PROTEAN-CR

PROTEAN-CR: A proteomics toolkit that integrates structural modeling and analysis in cancer research

Structural Computational Biology

Structural modeling and analysis of cancer-related proteins

Developing tools in the realm of structure-based computational drug design

Structural Immunoinformatics

Structural Modeling and Analysis of T-cell mediated Immunity

Modeling, predicting and identifying peptide targets with structurally-aware methodologies

We develop tools that aim to include protein flexibility in structural bioinformatics pipelines and enable better drug discovery for cancer-related proteins:

EnGens: Generating Representative Ensembles

Given a static or dynamic dataset of protein conformations, EnGens is able to analyze conformational diversity of the datasets and generate representative ensembles.

DINC-Ensemble: Docking large ligands incrementally to ensembles of receptors

Given a representative ensemble of protein conformations and a ligand, DINC-Ensemble docks the ligand to the ensemble in parallel incremenrally.

DINC-Covid: Ensemble docking of ligands to SARS-Cov2 related proteins

Given a ligand of interest, DINC-Covid docks the ligand to a predefined ensemble of SARS-Cov2 related proteins.

We have developed a list of tools pertaining to the peptide target identification workflows:

APE-Gen2.0: pMHC Structural Modeling

Given a peptide amino acid sequence and an MHC type, APE-Gen2.0 is are able to repidly provide a ensemble of pMHC structures.

3pHLA: Structure-derived pMHC binding affinity predictions

Given a pMHC structure, 3pHLA can acurately predict binding affinities and prioritize good peptide binders.

HLA-Arena: Customizable peptide-MHC structure analysis

HLA-Arena provides a customizable environment for the structural modeling and analysis of peptide-HLAs.

PepSim: T-cell Cross-reactivity prediction

Given two or more pMHC complexes, PepSim predicts T-cell cross-reactivity.