Biology subjects

Mantri, S. S.

Publications and source records attributed to Mantri, S. S..

3 recordsLinked to original sources

bioRxiv2026

Microbial bioprospecting for benzoxazolinate-like molecules: unleashing the potential of genome mining

The benzoxazolinate moiety is a key functional group found in a few natural products (NPs), exhibiting diverse bioactivities, including antitumor, antibacterial, and cytotoxic activities. Despite their clinical importance, only a few bacterial strains and NPs have been reported harboring this rare bis-heterocyclic moiety, underscoring a largely unexplored chemical space. Here, we performed large-scale genome mining and identified 277 putative biosynthetic gene clusters (BGCs) across diverse bacterial hosts, including previously unreported bacterial genera and strains. The BGCs were grouped into three compound classes: benzoxazolinate, benzobactin, and ashimides based on sequence similarity network clustering. Bioactivity predictions of the identified BGCs revealed the predominance of antibacterial and cytotoxic potential, highlighting promising candidates for future experimental validation and functional studies. This study also presents a neural network-based bioprospecting model that efficiently detects rare BGCs encoding benzoxazolinate-containing molecules from genomic sequences. Overall, our findings expand the known repertoire of bacterial hosts with the potential to produce benzoxazolinate-containing NPs and provide a comprehensive framework for the discovery and identification of candidate BGCs.

bioinformatics↗

bioRxiv2026

BGX: A Comprehensive Pipeline for Genomic Insight into Bioactivity Prediction, Genomic Surveillance, and Novel Biosynthetic Gene Cluster Assessment

The increasing availability of genomic and metagenomic data has created significant opportunities to explore microbial diversity, biosynthetic potential and functional traits. However, comprehensive and comparative genome analysis often requires integrating multiple independent tools, making large-scale studies challenging to implement and manage. Here, we present Bacterial Genome eXplorer (BGX), an integrated and scalable pipeline that streamlines large-scale genome analysis and exploration of biosynthetic potential. BGX integrates different analytical tools into six major stages: (i) genome retrieval and assembly, (ii) genome quality assessment, (iii) antimicrobial resistance (AMR) gene profiling, (iv) annotating Biosynthetic gene clusters (BGCs) and novelty assessment, (v) bioactivity predictions, and (vi) clustering and networking analysis. In addition, BGX provides a user-friendly interactive interface to facilitate data exploration and interpretation. We demonstrated the versatility and scalability of BGX through large-scale analysis of two independent datasets: 248 genomes from the One Day One Genome (ODOG) initiative and 153 publicly available genomes from NCBI. This analysis enabled the comprehensive characterisation of genome quality, AMR determinants, biosynthetic potential and candidate bioactive metabolites across two datasets. BGX is distributed as a Docker container that simplifies installation, enables reproducible data processing, and supports pipeline execution across different computational environments. The modular and reproducible architecture of the BGX pipeline provides an effective framework for large-scale genome mining, genomic surveillance, and accelerates the discovery and prioritisation of novel secondary metabolites. BGX is now accessible at https://bgx.nabi.res.in

bioinformatics↗

bioRxiv2024

Predicting biological activity from biosynthetic gene clusters using neural networks

Microorganisms like bacteria and fungi have been used for natural products that translate to drugs. However, assessing the bioactivity of extract from culture to identify novel natural molecules remains a strenuous process due to the cumbersome order of production, purification, and assaying. Thus, extensive genome mining of microbiomes is underway to identify biosynthetic gene clusters or BGCs that can be profiled as particular natural products, and computational methods have been developed to address this problem using machine learning. However, existing tools are ineffective due to a small training dataset, dependence on old genome mining tools, lack of relevant genomic descriptors, and prevalent class imbalance. This work presents a new tool, NPBdetect, that can detect multiple bioactivities and has been designed through rigorous experiments. Firstly, we composed a larger training set using MIBiG database and a test set through literature mining to build and assess the model respectively. Secondly, the latest antiSMASH genome mining tool was used to obtain BGC and introduced new sequence-based descriptors. Thirdly, neural networks are used to build the model by dealing with class imbalance issues through the class weighting technique. Finally, we compared the NPBdetect tool with an existing tool to show its efficacy and real-world utility in detecting several bioactivities with high confidence.

bioinformatics↗