Biology subjects

Vivek-Ananth, R. P.

Publications and source records attributed to Vivek-Ananth, R. P..

10 recordsLinked to original sources

bioRxiv2025

sCentInDB: A database of essential oil chemical profiles of Indian medicinal plants

Essential oils are complex mixtures of volatile compounds produced by aromatic plants. Due to their odor and therapeutic properties, essential oils are used in personal care, food flavoring and pharmaceutical industries. As a high-value and low-volume organic product, optimizing plant yield and modifying composition by leveraging knowledge on chemical profiles of essential oils can lead to enhanced bioproducts. To this end, we present sCentInDB, a manually curated DataBase of Essential oil Chemical profiles of Medicinal plants of India, which has been built based on information in published literature. sCentInDB is a FAIR-compliant database which compiles information on 554 Indian medicinal plants at the plant part level, encompassing 2170 essential oil profiles, 3420 chemicals, 471 plant-part-therapeutic use associations, 120 plant-part-odor associations, and 218 plant-part-color associations. For the documented essential oils, sCentInDB also compiles extensive metadata including sample location, isolation and analysis methods. Subsequently, an extensive analysis of the chemical space of essential oils documented in sCentInDB was performed. By constructing a chemical similarity network, the chemical space was found to be structurally diverse with terpenoids distributed across the network. Moreover, a comparison of the scaffold diversity in sCentInDB was performed against three other aroma libraries using cyclic system retrieval curves. Altogether, sCentInDB will serve as a valuable resource for researchers working on plant volatiles and employing genetic engineering to enhance oil yield and composition. Further, sCentInDB will aid in establishment of quality standards for essential oils and provide vital insights for therapeutic and perfumery applications. sCentInDB is accessible at https://cb.imsc.res.in/scentindb/.

pharmacology and toxicology↗

bioRxiv2023

Machine learning models for prediction of xenobiotic chemicals with high propensity to transfer into human milk

Breast milk serves as a vital source of essential nutrients for infants. However, human milk contamination via transfer of environmental chemicals from maternal exposome is a significant concern for infant health. Machine learning based predictive toxicology models can be valuable in predicting chemicals with high propensity to transfer into human milk. To this end, we build such classification- and regression-based models by employing multiple machine learning algorithms and leveraging the largest curated dataset to date of 375 chemicals with known Milk to Plasma concentration (M/P) ratios. Our Support Vector Machine (SVM) based classifier outperforms other models in terms of different performance metrics, when evaluated on both (internal) test data and external test dataset. Specifically, the SVM based classifier on (internal) test data achieved a classification accuracy of 77.33%, specificity of 84%, sensitivity of 64%, and F-score of 65.31%. When evaluated on an external test dataset, our SVM based classifier is found to be generalizable with sensitivity of 77.78%. While we were able to build highly predictive classification models, our best regression models for predicting the M/P ratio of chemicals could achieve only moderate R2 values on the (internal) test data. As noted in earlier literature, our study also highlights the challenges in developing accurate regression models for predicting the M/P ratio of xenobiotic chemicals. We have made our complete workflow, train and test datasets, and computer codes for the classification and regression models publicly available via a dedicated GitHub repository. Overall, this study attests the immense potential of predictive computational toxicology models in characterizing the myriad chemicals in the human exposome.

pharmacology and toxicology↗

bioRxiv2023

T9GPred: A Comprehensive Computational Tool for the Prediction of Type 9 Secretion System, Gliding Motility and the Associated Secreted Proteins

Type 9 secretion system (T9SS) is one of the least characterized secretion systems exclusively found in the Bacteroidetes phylum which comprise various environmental and economically relevant bacteria. While T9SS plays a central role in bacterial movement termed gliding motility, survival and pathogenicity, there is an unmet need for a comprehensive tool that predicts T9SS, gliding motility and proteins secreted via T9SS. In this study, we develop such a computational tool, Type 9 secretion system and Gliding motility Prediction (T9GPred). To build this tool, we manually curated published experimental evidence and identified mandatory components for T9SS and gliding motility prediction. We also compiled experimentally characterized proteins secreted via T9SS and determined the presence of three unique types of C-terminal domain signals, and these insights were leveraged to predict proteins secreted via T9SS. Notably, using recently published experimental evidence, we show that T9GPred has high predictive power. Thus, we used T9GPred to predict the presence of T9SS, gliding motility and associated secreted proteins across 693 completely sequenced Bacteroidetes strains. T9GPred predicted 402 strains to have T9SS, of which 327 strains are also predicted to exhibit gliding motility. Further, T9GPred also predicted putative secreted proteins for the 402 strains. In a nutshell, T9GPred is a novel computational tool for systems-level prediction of T9SS and streamlining future experimentation. The source code of the computational tool is available in our GitHub repository: https://github.com/asamallab/T9GPred. The tool and its predicted results are compiled in a web server available at: https://cb.imsc.res.in/t9gpred/.

