Efficient New Computational Protein Design Algorithms, with Applications to Drug Resistance Prediction and HIV Antibody Design

Abstract

Proteins are essential for myriad biological functions, including DNA replication, molecular transport, catalysis, and antigen recognition. Protein function is determined by three dimensional structure, which is largely determined by amino acid composition. The functional diversity of known proteins suggests that nature can support a much larger set of proteins than is currently available. Protein design aims to explore the space of possible proteins in order to create new proteins with novel or improved biological functions. Two key challenges in protein design, however, are the astronomically large number of possible protein sequences, along with the vast conformation space spanned by each protein. Computational structure-based protein design (CPD) enables the prediction of proteins with desired biochemical properties. A practical CPD method must not only efficiently tackle large sequence and conformation spaces but also use a computationally tractable yet biophysically realistic model of protein plasticity. To this end, I have developed algorithms that accurately and more efficiently search large sequence and conformational spaces to compute proteins that satisfy binding affinity, specificity, and stability requirements. Crucially, my algorithms maintain the state-of-the-art in protein design, namely: provable guarantees, continuous flexibility, and ensemble-based scoring. I applied my algorithms to two biomedically relevant problems: (i) prediction of drug resistance mutations that arise in response to four pre-clinical antibiotics, and (ii) the re-design of a monoclonal HIV antibody for improved potency and breadth of neutralization.