Molecular modeling and machine learning for protein structures and interactions

PROJECT SUMMARY / ABSTRACT
Structural biology provides a powerful lens through which to view living systems. With advances in algorithms
and computing, molecular simulations have begun to complement traditional experimental approaches as tools
for discovery. At the same time, data-intensive machine learning approaches are becoming increasingly
important in biology, fueled by the rapid growth in high-throughput experimentation. Research in my laboratory
applies techniques from structural biology, molecular simulation, and machine learning to design new protein
structures and predict protein interactions. We design new protein structures in order to better understand the
principles of protein folding and to create highly stable and robust molecular scaffolds for a range of biomedical
applications including multivalent display of binding or signaling domains, hosting of binding or catalytic sites,
and use as building blocks to assemble higher-order complexes. We predict protein interactions in order to
better understand the principles of macromolecular recognition and to gain insight into the process by which
the adaptive immune system discriminates self from non-self in the context of infectious and autoimmune
diseases and cancer. Our research during the project period will be directed toward two broad goals: de novo
design and functionalization of tandem repeat proteins, and prediction of peptide-MHC recognition by T cell
receptors (TCRs). The proposed protein design work builds on our recent progress designing circular tandem
repeat proteins with a range of repeat numbers and diameters and applying these designs as multivalent
display scaffolds for the presentation of binding and signalling domains. Our TCR studies leverage the tools we
have recently developed to model—structurally and bioinformatically—repertoires of T cell receptors and their
peptide:MHC specificity. Looking ahead, I am optimistic that by combining atomically-detailed molecular
simulations and data-intensive machine learning techniques we will be able to generate designed protein
constructs and predictive algorithms that have a significant positive impact on human health.

Grant Number: 5R35GM141457-06
NIH Institute/Center: NIH
Principal Investigator: Philip Bradley