Biology subjects

Baskaran, S. P.

Publications and source records attributed to Baskaran, S. P..

8 recordsLinked to original sources

bioRxiv2026

Molecular Characterization of SARS-CoV-2 N Protein Interfaces: Implications for Oligomerization, RNA Binding, and Phase Separation

The SARS-CoV-2 nucleocapsid (N) protein is central to genomic RNA recognition, condensation, and packaging, yet the molecular organization of its multivalent N-N and N-RNA interaction network involved in this process remains unclear. Here, we define the oligomerization and RNA-binding interfaces of the C-terminal domain (CTD) and its flanking intrinsically disordered regions (IDRs), the leucine-rich helix (LH) and the C-terminal IDR (C-IDR), using size-exclusion chromatography (SEC), cross-linking, mutational studies and NMR spectroscopy. We identify discrete oligomerization interfaces within the CTD and C-IDR that drive higher-order assembly, and show, through liquid-liquid phase separation (LLPS) and electron microscopy (EM), that C-IDR residues are essential for RNA-induced condensate formation. Moreover, the mapping of RNA-binding residues highlights Arg277 as a conserved determinant of CTD-RNA recognition. Notably, the two IDRs exert opposing regulatory effects on RNA binding, with the C-IDR enhancing and the LH attenuating CTD-RNA interactions. Together, these findings reveal how cooperative interfaces between the CTD and its flanking IDRs orchestrate N-protein oligomerization and RNA condensate formation and highlight potential intervention sites for disrupting SARS-CoV-2 ribonucleoprotein assembly.

biophysics↗

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↗

bioRxiv2024

Computational prediction of cyclotides from Viola odorata as potential inhibitors against the neuraminidase of Streptococcus pneumoniae

Cyclotides are naturally occurring peptides characterized by a cyclic cystine knot, which provides them with exceptional structural stability. In addition to their stability, cyclotides exhibit diverse therapeutic activities including antimicrobial, antiviral and antitumor activities, making them promising candidates in drug discovery. However, computational studies aimed at identifying cyclotide-based inhibitors for infectious diseases remain limited. To this end, we conducted a virtual screening of cyclotides from an Indian medicinal plant Viola odorata to identify potential inhibitors against a bacterial pathogen causing respiratory infections. We compiled a library of 93 cyclotides by retrieving their structures from public domain or predicting them using the AlphaFold server. We then docked these cyclotides against the neuraminidase protein of Streptococcus pneumoniae and analyzed the interacting residues and binding energies to identify top five potential inhibitors namely, kalata S, kalata B1, cycloviolacin O15, vodo L12, and cycloviolacin O36. Thereafter, we performed molecular dynamics simulations of the protein-cyclotide complexes, and observed that the cyclotides remained stable within the complex. Altogether, this study is the first computational effort to identify potential cyclotide inhibitors against bacterial pathogen causing respiratory diseases, which can be further pursued for experimental validation to develop novel therapeutic agents for respiratory infections.

pharmacology and toxicology↗

bioRxiv2024

UVREK: Development and analysis of an expression profile knowledgebase of biomolecules induced by ultraviolet radiation exposure

Humans encounter diverse environmental factors which can have impact on their health. One such environmental factor is the ultraviolet (UV) radiation, which is part of the physical component of the exposome. UV radiation is the leading cause of skin cancer and is a significant global health concern. A large body of published research has been conducted to uncover the mechanisms underlying the adverse outcomes of UV radiation exposure on living beings. These studies involve identifying the biomolecules induced upon UV radiation exposure. A few previous efforts have attempted to compile this information in the form of a database, but such earlier efforts have certain limitations. To fill this gap, we present a structured database named UVREK, containing manually curated data on biomolecules induced by UV radiation exposure from published literature. UVREK has compiled information on 985 genes, 470 proteins, 54 metabolites and 77 miRNAs along with their metadata. Thereafter, an enrichment analysis performed on the human gene set of UVREK database showed the importance of transcription related processes in UV related response, and enrichment of pathways involved in cancer and aging. While significantly contributing towards characterizing the physical component of the exposome, we expect the compiled data in UVREK will serve as a valuable resource for development of better UV protection mechanisms such as UV sensors and sunscreens. Noteworthy, UVREK is the only resource to date, compiling varied types of biomolecular responses to UV radiation with corresponding metadata. UVREK is openly accessible at: https://cb.imsc.res.in/uvrek/.

