Ratul Chowdhury

FluxSprout identifies metabolic dependencies in zebrafish sprouting angiogenesis by integrating single-cell RNA sequencing with genome-scale metabolic modeling, revealing biosynthetic capacity as a key feature of sprouting vessels.

Quality-controlled, cycle-certified E-Flux Human-GEM models of twelve regions from one prostate tumor curb implementation-driven flux inflation and show predicted metabolic similarity tracks spatial region, not histology.

OpenKinetics Predictor unifies thirteen enzyme kinetic predictors behind one interface and API; on E. coli iML1515, eight kcat predictors correlate pairwise at r 0.08 to 0.67.

RealKcat predicts enzyme kinetics from sequence using a curated dataset and embeddings, showing sensitivity to catalytic mutations and robustness across evaluations.

This paper shows that kinase inhibitors commonly target multiple kinases, independent of approval status, and highlights the need for better understanding of their polypharmacology.

The paper develops a computational framework to analyze cyclic peptides, integrating data, improving sequence alignment, simulating thermal stability, and predicting melting points and metal binding.

The paper develops a scoring system to identify effective anti-methanogenic compounds by analyzing their structural and metabolic properties.

This paper introduces EscaPRRS, a Bayesian variational autoencoder that predicts antibody escape propensities of PRRSV GP5 mutants using structural and sequence data, showing strong correlation with binding affinities.

The paper develops a method to classify hydrolase functions using structural similarity of reaction cores, identifying enzyme backbones that can support multiple catalytic activities.

This study investigates metabolic shifts in alveolar macrophages during M1 and M2 activation phases, highlighting non-central carbon metabolism pathways and temperature's role in immune response.

This paper uses molecular simulations and a graph neural network to identify potential methanogenesis inhibitors and their biochemical properties.

This paper introduces RGN2, a deep learning model that predicts protein structure from single sequences without MSAs, outperforming AlphaFold2 and RoseTTAFold on orphan proteins and achieving faster computation.

This study shows Atg11, an autophagy-related protein, regulates microtubule dynamics and chromosome segregation in yeast by affecting spindle pole body function and Kar9-dependent spindle positioning.

This paper improves AraC-based biosensors for triacetic acid lactone and orsellinic acid by reducing background expression, enhancing sensitivity, and increasing specificity through dual-selection and computational methods.