Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal.

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development. Availability & Implementation: The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

Cytokines inhibitory mechanism of Prunus domestica L. (Plum) peptides as potential immunomodulators against systemic lupus erythematosus: an in-silico screening.

Natural bioactive peptides exhibit various chemical and structural properties to enhance the immune response against multiple inflammatory and autoimmune related disorders. The immunomodulatory function and bioactivity of seed peptides show the capability for the development of biotherapeutics that could prevent autoimmune diseases. The aim of current study is to determine the immunomodulatory function of bioactive peptides derived from the seed of plum (Prunus domestica L.) by applying various immunoinformatic approaches. A thorough analysis of forty-one peptides was performed including drug likeliness, pharmacokinetic, and bioactivity profiling studies. Further, molecular docking and molecular dynamics (MD) simulations of screened peptides were carried out with the two interleukin targets (IL-17A and IL-23) of systemic lupus erythematosus (SLE). After the systematic screening, four peptides, namely HLLP, LPLL, LPAGV, and NLPL, were found as potential inhibitors against SLE. Additionally, site-directed mutagenesis analysis was conducted to explore the role of essential amino acid residues in the binding pattern/energy change. Computational alanine screening analysis found that CYS123, CYS121 of IL-17A and ASP270, and SER249 of IL-23 as hot spot residues that could play an important role in the inhibition property of screened peptides. Overall, the methodology described in the study can be utilized for developing unique peptide inhibitors that have a preventative role against SLE. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-023-00188-8.

Galaxy for open-source computational drug discovery solutions.

INTRODUCTION: Drug discovery in academia and industry poses contrasting challenges. While academia focuses on producing new knowledge, industry is keen on product development and success in clinical trials. Galaxy is a web-based open-source computational workbench which is used to analyze large datasets and is customized to integrate analysis and visualization tools in a single framework. Depending on the methodology, one can generate customized and suitable workflows in the Galaxy platform. AREAS COVERED: Herein, the authors appraise the suitability of the Galaxy platform for developing a disease specific web portal called the Molecular Property Diagnostic Suite (MPDS). The authors include their future perspectives in the expert opinion section. EXPERT OPINION: Galaxy is ideally suited for community-based software development as the scripts, tools, and codes developed in the different programming languages can be integrated in an extremely efficient fashion. MPDS puts forth a new approach known as a disease-specific web portal which aims to implement a range of computational methods and algorithms that can be developed and shared freely across the community of computer aided drug design (CADD) scientists.

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite.

A fragment-based drug discovery (FBDD) approach has traditionally been of utmost significance in drug design studies. It allows the exploration of large chemical space to find novel scaffolds and chemotypes which can be improved into selective inhibitors with good affinity. In the current work, several public domain chemical libraries (ChEMBL, DrugCentral, PDB ligands, COCONUT, and SAVI) comprising bioactive and virtual molecules were retrieved to develop a fragment library. A systematic fragmentation method that breaks a given molecule into rings, linkers, and substituents was used to cleave the molecules and the fragments were analyzed. Further, only the ring framework was taken into the consideration to develop a fragment library that consists of a total number of 107,614 unique fragments. This set represents a rich diverse structure framework that covers a wide variety of yet-to-be-explored fragments for a wide range of small molecule-based applications. This fragment library is an integral part of the molecular property diagnostic suite (MPDS) suite that can be used with other modeling and informatics methods for FBDD approaches. The fragment library module of MPDS can be accessed at http://mpds.neist.res.in:8085 .

Computational screening for investigating the synergistic regulatory potential of drugs and phytochemicals in combination with 2-deoxy-D-glucose against SARS-CoV-2.

COVID-19 disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was declared a global pandemic by the World Health Organization (WHO) in March 2020. Since then, the SARS-CoV-2 virus has impacted millions of lives worldwide. Various preclinical and clinical trials on the treatment of COVID-19 disease have revealed that the drugs that work in combination are more likely to reduce reinfection and multi-organ failure. Considering the combination drug therapy, herein, we performed a systematic computational study starting with the formation of sixty-two combinations of drugs and phytochemicals with 2-deoxy-D-glucose (2-DG). The top nineteen combinations resulting from Drug-Drug Interaction (DDI) analysis were selected for individual and multiple-ligand-simultaneous docking (MLSD) study with a host target Serine Protease (TMPRSS2; PDB ID: 7MEQ) and two viral targets, Main Protease (3CLpro; PDB ID: 6LU7) and Uridylate-Specific Endoribonuclease (NSP15; PDB ID: 6VWW). We found that the resulting drugs and phytochemicals in combination with 2-DG shows better binding than the individual compounds. We performed the re-docking of the top three drug combinations by utilizing the polypharmacology approach to validate the binding patterns of drug combinations with multiple targets for verifying the best drug combinatorial output obtained by blind docking. A strong binding affinity pattern was observed for 2-DG + Ruxolitinib (NIH-recommended drug), 2-DG + Telmisartan (phase 4 clinical trial drug), and 2-DG + Punicalagin (phytochemical) for all the selected targets. Additionally, we conducted multiple-ligand-simultaneous molecular dynamics (MLS-MD) simulations on the selected targets with the 2-DG + Ruxolitinib combination. The MLS-MD analysis of the drug combinations shows that stabilization of the interaction complexes could have significant inhibition potential against SARS CoV-2. This study provides an insight into developing drug combinations utilizing integrated computational approaches to uncover their potential in synergistic drug therapy. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02049-0.

