Abstract: Mycobacterium avium complex (MAC) is a major cause of non-tuberculous pulmonary and disseminated diseases worldwide, inducing bronchiectasis, and affects HIV and immunocompromised patients. In MAC, Mycobacterium avium subsp. hominissuis is a pathogen that infects humans and mammals, and that is why it is a focus of this study. It is crucial to find essential drug targets to eradicate the infections caused by these virulent microorganisms. The application of bioinformatics and proteomics has made a significant impact on discovering unique drug targets against the deadly pathogens. One successful bioinformatics methodology is the use of in silico subtractive genomics. In this study, the aim was to identify the unique, non-host and essential protein-based drug targets of Mycobacterium avium subsp. hominissuis via in silico a subtractive genomics approach. Therefore, an in silico subtractive genomics approach was applied in which complete proteome is subtracted systematically to shortlist potential drug targets. For this, the complete dataset of proteins of Mycobacterium avium subsp. hominissuis was retrieved. The applied subtractive genomics method, which involves the homology search between the host and the pathogen to subtract the non-druggable proteins, resulted in the identification of a few prioritized potential drug targets against the three strains of M. avium subsp. Hominissuis, i.e., MAH-TH135, OCU466 and A5. In conclusion, the current study resulted in the prioritization of vital drug targets, which opens future avenues to perform structural as well as biochemical studies on predicted drug targets against M. avium subsp. hominissuis.

Abstract: Leishmaniasis is endemic in at least 98 countries. Due to the high toxicity and resistance associated with the drugs, we chose lignans as an alternative, due to their favorable properties of absorption, distribution, metabolism, excretion, and toxicity (ADMET). To investigate their leishmanicidal potential, the biological activities of a set of 160 lignans were predicted using predictive models that were built using data for Leishmania major and L. (Viannia) braziliensis. A combined analysis, based on ligand and structure, and several other computational approaches were used. The results showed that the combined analysis was able to select 11 lignans with potential activity against L. major and 21 lignans against L. braziliensis, with multitargeting effects and low or no toxicity. Of these compounds, four were isolated from the species Justicia aequilabris (Nees) Lindau. All of the identified compounds were able to inhibit the growth of L. braziliensis promastigotes, with the most active compound, (159) epipinoresinol-4-O-beta-d-glucopyranoside, presenting an IC50 value of 5.39 microM and IC50 value of 36.51 microM for L. major. Our findings indicated the potential of computer-aided drug design and development and demonstrated that lignans represent promising prototype compounds for the development of multitarget drugs against leishmaniasis.

Abstract: Psychiatric and neurological disorders (PNDs) affect millions worldwide and only a few drugs achieve complete therapeutic success in the treatment of these disorders. Due to the high cost of developing novel drugs, drug repositioning represents a promising alternative method of treatment. In this manuscript, we used a network medicine approach to investigate the molecular characteristics of PNDs and identify novel drug candidates for repositioning. Using IBM Watson for Drug Discovery, a powerful machine learning text-mining application, we built knowledge networks containing connections between PNDs and genes or drugs mentioned in the scientific literature published in the past 50 years. This approach revealed several drugs that target key PND-related genes, which have never been used to treat these disorders to date. We validate our framework by detecting drugs that have been undergoing clinical trial for treating some of the PNDs, but have no published results in their support. Our data provides comprehensive insights into the molecular pathology of PNDs and offers promising drug repositioning candidates for follow-up trials.

Abstract: Sepsis is a life-threatening complication of pneumonia, including coronavirus disease-2019 (COVID-19)-induced pneumonia. Evidence of the benefits of vitamin C (VC) for the treatment of sepsis is accumulating. However, data revealing the targets and molecular mechanisms of VC action against sepsis are limited. In this report, a bioinformatics analysis of network pharmacology was conducted to demonstrate screening targets, biological functions, and the signaling pathways of VC action against sepsis. As shown in network assays, 63 primary causal targets for the VC action against sepsis were identified from the data, and four optimal core targets for the VC action against sepsis were identified. These core targets were epidermal growth factor receptor (EGFR), mitogen-activated protein kinase-1 (MAPK1), proto-oncogene c (JUN), and signal transducer and activator of transcription-3 (STAT3). In addition, all biological processes (including a top 20) and signaling pathways (including a top 20) potentially involved in the VC action against sepsis were identified. The hub genes potentially involved in the VC action against sepsis and interlaced networks from the Kyoto Encyclopedia of Genes and Genomes Mapper assays were highlighted. Considering all the bioinformatic findings, we conclude that VC antisepsis effects are mechanistically and pharmacologically implicated with suppression of immune dysfunction-related and inflammation-associated functional processes and other signaling pathways. These primary predictive biotargets may potentially be used to treat sepsis in future clinical practice.

Abstract: Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol(-1) (vs DFT) for BDEs of unseen molecules. We further demonstrate the model on two applications: first, we rapidly and accurately predict major sites of hydrogen abstraction in the metabolism of drug-like molecules, and second, we determine the dominant molecular fragmentation pathways during soot formation.

