Aarya Venkat

Glydentify predicts donor substrates for glycosyltransferases using deep learning, achieving high accuracy and providing explainable insights into enzyme-substrate relationships.

The paper introduces a toolkit using machine learning to predict ice binding protein potential, activity, and expression from sequences, achieving high accuracy and scalability.

This study investigates how redox regulation via cysteine oxidation controls BRSK1/2 kinase activity, revealing conserved mechanisms that modulate catalytic function through disulfide bond formation and salt bridge disruption.

This paper introduces an alignment-free protein classification framework using sequence embeddings to infer evolutionary relationships and functional motifs across diverse protein superfamilies.

The paper introduces QSMART, a model that predicts protein kinase inhibitor response by combining statistical and neural network methods, achieving high accuracy and feature explainability.

The paper uses evolutionary analysis of GT-A fold sequences to identify core components and hypervariable loops contributing to catalytic diversity, and develops a machine learning classifier for donor specificity prediction.