HgutMgene-Miner: In silico genome mining tool for deciphering the drug-metabolizing potential of human gut microbiome.

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.

Influence of 1-Hexanol/Waste Plastic Oil Blends on Combustion, Performance, and Emission Characteristics of a Common Rail Direct Injection Diesel Engine.

Waste plastic oils (WPOs) can help address the global energy crisis caused by the rapid depletion of fossil fuels, global warming, and strict emission regulations. The present research delves into the intricate interplay of higher alcohol blends in the context of combustion, performance, and emission characteristics within a common rail direct injection engine. In this regard, 1-hexanol has been selected as the blending constituent for the WPO to tackle emission challenges while concurrently reducing dependence on conventional fuel, as it stands out for its enhanced fuel properties compared to lower alcohols.

SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes.

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.

Whole-genome sequencing of novel pathogenic and multidrug-resistant , causing mortality in fish species ().

We present a novel pathogenic and multidrug-resistant isolated from . The bacterium belongs to the Micrococcales order and has a genome consisting of 2.59 Mb in length and 71.

SG-ML-PLAP: A structure-guided machine learning-based scoring function for protein-ligand binding affinity prediction.

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.