Nishant Kumar

HAIRpred2 predicts antibody-interacting residues in human antigens using structural features, achieving an AUC of 0.78 on an independent test set.

HAIRpred predicts human antibody-interacting residues in antigens using machine learning and features like evolutionary profiles and surface accessibility, achieving an AUC of 0.72.

This paper develops models to predict and design proteins with desired melting temperatures using machine learning and large language models, achieving high accuracy and providing tools for application.

PEPlife2 is an updated manually curated database with 4,412 peptide half-life entries, including structural data and advanced analysis tools.

HemoPI2.0 predicts peptide hemolysis and pHC50 from sequence; a hybrid of MERCI motif scoring and ESM2-t6 reaches AUC 0.909 on held-out peptides, beating the earlier tools tested.

The paper develops a hybrid model combining alignment-based and alignment-free methods to predict plant resistance proteins, achieving high AUROC scores and creating an online platform for this purpose.

This paper introduces HLA-DR4Pred2, an improved method for predicting HLA-DRB1*04:01 binders using a larger dataset and hybrid approaches, achieving higher AUC scores than existing methods.

This paper develops a machine learning model to predict antifreeze proteins using evolutionary information, achieving higher accuracy than existing methods on an independent dataset.

The paper develops a model to predict MIC values of peptides against E. coli using machine learning and a web server for peptide design.

The paper develops a hybrid model for predicting both linear and conformational B-cell epitopes using machine learning and BLAST, achieving an AUC of 0.83 on an independent dataset.

This paper develops methods to predict and design T1DM-associated peptides, achieving high accuracy with a hybrid approach and a webserver for peptide prediction.