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/.

An interpretative review of the wastewater-based surveillance of the SARS-CoV-2: where do we stand on its presence and concern?

Wastewater-based epidemiology (WBE) has been used for monitoring infectious diseases like polio, hepatitis, etc. since the 1940s. It is also being used for tracking the SARS-CoV-2 at the population level. This article aims to compile and assess the information for the qualitative and quantitative detection of the SARS-CoV-2 in wastewater. Based on the globally published studies, we highlight the importance of monitoring SARS-CoV-2 presence/detection in the wastewater and concurrently emphasize the development of early surveillance techniques. SARS-CoV-2 RNA sheds in the human feces, saliva, sputum and mucus that ultimately reaches to the wastewater and brings viral RNA into it. For the detection of the virus in the wastewater, different detection techniques have been optimized and are in use. These are based on serological, biosensor, targeted PCR, and next generation sequencing for whole genome sequencing or targeted amplicon sequencing. The presence of the SARS-CoV-2 RNA in wastewater could be used as a potential tool for early detection and devising the strategies for eradication of the virus before it is spread in the community. Additionally, with the right and timely understanding of viral behavior in the environment, an accurate and instructive model that leverages WBE-derived data may be created. This might help with the creation of technological tools and doable plans of action to lessen the negative effects of current viral epidemics or future potential outbreaks on public health and the economy. Further work toward whether presence of viral load correlates with its ability to induce infection, still needs evidence. The current increasing incidences of JN.1 variant is a case in point for continued early detection and surveillance, including wastewater.

Analyzing the cation-aromatic interactions in proteins: Cation-aromatic database V2.0.

The cation-aromatic database (CAD) is a comprehensive repository of cation-aromatic motifs found in experimentally determined protein structures, first reported in 2007 [Proteins, 2007, 67, 1179]. The present article is an update of CAD that contains information of approximately 27.26 million cation-aromatic motifs. CAD uses three distance parameters (r, d1, and d2) to determine the position of the cation relative to the centroid of the aromatic residue and classifies the motifs as cation-π or cation-σ interactions. As of June 2023, about 193 936 protein structures were retrieved from Protein Data Bank, and this resulted in the identification of an impressive number of 27 255 817 cation-aromatic motifs. Among these motifs, spherical motifs constituted 94.09%, while cylindrical motifs made up the remaining 5.91%. When considering the interaction of metal ions with aromatic residues, 965 564 motifs are identified. Remarkably, 82.08% of these motifs involved the binding of metal ions to the amino acid HIS. Moreover, the analysis of binding preferences between cations and aromatic residues revealed that the HIS-HIS, PHE-ARG, and TRP-ARG pairs exhibited a preferential geometry. The motif pair HIS-HIS was the most prevalent, accounting for 19.87% of the total, closely followed by TYR-LYS at 10.17%. Conversely, the motif pair TRP-HIS had the lowest occurrence, representing only 4.20% of the total. The data generated help in revealing the characteristics and biological functions of cation-aromatic interactions in biological molecules. The updated version of CAD (Cation-Aromatic Database V2.0) can be accessed at https://acds.neist.res.in/cadv2.

Analyzing the aromatic-aromatic interactions in proteins: A2ID 2.0.

The Aromatic-Aromatic Interactions Database (A2ID) is a comprehensive repository dedicated to documenting aromatic-aromatic (π-π) networks observed in experimentally determined protein structures. The first version of A2ID was reported in 2011 [Int J Biol Macromol, 2011, 48, 540]. It has undergone a series of significant updates, leading to its current version, which focuses on the identification and analysis of 3,444,619 π-π networks from proteins. The geometrical parameters such as centroid-centroid distances (r) and interplanar angles (ϕ) were used to identify and characterize π-π networks. It was observed that among the 84,500 proteins with at least one aromatic π-π network, about 92.50 % of the instances are found to be either 2π (77.34 %) or 3π (15.23 %) networks. The analysis of interacting amino acid pairs in 2π networks indicated a dominance of PHE residues followed by TYR. The updated version of A2ID incorporates analysis of π-π networks based on SCOP2 and ECOD classifiers, in addition to the existing SCOP, CATH, and EC classifications. This expanded scope allows researchers to explore the characteristics and functional implications of π-π networks in protein structures from multiple perspectives. The current version of A2ID along with its extensive dataset and detailed geometric information is publicly accessible using https://acds.neist.res.in/a2idv2.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL): a structure-based classification of the chemical space.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL) is an open-source Galaxy-based cheminformatics web portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, obtained from 42 publicly available databases and curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions, has been done. These are further subjected to 56-bit fingerprint-based classification algorithm which led to the formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put into 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-AadharID, for each of these 149,169,443 molecules. MPDS-AadharID is akin to the unique number given to citizens in India (similar to SSN in the US and NINO in the UK). The unique features of MPDS-CL are (a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; (b) automatic generation of information for the processing for MPDS and other galaxy tools; (c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and (d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at https://mpds.neist.res.in:8086/ .

Machine learning based dynamic consensus model for predicting blood-brain barrier permeability.

The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, however, the reliability of these models is questionable due to the smaller and class-imbalance dataset which subsequently leads to a very high false positive rate. In this study, machine learning and deep learning-based predictive models were built using XGboost, Random Forest, Extra-tree classifiers and deep neural network. A dataset of 8153 compounds comprising both the BBB permeable and BBB non-permeable was curated and subjected to calculations of molecular descriptors and fingerprints for generating the features for machine learning and deep learning models. Three balancing techniques were then applied to the dataset to address the class-imbalance issue. A comprehensive comparison among the models showed that the deep neural network model generated on the balanced MACCS fingerprint dataset outperformed with an accuracy of 97.8% and a ROC-AUC score of 0.98 among all the models. Additionally, a dynamic consensus model was prepared with the machine learning models and validated with a benchmark dataset for predicting BBB permeability with higher confidence scores.

Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials.

One of the major challenges in drug development is having acceptable levels of efficacy and safety throughout all the phases of clinical trials followed by the successful launch in the market. While there are many factors such as molecular properties, toxicity parameters, mechanism of action at the target site, etc. that regulates the therapeutic action of a compound, a holistic approach directed towards data-driven studies will invariably strengthen the predictive toxicological sciences. Our quest for the current study is to find out various reasons as to why an investigational candidate would fail in the clinical trials after multiple iterations of refinement and optimization. We have compiled a dataset that comprises of approved and withdrawn drugs as well as toxic compounds and essentially have used time-split based approach to generate the training and validation set. Five highly robust and scalable machine learning binary classifiers were used to develop the predictive models that were trained with features like molecular descriptors and fingerprints and then validated rigorously to achieve acceptable performance in terms of a set of performance metrics. The mean AUC scores for all the five classifiers with the hold-out test set were obtained in the range of 0.66-0.71. The models were further used to predict the probability score for the clinical candidate dataset. The top compounds predicted to be toxic were analyzed to estimate different dimensions of toxicity. Apparently, through this study, we propose that with the appropriate use of feature extraction and machine learning methods, one can estimate the likelihood of success or failure of investigational drugs candidates thereby opening an avenue for future trends in computational toxicological studies. The models developed in the study can be accessed at https://github.com/gnsastry/predicting_clinical_trials.git.

OSADHI - An online structural and analytics based database for herbs of India.

The current study aims to develop a PAN India database of medicinal plants along with their phytochemicals and geographical availability. The database consists of 6959 unique medicinal plants belonging to 348 families which are available across 28 states and 8 union territories of India. The database sources the information on four different sections - traditional knowledge, geographical indications, phytochemicals, and chemoinformatics. The traditional knowledge reports the plant taxonomy with their vernacular names. A total of 27,440 unique phytochemicals associated with these plants were curated from various sources in this study. However, due to the non-availability of general information like IUPAC names, InChI key, etc. from reliable sources, only 22,314 phytochemicals have been currently reported in the database. Various analyses have been performed for the phytochemicals which include analysis of physicochemical and ADMET properties calculated from open-source web servers using in-house python scripts. The phytochemical data set has also been classified based on the class, superclass, and pathways respectively using NPClassifier, a deep learning framework. Additionally, the antiviral potency of the phytochemicals was also predicted using two machine learning models - Random Forest and XGBoost. The database aims to provide accurate and exhaustive data of the traditional practice of medicinal plants in India in a single platform integrating and analyzing the rich customary practices and facilitating the development and identification of plant-based therapeutics for a variety of diseases. The database can be accessed at https://neist.res.in/osadhi/.

Seroepidemiological and genomic investigation of SARS-CoV-2 spread in North East region of India.

PURPOSE: Seroepidemiology and genomic surveillance are valuable tools to investigate infection transmission during a pandemic. North East (NE) India is a strategically important region being the gateway connecting the country with Southeast Asia. Here, we examined the spread of SARS-CoV-2 in NE India during the first and second waves of COVID-19 using serological and whole genome sequencing approaches. METHODS: qRT-PCR analysis was performed on a selected population (n â€‹= â€‹16,295) from June 2020 to July 2021, and metadata was collected. Immunoassays were studied (n â€‹= â€‹2026) at three-time points (August 2020, February 2021, and June 2021) and in a cohort (n â€‹= â€‹35) for a year. SARS-CoV-2 whole genomes (n â€‹= â€‹914) were sequenced and analyzed with those obtained from the databases. RESULTS: Test positivity rates (TPR) in the first and second waves were 6.34% and 6.64% in Assam, respectively, and a similar pattern was observed in other NE states. Seropositivity in the three time points was 10.63%, 40.3%, and 46.33%, respectively, and neutralizing antibody prevalence was 90.91%, 52.14%, and 69.30%, respectively. Persistence of pan-IgG-N SARS-CoV-2 antibody for over a year was observed among three subjects in the cohort group. Normal variants dominated the first wave, while B.1.617.2 and AY-sublineages dominated the second wave in the region. The prevalence of the variants co-related well with high TPR and seropositivity rate in the region and identified mostly among vaccinated individuals. CONCLUSION: The COVID-19 first wave in the region witnessed low transmission with the evolution of diverse variants. Seropositivity increased during the study period with over half of the individuals carrying neutralizing antibodies against SARS-CoV-2. High infection and seroprevalence in NE India during the second wave were associated with the dominant emergence of variants of concern.

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 .

North East India medicinal plants database (NEI-MPDB).

The rich biodiversity of North East India is one of the recognized biodiversity hotspots of the world. This region comprises of eight states (Assam, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura) with diverse ethnic communities having invaluable traditional knowledge/practices, passed through genesis. The medicinal plants in this region are rich in natural products/phytochemicals and have been used extensively by pharmaceutical industries. The present study is an attempt to develop a comprehensive resource of the medicinal plants with a quantitative analysis of the phytochemicals which can enhance knowledge on therapeutic indications and contribute in drug discovery and development. The database is a collection of 561 unique plants comprising of 9225 phytochemicals. The physiochemical properties of the phytochemicals were analyzed using indigenous python scripts whereas for the ADMET properties, open access servers were used. The data available in NEI-MPDB will help to connect the cutting-edge approach of various research groups and will help to translate the information into economic wealth by the pharmaceutical industries. The database is openly accessible at https://neist.res.in/neimpdb/.

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds.

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID-19 antiviral SAR dataset whose activity was reported based on IC50 value. A total 1157 two-dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree-based, Correlation-based and Mutual information-based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies.