J. R. Jeliazkov

The paper describes the engineering of high-affinity human antibodies that recognize gluten epitopes bound to HLA-DQ2.5 and inhibit T-cell activation in celiac disease.

This paper introduces two computational methods for accurately positioning functional residues in proteins and validates them by redesigning enzyme active sites.

This paper improves RosettaAntibody and Rosetta SnugDock by simplifying interfaces, expanding templates, and enhancing docking accuracy through benchmarking.

The paper identifies a compact vocabulary of paratope-epitope interaction motifs that enables machine learning prediction of antibody-antigen binding.

The paper improves Rosetta docking methods for complex targets in CAPRI Rounds 37-45, achieving successful predictions and refining models for symmetric and carbohydrate-containing complexes.

This paper develops a computational method to improve protein crystal resolution by designing point mutations, finding that resolution can be enhanced and space group altered through such mutations.

The paper analyzes the flexibility of CDR-H3 loops in antibodies to assess if affinity maturation leads to rigidification, finding only a slight decrease in flexibility.

The paper explains the Rosetta all-atom energy function, its mathematical models, and its application to biomolecular modeling and design, including new capabilities for membrane proteins and other macromolecules.