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.

Graphical abstract

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.

Chronic groin pain in Desarda versus Lichtenstein hernia repair - a randomised controlled study

BACKGROUND

Chronic pain after inguinal hernia repair is a common complication. This study compared the difference between Desarda repair and Lichtenstein repair for inguinal hernia in chronic groin pain.

METHODS

One hundred patients with unilateral uncomplicated inguinal hernia were randomised to either Desarda repair (n = 50) or Lichtenstein repair (n = 50) under local anaesthesia and were evaluated for pain postoperatively. Operative time, surgical complications, time to return to normal gait and work, and overall patient satisfaction were recorded. The patient was blinded to the procedure. Any pain at three months (numerical rating scale 1 or more) was considered chronic pain.

RESULTS

Mean operation time was approximately 5 minutes less for Desarda (p = 0.33). There was no significant difference in terms of pain level postoperatively between Lichtenstein and Desarda groups. Twenty-two (44%) patients in the Lichtenstein group had chronic pain, and twenty-one (45.7%) patients had chronic pain in the Desarda group (p = 0.871). No significant difference was observed in haematoma formation, wound infection, recurrence rate, seroma, or foreign body sensation. The mean time for patients to return to normal gait was approximately 0.5 day earlier for the Desarda group (p = 0.29). The mean time for patients to return to normal work was comparable (p = 0.99). Desarda group had a slightly higher satisfaction rate than the Lichtenstein group (9.1%).

CONCLUSION

Desarda repair is not inferior to Lichtenstein repair in the short-term concerning complications or pain.

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 (3CL^pro, PL^pro, 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.

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.

Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery

Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.