Applying polypharmacology approach for drug repurposing for SARS-CoV2.

Exploring the new therapeutic indications of known drugs for treating COVID-19, popularly known as drug repurposing, is emerging as a pragmatic approach especially owing to the mounting pressure to control the pandemic. Targeting multiple targets with a single drug by employing drug repurposing known as the polypharmacology approach may be an optimised strategy for the development of effective therapeutics. In this study, virtual screening has been carried out on seven popular SARS-CoV-2 targets (3CLpro, PLpro, RdRp (NSP12), NSP13, NSP14, NSP15, and NSP16). A total of 4015 approved drugs were screened against these targets. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected based on the docking score, ability to interact with four or more targets and having a reasonably good number of interactions with key residues in the targets. The MD simulations and MM-PBSA studies showed reasonable stability of protein-drug complexes and sustainability of key interactions between the drugs with their respective targets throughout the course of MD simulations. The identified four drug molecules were also compared with the known drugs namely elbasvir and nafamostat. While the study has provided a detailed account of the chosen protein-drug complexes, it has explored the nature of seven important targets of SARS-CoV-2 by evaluating the protein-drug complexation process in great detail. Graphical abstract: Drug repurposing strategy against SARS-CoV2 drug targets. Computational analysis was performed to identify repurposable approved drug candidates against SARS-CoV2 using approaches such as virtual screening, molecular dynamics simulation and MM-PBSA calculations. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected as potential candidates. Supplementary Information: The online version contains supplementary material available at 10.1007/s12039-022-02046-0.

Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing.

Machine learning and data-driven approaches are currently being widely used in drug discovery and development due to their potential advantages in decision-making based on the data leveraged from existing sources. Applying these approaches to drug repurposing (DR) studies can identify new relationships between drug molecules, therapeutic targets and diseases that will eventually help in generating new insights for developing novel therapeutics. In the current study, a dataset of 1671 approved drugs is analyzed using a combined approach involving unsupervised Machine Learning (ML) techniques (Principal Component Analysis (PCA) followed by k-means clustering) and Structure-Activity Relationships (SAR) predictions for DR. PCA is applied on all the two dimensional (2D) molecular descriptors of the dataset and the first five Principal Components (PC) were subsequently used to cluster the drugs into nine well separated clusters using k-means algorithm. We further predicted the biological activities for the drug-dataset using the PASS (Predicted Activities Spectra of Substances) tool. These predicted activity values are analyzed systematically to identify repurposable drugs for various diseases. Clustering patterns obtained from k-means showed that every cluster contains subgroups of structurally similar drugs that may or may not have similar therapeutic indications. We hypothesized that such structurally similar but therapeutically different drugs can be repurposed for the native indications of other drugs of the same cluster based on their high predicted biological activities obtained from PASS analysis. In line with this, we identified 66 drugs from the nine clusters which are structurally similar but have different therapeutic uses and can therefore be repurposed for one or more native indications of other drugs of the same cluster. Some of these drugs not only share a common substructure but also bind to the same target and may have a similar mechanism of action, further supporting our hypothesis. Furthermore, based on the analysis of predicted biological activities, we identified 1423 drugs that can be repurposed for 366 new indications against several diseases. In this study, an integrated approach of unsupervised ML and SAR analysis have been used to identify new indications for approved drugs and the study provides novel insights into clustering patterns generated through descriptor level analysis of approved drugs.

Notch-Jagged signaling complex defined by an interaction mosaic.

The Notch signaling system links cellular fate to that of its neighbors, driving proliferation, apoptosis, and cell differentiation in metazoans, whereas dysfunction leads to debilitating developmental disorders and cancers. Other than a five-by-five domain complex, it is unclear how the 40 extracellular domains of the Notch1 receptor collectively engage the 19 domains of its canonical ligand, Jagged1, to activate Notch1 signaling. Here, using cross-linking mass spectrometry (XL-MS), biophysical, and structural techniques on the full extracellular complex and targeted sites, we identify five distinct regions, two on Notch1 and three on Jagged1, that form an interaction network. The Notch1 membrane-proximal regulatory region individually binds to the established Notch1 epidermal growth factor (EGF) 8-EGF13 and Jagged1 C2-EGF3 activation sites as well as to two additional Jagged1 regions, EGF8-EGF11 and cysteine-rich domain. XL-MS and quantitative interaction experiments show that the three Notch1-binding sites on Jagged1 also engage intramolecularly. These interactions, together with Notch1 and Jagged1 ectodomain dimensions and flexibility, determined by small-angle X-ray scattering, support the formation of nonlinear architectures. Combined, the data suggest that critical Notch1 and Jagged1 regions are not distal but engage directly to control Notch1 signaling, thereby redefining the Notch1-Jagged1 activation mechanism and indicating routes for therapeutic applications.

