disease modules and drug targets. The functional protein modules were identified using community detection algorithms and biological processes, and pathway enrichment analysis. RESULTS: In total, 141 lung diseases were linked to a neighborhood network of SARS-CoV-2 targets, and 59 lung diseases were found to be topologically overlapped with the COVID-19 module. Topological overlap with various lung disorders allows repurposing of drugs used for these disorders to hit the closely associated COVID-19 module. Further analysis showed that functional protein-protein interaction modules in the lungs, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. FDA-approved targets in the hijacked protein modules were identified and that can be hit by exiting drugs to rescue these modules from virus possession. CONCLUSION: Lung diseases are clustered with COVID-19 in the same network vicinity, indicating the potential threat for patients with respiratory diseases after SARS-CoV-2 infection. Pathobiological similarities between lung diseases and COVID-19 and clinical evidence suggest that shared molecular features are the probable reason for comorbidity. Network-based drug repurposing approaches can be applied to improve the clinical conditions of COVID-19 patients.

Abstract: Staphylococcus aureus infection is a leading cause of mortality and morbidity in community, hospital and live-stock sectors, especially with the widespread emergence of methicillin-resistant S. aureus (MRSA) strains. To identify new drug molecules to treat MRSA patients, we have undertaken to search essential proteins that are indispensable for their survival but non-homologous to human host proteins. The current study utilizes a subtractive genome and proteome approach to screen the possible therapeutic targets against S. aureus USA300. Bacterial essential genes are obtained from the DEG database and are compared to avoid cross-reactivity with human host genes. In silico analysis shows 198 proteins that may be considered as therapeutic candidates. Depending on their sub-cellular localization, proteins are grouped as either vaccine or drug targets or both. Extracellular proteins such as cell division proteins (Q2FZ91, Q2FZ95), penicillin-binding proteins (Q2FZ94, Q2FYI0) of the bacterial cell wall, phosphoglucomutase (Q2FE11) and lipoteichoic acid synthase (Q2FIS2) are considered as vaccine targets, and their epitopes have been mapped. Altogether, 53 drug targets are identified, which have shown similarity with the drug targets available in the DrugBank database. Predicted drug targets belong to the common metabolic pathways of MRSA, such as fatty acid biosynthesis, folate biosynthesis, peptidoglycan biosynthesis, ribosome, etc. Protein-protein interaction analysis emphasizing peptidoglycan biosynthesis reveals the connection between penicillin-binding proteins, mur-family proteins and FemXAB proteins. In this study, staphylococcal FemA protein (P0A0A5) is subjected to structure-based virtual screening for the drug repurposing approach. There are 20 residues missing in the crystal structure of FemA, and 12 of these residues are located at the catalytic site. The missing residues are modelled, and stereochemistry is checked. FDA approved drugs available in the DrugBank database have been used in virtual screening with FemA in search of potential repurposed molecules. This approach provides us with 10 drugs that may be used in the treatment of methicillin-resistant staphylococcal mediated diseases. AutoDock 4.2 is used for in silico screening and shows a comparable inhibition constant (Ki) for all 10 FDA-approved drugs towards FemA. Most of these drugs are used in the treatment of various cancers, migraines and leukaemia. Protein-drug interaction analysis shows that the drugs mostly interact with hydrophobic residues of FemA. Moreover, Tyr328 and Lys383 contribute largely to hydrogen bondings during interactions. All interacting amino acids that bind to the drugs are part of the active site cavity of FemA. Supplementary Information: The online version contains supplementary material available at 10.1007/s10989-021-10287-9.

Abstract: Drug repositioning represents a cost- and time-efficient strategy for drug development. Artificial intelligence-based algorithms have been applied in drug repositioning by predicting drug-target interactions in an efficient and high throughput manner. Here, we present a workflow of in silico drug repositioning for host-based antivirals using specially defined targets, a refined list of drug candidates, and an easily implemented computational framework. The workflow described here can also apply to more general purposes, especially when given a user-defined druggable target gene set. For complete details on the use and execution of this protocol, please refer to Li et al. (2021).

Abstract: In the current study, we used 7922 FDA approved small molecule drugs as well as compounds in clinical investigation from NIH's NPC database in our drug repurposing study. SARS-CoV-2 main protease as well as Spike protein/ACE2 targets were used in virtual screening and top-100 compounds from each docking simulations were considered initially in short molecular dynamics (MD) simulations and their average binding energies were calculated by MM/GBSA method. Promising hit compounds selected based on average MM/GBSA scores were then used in long MD simulations. Based on these numerical calculations following compounds were found as hit inhibitors for the SARS-CoV-2 main protease: Pinokalant, terlakiren, ritonavir, cefotiam, telinavir, rotigaptide, and cefpiramide. In addition, following 3 compounds were identified as inhibitors for Spike/ACE2: Denopamine, bometolol, and rotigaptide. In order to verify the predictions of in silico analyses, 4 compounds (ritonavir, rotigaptide, cefotiam, and cefpiramide) for the main protease and 2 compounds (rotigaptide and denopamine) for the Spike/ACE2 interactions were tested by in vitro experiments. While the concentration-dependent inhibition of the ritonavir, rotigaptide, and cefotiam was observed for the main protease; denopamine was effective at the inhibition of Spike/ACE2 binding.

Abstract: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.