Paul M. Levine

This paper designs soluble, miniaturized MHC proteins that enable stable expression in E. coli and retain peptide and TCR binding specificity.

This paper introduces RFpeptides, a deep learning framework for designing high-affinity macrocyclic peptides, achieving sub-nanomolar binding affinities and accurate structural predictions.

The paper designs nanomolar binders for diverse small molecules using a pseudocycle-based shape complementarity method, enabling direct conversion into sensing systems.

The paper designs peptide traps that bind amyloidogenic segments in {beta}-strand and {beta}-hairpin conformations, effectively blocking fibril assembly for A{beta}42.