ß-Lactamase of Mycobacterium tuberculosis Shows Dynamics in the Active Site That Increase upon Inhibitor Binding.

The Mycobacterium tuberculosis ß-lactamase BlaC is a broad-spectrum ß-lactamase that can convert a range of ß-lactam antibiotics. Enzymes with low specificity are expected to exhibit active-site flexibility. To probe the motions in BlaC, we studied the dynamic behavior in solution using nuclear magnetic resonance (NMR) spectroscopy. 15N relaxation experiments show that BlaC is mostly rigid on the pico- to nanosecond timescale. Saturation transfer experiments indicate that also on the high-millisecond timescale BlaC is not dynamic. Using relaxation dispersion experiments, clear evidence was obtained for dynamics in the low-millisecond range, with an exchange rate of ca. 860 s-1 The dynamic amide groups are localized in the active site. Upon formation of an adduct with the inhibitor avibactam, extensive line broadening occurs, indicating an increase in magnitude of the active-site dynamics. Furthermore, the rate of the motions increases significantly. Upon reaction with the inhibitor clavulanic acid, similar line broadening is accompanied by duplication of NMR signals, indicative of at least one additional, slower exchange process (exchange rate, kex, of <100 s-1), while for this inhibitor also loss of pico- to nanosecond timescale rigidity is observed for some amides in the α domain. Possible sources of the observed dynamics, such as motions in the omega loop and rearrangements of active-site residues, are discussed. The increase in dynamics upon ligand binding argues against a model of inhibitor binding through conformational selection. Rather, the induced dynamics may serve to maximize the likelihood of sampling the optimal conformation for hydrolysis of the bound ligand.

Molecular property diagnostic suite for diabetes mellitus (MPDSDM): An integrated web portal for drug discovery and drug repurposing.

Molecular Property Diagnostic Suite - Diabetes Mellitus (MPDSDM) is a Galaxy-based, open source disease-specific web portal for diabetes. It consists of three modules namely (i) data library (ii) data processing and (iii) data analysis tools. The data library (target library and literature) module provide extensive and curated information about the genes involved in type 1 and type 2 diabetes onset and progression stage (available at http://www.mpds-diabetes.in). The database also contains information on drug targets, biomarkers, therapeutics and associated genes specific to type 1, and type 2 diabetes. A unique MPDS identification number has been assigned for each gene involved in diabetes mellitus and the corresponding card contains chromosomal data, gene information, protein UniProt ID, functional domains, druggability and related pathway information. One of the objectives of the web portal is to have an open source data repository that contains all information on diabetes and use this information for developing therapeutics to cure diabetes. We also make an attempt for computational drug repurposing for the validated diabetes targets. We performed virtual screening of 1455 FDA approved drugs on selected 20 type 1 and type 2 diabetes proteins using docking protocol and their biological activity was predicted using "PASS Online" server (http://www.way2drug.com/passonline) towards anti-diabetic activity, resulted in the identification of 41 drug molecules. Five drug molecules (which are earlier known for anti-malarial/microbial, anti-viral, anti-cancer, anti-pulmonary activities) were proposed to have a better repurposing potential for type 2 anti-diabetic activity and good binding affinity towards type 2 diabetes target proteins.

The complex metabolism of trimethylamine in humans: endogenous and exogenous sources.

Trimethylamine (TMA) is a tertiary amine with a characteristic fishy odour. It is synthesised from dietary constituents, including choline, L-carnitine, betaine and lecithin by the action of microbial enzymes during both healthy and diseased conditions in humans. Trimethylaminuria (TMAU) is a disease typified by its association with the characteristic fishy odour because of decreased TMA metabolism and excessive TMA excretion. Besides TMAU, a number of other diseases are associated with abnormal levels of TMA, including renal disorders, cancer, obesity, diabetes, cardiovascular diseases and neuropsychiatric disorders. Aside from its role in pathobiology, TMA is a precursor of trimethylamine-N-oxide that has been associated with an increased risk of athero-thrombogenesis. Additionally, TMA is a major air pollutant originating from vehicular exhaust, food waste and animal husbandry industry. The adverse effects of TMA need to be monitored given its ubiquitous presence in air and easy absorption through human skin. In this review, we highlight multifaceted attributes of TMA with an emphasis on its physiological, pathological and environmental impacts. We propose a clinical surveillance of human TMA levels that can fully assess its role as a potential marker of microbial dysbiosis-based diseases.