bioinformatics↗

bioRxiv2022

Identification of activity cliffs in structure-activity landscape of androgen receptor binding chemicals

Androgen mimicking environmental chemicals can bind to Androgen receptor (AR) and can cause severe effects on the reproductive health of males. Predicting such endocrine disrupting chemicals (EDCs) in the human exposome is vital for improving current chemical regulations. To this end, QSAR models have been developed to predict androgen binders. However, a continuous structure-activity relationship (SAR) wherein chemicals with similar structure have similar activity does not always hold. Activity landscape analysis can help map the structure-activity landscape and identify unique features such as activity cliffs. Here we performed a systematic investigation of the chemical diversity along with the global and local structure-activity landscape of a curated list of 144 AR binding chemicals. Specifically, we clustered the AR binding chemicals and visualized the associated chemical space. Thereafter, consensus diversity plot was used to assess the global diversity of the chemical space. Subsequently, the structure-activity landscape was investigated using SAS maps which capture the activity difference and structural similarity among the AR binders. This analysis led to a subset of 41 AR binding chemicals forming 86 activity cliffs, of which 14 are activity cliff generators. Additionally, SALI scores were computed for all pairs of AR binding chemicals and the SALI heatmap was also used to evaluate the activity cliffs identified using SAS map. Finally, we provide a classification of the 86 activity cliffs into six categories using structural information of chemicals at different levels. Overall, this investigation reveals the heterogeneous nature of the structure-activity landscape of AR binding chemicals and provides insights which will be crucial in preventing false prediction of chemicals as androgen binders and developing predictive computational toxicity models in the future.

pharmacology and toxicology↗

bioRxiv2022

Scaffold and structural diversity of the secondary metabolite space of medicinal fungi

Medicinal fungi including mushrooms have well documented therapeutic uses. The MeFSAT database provides a curated library of more than 1800 secondary metabolites produced by medicinal fungi for potential use in high throughput screening (HTS) studies. In this study, we perform a cheminformatics based investigation of the scaffold and structural diversity of the secondary metabolite space of medicinal fungi, and moreover, perform a detailed comparison with approved drugs, other natural product libraries and semi-synthetic libraries. We find that the secondary metabolite space of MeFSAT has similar or higher scaffold diversity in comparison to other natural product libraries analysed here. Notably, 94% of the scaffolds in the secondary metabolite space of MeFSAT are not present in the approved drugs. Further, we find that the secondary metabolites of medicinal fungi, on the one hand are structurally far from the approved drugs, while on the other hand are close in terms of molecular properties to approved drugs. Lastly, chemical space visualization using dimensionality reduction methods showed that the secondary metabolite space has minimal overlap with the approved drug space. In a nutshell, our results underscore that the secondary metabolite space of medicinal fungi is a valuable resource for identifying potential lead molecules for natural product based drug discovery.

pharmacology and toxicology↗

bioRxiv2022

IMPPAT 2.0: an enhanced and expanded phytochemical atlas of Indian medicinal plants

Compilation, curation, digitization and exploration of the phytochemical space of Indian medicinal plants can expedite ongoing efforts toward natural product and traditional knowledge based drug discovery. To this end, we present IMPPAT 2.0, an enhanced and expanded database, compiling manually curated information on 4010 Indian medicinal plants, 17967 phytochemicals, 1095 therapeutic uses and 1133 traditional Indian medicinal formulations. Notably, IMPPAT 2.0 compiles associations at the level of plant parts, and provides a FAIR compliant non-redundant in silico stereo-aware library of 17967 phytochemicals from Indian medicinal plants. The phytochemical library has been annotated with several useful properties to enable easier exploration of the chemical space. We also filtered a subset of 1335 drug-like phytochemicals of which majority have no similarity to existing approved drugs. Using cheminformatics, we have characterized the molecular complexity and molecular scaffold based structural diversity of the phytochemical space of Indian medicinal plants, and performed a comparative analysis with other chemical libraries. Altogether, IMPPAT is the largest phytochemical atlas of Indian medicinal plants which is accessible at: https://cb.imsc.res.in/imppat/.

pharmacology and toxicology↗

bioRxiv2020

MeFSAT: A curated natural product database specific to secondary metabolites of medicinal fungi

Fungi are a rich source of secondary metabolites which constitutes a valuable and diverse chemical space of natural products. Medicinal fungi have been used in traditional medicine to treat human ailments for centuries. To date, there is no devoted resource on secondary metabolites and therapeutic uses of medicinal fungi. Such a dedicated resource compiling dispersed information on medicinal fungi across published literature will facilitate ongoing efforts towards natural product based drug discovery. Here, we present the first comprehensive manually curated database on Medicinal Fungi Secondary metabolites And Therapeutics (MeFSAT) that compiles information on 184 medicinal fungi, 1830 secondary metabolites and 149 therapeutics uses. Importantly, MeFSAT contains a non-redundant in silico natural product library of 1830 secondary metabolites along with information on their chemical structures, computed physicochemical properties, drug-likeness properties, predicted ADMET properties, molecular descriptors and predicted human target proteins. By comparing the physicochemical properties of secondary metabolites in MeFSAT with other small molecules collections, we find that fungal secondary metabolites have high stereochemical complexity and shape complexity similar to other natural product libraries. Based on multiple scoring schemes, we have filtered a subset of 228 drug-like secondary metabolites in MeFSAT database. By constructing and analyzing chemical similarity networks, we show that the chemical space of secondary metabolites in MeFSAT is highly diverse. The compiled information in MeFSAT database is openly accessible at: https://cb.imsc.res.in/mefsat/.

