Karuna Anna Sajeevan

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.

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.