Puneet Rawat

The paper describes a computational workflow for designing NAZ-mAb variants targeting p95HER2, showing improved expression yields but weaker binding affinities compared to the parent antibody.

Amylo-Pipe integrates mechanistic and kinetic prediction tools for protein and peptide aggregation, offering a web interface for assessing aggregation behavior and supporting protein engineering.

The paper disentangles protein-interaction and protein-quality effects in antibody-antigen binding using deep mutational scanning and evaluates models for their ability to predict these effects.

The paper identifies TCR subsequence motifs associated with type 1 diabetes using deep learning and shows their enrichment in T1D individuals based on HLA risk alleles.

TCR2HLA infers HLA genotypes from TCR repertoires, enabling identification of immunologically relevant metaclonotypes and discovery of HLA-associated TCRs.

The paper quantitatively maps antigen specificity in immune repertoire embedding spaces, showing distinct regions for different antigen-specific sequences.

This study shows that the composition of training data, especially negative examples, affects machine learning model performance and biological rule discovery in antibody-antigen binding.

YAbS catalogues over 2,900 commercially sponsored antibody therapeutics, from clinical entry since 2000 to approval, with milestone-date filtering for pipeline, trend and phase-length analyses; over 450 are openly accessible.

The paper quantifies the plasticity of antibody developability by analyzing sequence and structure-based parameters in natural and human-engineered antibodies.

The paper shows that simple computational methods like BLOSUM can create diverse, binder-enriched antibody libraries better than deep learning approaches.