Hamed Khakzad

SIMORGH predicts ligand binding sites from apo proteins by integrating conformational ensembles, outperforming existing methods in ligand-free settings and identifying cryptic pockets.

BindCORE improves prediction of interaction sites in intrinsically disordered regions by integrating biophysical ensemble features, outperforming sequence-based methods.

The paper introduces PROTEUS, a method to assess protein conformational plasticity using a generative model, showing it correlates with molecular dynamics data and identifies functionally plastic proteins.

The paper identifies 4,500 targetable sites on the human surfaceome and introduces binder seeds for protein design, validated through experimental approaches and supported by a database.

DynamicGT improves protein binding interface prediction by incorporating dynamic features and conformational changes, outperforming static models on flexible and disordered regions.

DynaRepo provides a repository of macromolecular conformational dynamics with simulations and analyses for over 450 complexes and 270 proteins.

ppIRIS predicts bacterial protein-protein interactions using deep learning, achieving high accuracy and enabling proteome-wide screening, with applications in identifying virulence pathways and host-pathogen interactions.

This paper introduces a novel MS score framework for analyzing polyclonal antibodies, demonstrating its ability to identify antibody subgroups and predict interactions.

The paper uses molecular dynamics simulations to analyze Spike-ACE2 binding across SARS-CoV-2 variants, revealing conformational changes and variant-specific binding differences.

This study shows that streptolysin O enhances plasminogen to plasmin conversion by interacting with plasminogen and altering its conformation to increase sensitivity to proteolytic processing.

This paper inverts the AlphaFold2 network to perform de novo protein design, showing that while the method generates viable sequences with correct folds, it requires post-design optimization to achieve optimal surface properties.

This study develops multienzyme deep learning models for de novo peptide sequencing from mass spectrometry data, showing improved generalizability and sequence coverage across diverse protease digests and species.

This study determines the structural interactions between Streptococcus pyogenes M1 protein and human IgG using integrative structural biology, revealing conserved mechanisms to inhibit phagocytosis.

This paper accelerates protein structure prediction using GPU-accelerated particle swarm optimization, achieving 34x speedup over CPU with acceptable precision.