Neeladri Sen

The paper predicts and validates amino acid residues determining product specificity in plant oxidosqualene cyclases using structural and computational methods.

The paper predicts human and viral protein variants that enhance SARS-CoV-2 binding affinity and may increase COVID-19 susceptibility.

The paper characterizes structural and functional motifs of ATP-PPase substrate-specificity groups using structural data to inform antibacterial drug design.

The paper uses AlphaFold2 to analyze protein structures from 21 organisms, identifying 26 novel superfamilies and expanding structural diversity insights.

The paper introduces CATHe, a method using protein language model embeddings to detect remote homologues for CATH superfamilies, achieving high accuracy and validating predictions with AlphaFold structures.

The paper creates a structural database of 178,465 protein-protein and domain-domain interfaces, revealing similarities between interactions and potential therapeutic applications.

The paper predicts structures of disease-associated proteins without known structures, identifies functional sites, and explains 80% of mutations based on their proximity to these sites or structural impact.

The paper introduces FunSite, a machine learning method for predicting functional sites in proteins using sequence, structure, and evolutionary data from CATH functional families, showing superior performance over existing methods.

The paper predicts SARS-CoV-2 spike protein interactions with ACE2 orthologues across 215 vertebrates, finds mutations in ACE2 are more detrimental, and shows human strains are present in animals.

This paper predicts and designs therapeutics against Nipah virus by modeling its proteins, identifying peptide and small molecule inhibitors, and analyzing viral strain sequences for drug resistance.