bioinformatics↗

bioRxiv2019

A curated knowledgebase enabling a network perspective on endocrine disrupting chemicals and their biological systems-level perturbations

Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can perturb the hormonal homeostasis leading to adverse health effects. In this work, we have developed a detailed workflow to identify EDCs with supporting evidence of endocrine disruption in published experiments in humans or rodents. Thereafter, this workflow was used to manually evaluate more than 16000 published research articles and identify 686 potential EDCs with published evidence in humans or rodents. Importantly, we have compiled the observed adverse effects or endocrine-specific perturbations along with the dosage information for the potential EDCs from their supporting published experiments. Subsequently, the potential EDCs were classified based on the type of supporting evidence, their environmental source and their chemical properties. Additional compiled information for potential EDCs include their chemical structure, physicochemical properties, predicted ADMET properties and target genes. In order to enable future research based on this compiled information on potential EDCs, we have built an online knowledgebase, Database of Endocrine Disrupting Chemicals and their Toxicity profiles (DEDuCT), accessible at: https://cb.imsc.res.in/deduct/. After building this comprehensive resource, we employed a network biology approach to study the chemical space of EDCs and its potential link to the biological space of target genes of EDCs. Specifically, we have constructed two networks of EDCs using our resource based on similarity of chemical structures or target genes. Ensuing analysis of these two networks revealed that EDCs can differ both in their chemical structure and set of target genes. Though our detailed results highlight potential challenges in developing predictive models for EDCs, the compiled information in our resource will undoubtedly enable future research in the field, especially, those focussed towards mechanistic understanding of the systems-level perturbations caused by EDCs.

bioinformatics↗

bioRxiv2017

Comparative systems analysis of the secretome of the opportunistic pathogen Aspergillus fumigatus and other Aspergillus species

Aspergillus fumigatus and multiple other Aspergillus species cause a wide range of lung infections, collectively termed aspergillosis. Aspergilli are ubiquitous in environment with healthy immune systems routinely eliminating inhaled conidia, however, Aspergilli can become an opportunistic pathogen in immune-compromised patients. The aspergillosis mortality rate and emergence of drug-resistance reveals an urgent need to identify novel targets. Secreted and cell membrane proteins play a critical role in fungal-host interactions and pathogenesis. Using a computational pipeline integrating data from high-throughput experiments and bioinformatic predictions, we have identified secreted and cell membrane proteins in ten Aspergillus species known to cause aspergillosis. Small secreted and effector-like proteins similar to agents of fungal-plant pathogenesis were also identified within each secretome. A comparison with humans revealed that at least 70% of Aspergillus secretomes has no sequence similarity with the human proteome. An analysis of antigenic qualities of Aspergillus proteins revealed that the secretome is significantly more antigenic than cell membrane proteins or the complete proteome. Finally, overlaying an expression dataset, four A. fumigatus proteins upregulated during infection and with available structures, were found to be structurally similar to known drug target proteins in other organisms, and were able to dock in silico with the respective drug.

bioinformatics↗

bioRxiv2017

IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics

Phytochemical constituents of medicinal plants encompass a diverse space of chemical scaffolds which can be used for rational design of novel drugs. India is rich with a flora of indigenous medicinal plants that have been used for centuries in traditional Indian medicine to treat human maladies. A comprehensive online database on the phytochemistry of Indian medicinal plants will enable the application of systems biology and cheminformatic approaches towards natural product based drug discovery. In this direction, we here present, IMPPAT, a manually curated database of Indian Medicinal Plants, Phytochemistry, And Therapeutics. IMPPAT contains 1742 Indian medicinal plants, 9596 phytochemicals and 1124 therapeutic uses which span across 27074 plant-phytochemical associations and 11514 plant-therapeutic associations. Notably, the curation effort led to a non-redundant in silico chemical library of 9596 phytochemicals with standard chemical identifiers and structure information. Using cheminformatic approaches, we have computed the physicochemical properties and drug-likeliness of the phytochemicals in IMPPAT which led to a filtered subset of 960 potential druggable phytochemicals. Moreover, a comparative analysis against FDA approved drugs suggests that majority of the druggable phytochemicals in IMPPAT are good candidates for novel prospective drugs as they have little or no structural similarity with existing drugs. The IMPPAT database is openly accessible at: https://www.imsc.res.in/~asamal/resources/imppat/home.

bioinformatics↗