Abstract: The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causing the COVID-19 respiratory disease pandemic utilizes unique 2'-O-methyltransferase (2'-O-MTase) capping machinery to camouflage its RNA from innate immune recognition. The nsp16 catalytic subunit of the 2'-O-MTase is unusual in its requirement for a stimulatory subunit (nsp10) to catalyze the ribose 2'-O-methylation of the viral RNA cap. Here we provide a computational basis for drug repositioning or de novo drug development based on three differential traits of the intermolecular interactions of the SARS-CoV-2-specific nsp16/nsp10 heterodimer, namely: (1) the S-adenosyl-l-methionine-binding pocket of nsp16, (2) the unique "activating surface" between nsp16 and nsp10, and (3) the RNA-binding groove of nsp16. We employed approximately 9000 U.S. Food and Drug Administration (FDA)-approved investigational and experimental drugs from the DrugBank repository for docking virtual screening. After molecular dynamics calculations of the stability of the binding modes of high-scoring nsp16/nsp10-drug complexes, we considered their pharmacological overlapping with functional modules of the virus-host interactome that is relevant to the viral lifecycle, and to the clinical features of COVID-19. Some of the predicted drugs (e.g., tegobuvir, sonidegib, siramesine, antrafenine, bemcentinib, itacitinib, or phthalocyanine) might be suitable for repurposing to pharmacologically reactivate innate immune restriction and antagonism of SARS-CoV-2 RNAs lacking 2'-O-methylation.

Abstract: The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) caused by the novel coronavirus SARS-CoV-2 a pandemic. There is, however, no confirmed anti-COVID-19 therapeutic currently. In order to assist structure-based discovery efforts for repurposing drugs against this disease, we constructed knowledge-based models of SARS-CoV-2 proteins and compared the ligand molecules in the template structures with approved/experimental drugs and components of natural medicines. Our theoretical models suggest several drugs, such as carfilzomib, sinefungin, tecadenoson, and trabodenoson, that could be further investigated for their potential for treating COVID-19.

Abstract: BACKGROUND: The microbiome has been shown to affect the response to Immune Checkpoint Inhibitors (ICIs) in a small number of cancers and in preclinical models. Here, we sought to broadly survey cancers to identify those in which the microbiome may play a prognostic role using retrospective analyses of patients with advanced cancer treated with ICIs. METHODS: We conducted a retrospective analysis of 690 patients who received ICI therapy for advanced cancer. We used a literature review to define a causal model for the relationship between medications, the microbiome, and ICI response to guide the abstraction of electronic health records. Medications with precedent for changes to the microbiome included antibiotics, corticosteroids, proton pump inhibitors, histamine receptor blockers, non-steroid anti-inflammatories and statins. We tested the effect of medication timing on overall survival (OS) and evaluated the robustness of medication effects in each cancer. Finally, we compared the size of the effect observed for different classes of antibiotics to taxa that have been correlated to ICI response using a literature review of culture-based antibiotic susceptibilities. RESULTS: Of the medications assessed, only antibiotics and corticosteroids significantly associated with shorter OS. The hazard ratios (HRs) for antibiotics and corticosteroids were highest near the start of ICI treatment but remained significant when given prior to ICI. Antibiotics and corticosteroids remained significantly associated with OS even when controlling for multiple factors such as Eastern Cooperative Oncology Group performance status, Charlson Comorbidity Index score, and stage. When grouping antibiotics by class, beta-lactams showed the strongest association with OS across all tested cancers. CONCLUSIONS: The timing and strength of the correlations with antibiotics and corticosteroids after controlling for confounding factors are consistent with the microbiome involvement with the response to ICIs across several cancers.

Abstract: Tuning reactivity of sulfur electrophiles is key for advancing click chemistry and chemical probe discovery. To date, activation of the sulfur electrophile for protein modification has been ascribed principally to stabilization of a fluoride leaving group (LG) in covalent reactions of sulfonyl fluorides and arylfluorosulfates. We recently introduced sulfur-triazole exchange (SuTEx) chemistry to demonstrate the triazole as an effective LG for activating nucleophilic substitution reactions on tyrosine sites of proteins. Here, we probed tunability of SuTEx for fragment-based ligand discovery by modifying the adduct group (AG) and LG with functional groups of differing electron-donating and -withdrawing properties. We discovered the sulfur electrophile is highly sensitive to the position of modification (AG versus LG), which enabled both coarse and fine adjustments in solution and proteome activity. We applied these reactivity principles to identify a large fraction of tyrosine sites ( approximately 30%) on proteins ( approximately 44%) that can be liganded across >1500 probe-modified sites quantified by chemical proteomics. Our proteomic studies identified noncatalytic tyrosine and phosphotyrosine sites that can be liganded by SuTEx fragments with site specificity in lysates and live cells to disrupt protein function. Collectively, we describe SuTEx as a versatile covalent chemistry with broad applications for chemical proteomics and protein ligand discovery.

Abstract: Coronavirus disease strain (SARS-CoV-2) was discovered in 2019, and it is spreading very fast around the world causing the disease Covid-19. Currently, more than 1.6 million individuals are infected, and several thousand are dead across the globe because of Covid-19. Here, we utilized the in-silico approaches to identify possible protease inhibitors against SARS-CoV-2. Potential compounds were screened from the CHEMBL database, ZINC database, FDA approved drugs and molecules under clinical trials. Our study is based on 6Y2F and 6W63 co-crystallized structures available in the protein data bank (PDB). Seven hundred compounds from ZINC/CHEMBL databases and fourteen hundred compounds from drug-bank were selected based on positive interactions with the reported binding site. All the selected compounds were subjected to standard-precision (SP) and extra-precision (XP) mode of docking. Generated docked poses were carefully visualized for known interactions within the binding site. Molecular mechanics-generalized born surface area (MM-GBSA) calculations were performed to screen the best compounds based on docking scores and binding energy values. Molecular dynamics (MD) simulations were carried out on four selected compounds from the CHEMBL database to validate the stability and interactions. MD simulations were also performed on the PDB structure 6YF2F to understand the differences between screened molecules and co-crystallized ligand. We screened 300 potential compounds from various databases, and 66 potential compounds from FDA approved drugs. Cobicistat, ritonavir, lopinavir, and darunavir are in the top screened molecules from FDA approved drugs. The screened drugs and molecules may be helpful in fighting with SARS-CoV-2 after further studies.