Reda Rawi

VIProDesign is a computational tool that designs viral protein panels to balance diversity and size, enabling better evaluation of immune responses and identification of broadly neutralizing antibodies.

This study designs a dual-epitope vaccine targeting HIV-1 Env's fusion peptide and V2 apex, showing it elicits neutralizing antibodies against both sites in rhesus macaques.

BRAVE predicts HIV-1 resistance to 33 bNAbs using a Random Forests classifier with a protein language model, outperforming existing tools in accuracy and other metrics.

GLYCO-2 improves computational efficiency for quantifying glycan shielding of glycoproteins, revealing increased shielding in influenza HA proteins over time.

This study compares vaccine-elicited and naturally elicited antibodies' recognition of HIV-1 fusion peptide sequences, finding that naturally elicited antibodies show greater tolerance to sequence variation.

This study identifies an occluded-intermediate state in the HIV-1 Env trimer transition pathway using molecular dynamics and smFRET, revealing new structural features and antibody recognition.

The paper develops machine learning classifiers to predict HIV-1 resistance to 33 neutralizing antibodies with 96% accuracy using clinical isolates.

The paper analyzes HIV-1 broadly neutralizing antibodies to classify epitopes and identify potential vaccine candidates based on structural and immunological features.

The paper identifies cancer gene biomarkers associated with drug sensitivity and provides open-source tools for analysis.

The paper applies high-dimensional statistical and network visualization techniques to Arab diabetes and obesity data to identify key biomarkers and interactions associated with the diseases.