bioinformatics↗

bioRxiv2023

Cheminformatics analysis of multi-target structure-activity landscape of environmental chemicals binding to human endocrine receptors

In human exposome, environmental chemicals can target, and disrupt different endocrine axes, ultimately leading to several endocrine disorders. Such chemicals, termed endocrine disrupting chemicals (EDCs), can promiscuously bind to different endocrine receptors and lead to varying biological endpoints. Thus, understanding the complexity in molecule-receptor binding of environmental chemicals can aid in the development of robust toxicity predictors. Towards this, the ToxCast project has generated the largest resource on the chemical-receptor activity data for environmental chemicals that were screened across various endocrine receptors. However, the heterogeneity in the multi-target structure-activity landscape of such chemicals is not yet explored. In this study, we systematically curated the chemicals targeting 8 human endocrine receptors, their activity values and biological endpoints from the ToxCast chemical library. We employed dual-activity difference and triple-activity difference maps to identify single-, dual-, and triple-target cliffs across different target combinations. We annotated the identified activity cliffs through matched molecular pair (MMP) based approach, and observed that a small fraction of activity cliffs form MMPs. Further, we structurally classified the activity cliffs and observed that R-group cliffs form the highest fraction among the cliffs identified in various target combinations. Finally, we leveraged the mechanism of action (MOA) annotations to analyze structure-mechanism relationships, identified strong MOA-cliffs and weak MOA-cliffs, for each of the 8 endocrine receptors. Overall, insights from this first study analyzing the structure-activity landscape of environmental chemicals targeting multiple human endocrine receptors, will likely contribute towards the development of better toxicity prediction models for characterizing the human chemical exposome.

pharmacology and toxicology↗

bioRxiv2023

Analysis of structure-activity and structure-mechanism relationships among thyroid stimulating hormone receptor binding chemicals by leveraging ToxCast library

Thyroid stimulating hormone receptor (TSHR) is an integral part of the hypothalamic-pituitary-thyroid axis. Notably, dysregulation in TSHR activation in humans can lead to adverse effects such as Graves disease, hypothyroidism and Hashimotos disease. Moreover, animal studies have shown that binding of endocrine disrupting chemicals (EDCs) with TSHR can lead to developmental toxicity. Several such chemicals have also been screened for their adverse physiological effects in human cell lines through various high-throughput assays under the ToxCast project. The vast resource of data generated through ToxCast has enabled the development of different toxicity predictors, but they can be limited in their predictive ability due to the heterogeneity in structure-activity relationships among chemicals. In an attempt to explore this heterogeneity, we systematically investigated structure-activity and structure-mechanism relationships among the TSHR binding chemicals from ToxCast. By employing structure-activity similarity (SAS) map, we identified 79 activity cliffs among 509 chemicals in the TSHR agonist dataset and 69 activity cliffs among 650 chemicals in the TSHR antagonist dataset. Further, by using the matched molecular pair (MMP) approach, we find that the resultant activity cliffs (MMP-cliffs) are a subset of activity cliffs identified via the SAS map approach. Moreover, by leveraging ToxCast mechanism of action (MOA) annotations for chemicals common to both TSHR agonist and antagonist datasets, we identified 3 chemical pairs as Strong MOA-cliffs and 19 chemical pairs as Weak MOA-cliffs. In conclusion, the insights from this systematic analysis of the structure-activity as well as the structure-mechanism relationships of TSHR binding chemicals are likely to inform ongoing efforts towards development of better predictive toxicity models for characterizing the chemical exposome.

pharmacology and toxicology↗

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↗