Rahil Taujale

The paper identifies and classifies ion channels across species, revealing functional insights into understudied CALHM channels through sequence, structure, and functional data integration.

PyINETA integrates INADEQUATE and 13C-JRES NMR data to improve compound annotation in metabolomics, showing tissue-specific metabolite distribution and handling unknown compounds.

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

This paper introduces a deep learning model to predict glycosyltransferase folds using secondary structure features, identifying novel fold families and improving fold classification.

This paper refines the definition of the human kinome, identifies understudied kinases, and highlights their potential as drug targets through differential expression, essentiality, and mutation data.

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.