A Multi-class Machine Learning Framework to Predict Ampicillin-Sulbactam Resistance of Acinetobacter baumannii

Acinetobacter baumannii is a serious pathogen responsible for many of the hospital-acquired infections. The emergence of multi-drug and pan-drug resistant strains of A. baumannii has been a growing concern. Ampicillin-sulbactam combination has proven to be effective in treatment of several resistant strains. However, strains resistant to ampicillin-sulbactam combination have also emerged necessitating other combination therapy. Rapid and accurate identification of the phenotype of the organism is essential for starting the right treatment. To this end, genome-based approaches have garnered much attention. In this work, we report a multi-class machine-learning based approach to predict the ampicillin-sulbactam resistance phenotype and MIC of Acinetobacter baumannii based on the presence/absence of AMR genes in the genome of strains isolated in the USA region. Our model achieves an accuracy of about 94% indicating that the gene presence/absence itself can capture the resistance phenotype.  Further, we show that our model, built based on the USA strains, does not predict reliably the AMR phenotypes of Indian isolates pointing to the need for building machine learning models from region-specific data

Genome inventory and analysis of nuclear hormone receptors in Tetraodon nigroviridis

Nuclear hormone receptors (NRs) form a large superfamily of ligand-activated transcription factors, which regulate genes underlying a wide range of (patho) physiological phenomena. Availability of the full genome sequence of Tetraodon nigroviridis facilitated a genome wide analysis of the NRs in fish genome. Seventy one NRs were found in Tetraodon and were compared with mammalian and fish NR family members. In general, there is a higher representation of NRs in fish genomes compared to mammalian ones. They showed high diversity across classes as observed by phylogenetic analysis. Nucleotide substitution rates show strong negative selection among fish NRs except for pregnane X receptor (PXR), estrogen receptor (ER) and liver X receptor (LXR). This may be attributed to crucial role played by them in metabolism and detoxification of xenobiotic and endobiotic compounds and might have resulted in slight positive selection. Chromosomal mapping and pairwise comparisons of NR distribution in Tetraodon and humans led to the identification of nine synthenic NR regions, of which three are common among fully sequenced vertebrate genomes. Gene structure analysis shows strong conservation of exon structures among orthologoues. Whereas paralogous members show different splicing patterns with intron gain or loss and addition or substitution of exons played a major role in evolution of NR superfamily

GROMACS Simulation Settings & Files

This folder contains the simulation parameter files for the EcoR

Replicate Simulation Files

This dataset contains files pertaining to the replication simulations done for the work on Effect of osmolytes on free EcoRI

Entropy Calculation Scripts

This set of scripts is based on the 2PT scheme to calculate entropy of water from MD simulations. Read the citation for more details

Proinflammatory Peptides

This file contains supplementary information - on model parameters - used for the study "Distributed Reduced Alphabet Representation for Predicting Proinflammatory Peptides

Results

Conformational parameters and significance plot