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Biswal, M. R.

Publications and source records attributed to Biswal, M. R..

2 recordsLinked to original sources

One drug multiple targets: An approach to predict drug efficacies on bacterial strains differing in membrane composition

Rational design methodologies such as quantitative structure activity relationships (QSAR) have conventionally focused on screening through several drugs for their activity against a single target, either a bacterial protein or membrane. Recent concerns in drug design such as the development of drug resistance by membrane adaptation, or the undesirable damage to gut microbiota require a paradigm shift in activity prediction. A complementary approach capable of predicting the activity of a single drug against diverse targets, the diversity arising from bacterial adaptation or a heterogeneous composition with other helpful or harmful bacteria, is needed. As a first predictive step towards this goal, we develop a quantitative model for the activity of daptomycin on Streptococcus aureus strains with different membrane compositions, mainly varying in lysylation. The results of the predictions are good, and within the limits of the scarcely available data, hint at an interaction of daptomycin with the inner membrane. The complementary approach may in principle be extended to estimate the activity against gut bacterial membranes, when systematic data can be curated for training the model.

biochemistry

Computationally designing antimicrobial peptides for Acinectobacter Baumannii

Acinetobacter Baumannii, which is mostly contracted in hospital stays, has been developing resistance to all available antibiotics, including the last line of drugs, such as carbapenem. Because of its quick adaptation there is an immediate need to design new antibiotics, possibly antimicrobial peptides (AMPs) to which bacteria do not develop resistance easily. Our threefold goal was to curate the available activity of AMPs on the same strain of A. Baumannii, build a neural network model for predicting their activity and use it to rationally pre-screen for lead generation from the thousands of naturally occurring AMPs. By curating and analyzing the recent activity data from 81 AMPs on ATCC 19606 strain, we develop a quantitative AMP activity prediction model. We selected three other models with comparable performance against a test set with known activities. With the goal of inspiring further studies on AMP drug candidates and their rational shortlisting, we made activity predictions for the entire database of AMPs using all the models. To handle the uncertainty of training with a small data set, highlighted peptides which had consistent results from all models.

biochemistry