https://dspace.nii.res.in//https://dspace.nii.res.in/handle/123456789/13 Principal Investigator- Dr. Debasisa Mohanty 2026-04-03T00:46:51Z https://dspace.nii.res.in//https://dspace.nii.res.in/handle/123456789/1584 Title: HgutMgene-Miner: In silico genome mining tool for deciphering the drug-metabolizing potential of human gut microbiome Authors: Mohanty, Debasisa; Kumar, Manish; Kumar, Vikas; Amir, Sana Abstract: The biotransformation of drugs by enzymes from the human microbiome can produce active or inactive products, impacting the bioactivity and function of these drugs inside the human host. However, understanding the biotransformation reactions of drug molecules catalyzed by bacterial enzymes in human microbiota is still limited. Hence, to characterize drug utilization capabilities across all the microbial phyla inside the human gut, we have used a knowledge-based approach to develop HgutMgene-Miner software which predicts xenobiotic metabolizing enzymes (XMEs) through genome mining. HgutMgene-Miner derives its predictive power from the MicrobiomeMetDB database, which systematically catalogs all known biotransformation reactions of xenobiotics and primary metabolites mediated by host-associated microbial enzymes. Over 10,000 isolate genomes from 830 different bacterial species found in the Unified Human Gastrointestinal Genome (UHGG) collection have been analyzed by HgutMgene-Miner. This led to the identification of 89,377 xenobiotic metabolizing enzymes (XMEs) across 13 phyla, with the greatest diversity in Bacteroidota, Firmicutes_A, Firmicutes, and Proteobacteria. Bacteroides, Clostridium, and Alitsipes were found to be the richest genera, while Actinomyces were found to encode the fewest XMEs, primarily metabolizing Diclofenac, a nonsteroidal anti-inflammatory drug. Overall, we discovered XMEs in 220 genera, exceeding the number experimentally reported in fewer than 10 genera. Notably, Eggerthella lenta's cgr2 involved in Digoxin inactivation was identified in very distant Holdemania genera, likewise Clostridium leptum's nitroreductase, involved in Nitrazepam metabolism, was found in Fusobacterium. These findings highlight the extensive and diverse distribution of XMEs across microbial taxa. 2025-01-01T00:00:00Z https://dspace.nii.res.in//https://dspace.nii.res.in/handle/123456789/1580 Title: SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes Authors: Debasisa, Mohanty; Khanduja, Akshay Abstract: Small proteins (≤100 amino acids) play important roles across all life forms, ranging from unicellular bacteria to higher organisms. In this study, we have developed SProtFP which is a machine learning-based method for functional annotation of prokaryotic small proteins into selected functional categories. SProtFP uses independent artificial neural networks (ANNs) trained using a combination of physicochemical descriptors for classifying small proteins into antitoxin type 2, bacteriocin, DNA-binding, metal-binding, ribosomal protein, RNA-binding, type 1 toxin and type 2 toxin proteins. We have also trained a model for identification of small open reading frame (smORF)-encoded antimicrobial peptides (AMPs). Comprehensive benchmarking of SProtFP revealed an average area under the receiver operator curve (ROC-AUC) of 0.92 during 10-fold cross-validation and an ROC-AUC of 0.94 and 0.93 on held-out balanced and imbalanced test sets. Utilizing our method to annotate bacterial isolates from the human gut microbiome, we could identify thousands of remote homologs of known small protein families and assign putative functions to uncharacterized proteins. This highlights the utility of SProtFP for large-scale functional annotation of microbiome datasets, especially in cases where sequence homology is low. SProtFP is freely available at http://www.nii.ac.in/sprotfp.html and can be combined with genome annotation tools such as ProsmORF-pred to uncover the functional repertoire of novel small proteins in bacteria. 2025-01-01T00:00:00Z https://dspace.nii.res.in//https://dspace.nii.res.in/handle/123456789/1577 Title: SG-ML-PLAP: A structure-guided machine learning- based scoring function for protein-ligand binding affinity prediction Authors: Pal, Sapna; Pal, Ankita; Debasisa, Mohanty Abstract: Computational methods to predict binding affinity of protein-ligand complex have been used extensively to design inhibitors for proteins selected as drug targets. In recent years machine learning (ML) is being increasingly used for design of drugs/inhibitors. However, ranking compounds as per their experimental binding affinity has remained a major challenge. Therefore, it is necessary to develop ML-based scoring function (MLSF) for predicting the binding affinity of protein-ligand complexes. In this work, protein-ligand interaction features, namely, extended connectivity interaction fingerprints (ECIF), derived from the PDBbind dataset have been used to build ML models for binding affinity prediction. The benchmarking has been done on the Comparative Assessment of Scoring Functions (CASF) dataset and also by predicting the binding affinity of unseen protein-ligand complexes which have structural features different from those present in the training dataset. Furthermore, an improvement in the performance of MLSF on the redocked CASF complexes generated by AutoDock Vina software was seen when the training set consisting of crystal structures was supplemented with redocked protein-ligand complexes. The MLSF trained on crystal structures alone using a combination of ECIF and VINA features also predicted binding affinities of crystal as well as docked complexes with high accuracy. Overall, the MLSF developed in this work shows improved performance compared to conventional SFs and several other MLSFs. It will be a valuable resource for identifying novel inhibitors by structure-based virtual screening protocols. The proposed MLSF SG-ML-PLAP (Structure-Guided Machine-Learning-based Protein-Ligand Affinity Predictor) is freely accessible as a webserver, http://www.nii.ac.in/sg-ml-plap.html. 2025-01-01T00:00:00Z https://dspace.nii.res.in//https://dspace.nii.res.in/handle/123456789/1532 Title: Allosteric regulation of the inactive to active state conformational transition in CDPK1 protein of Plasmodium falciparum Authors: Gupta, Priya; Mohanty, Debasisa Abstract: The aim of the current study is to investigate the role of the CAD domain in the activation mechanism of calcium dependent protein kinase-1 of Plasmodium falciparum (PfCDPK1) and explore the possibility of allosteric inhibition of this kinase. PfCDPK1 belongs to CDPK family of apicomplexan kinases which have a C-terminal CAD domain. Microsecond scale MD simulations were performed on modeled structures of complete PfCDPK1 and its kinase domain alone. The simulations revealed that in absence of CAD the salt bridge between Glu116 in αC-helix and Lys85 in β3-sheet of kinase breaks after 200 ns resulting in inactive conformation of the kinase, but the salt bridge stays intact in the complete protein stabilizing it in active conformation. These results highlight the novel CAD mediated allosteric stabilization of the crucial salt bridge which is a hallmark of active conformation of kinase domains. The mechanistic details of the allosteric activation revealed by our study, opens up the possibility for design of allosteric inhibitors of PfCDPK1 kinase by disrupting the kinase:CAD interactions. Using a combination of machine learning and structure-based in silico screening, we have identified novel PPI modulators for allosteric inactivation of PfCDPK1 kinase. 2022-01-01T00:00:00Z