Projects

A lightweight Web Server and Python tool for comparing UniProt sequences with sequences extracted from 3D structures.

A machine learning structure-based protocol for predicting binding affinity of peptides to HLA receptors.

DINC-Ensemble takes as input an ensemble of receptors and a ligand. It performs incremental meta-docking to find the most favorable pose of the ligand inside the binding pocket of the given receptors.

EnGens is a tool for end-to-end processing of large protein structural datasets with the aim of generating and analyzing representative protein conformational ensembles.

A pipeline for structural analysis of class I HLA proteins and their binding to the self peptides.

Examining bias in peptide HLA binding affinity prediction datasets and models.