Design of Protein-Protein Interactions with a Novel Ensemble-Based Scoring Algorithm

Protein-protein interactions (PPIs) are vital for cell signaling, protein trafficking and localization, gene expression, and many other biological functions. Rational modification of PPI targets provides a mechanism to understand their function and importance. However, PPI systems often have many more degrees of freedom and flexibility than the small-molecule binding sites typically targeted by protein design algorithms. To handle these challenging design systems, we have built upon the computational protein design algorithm K* [8,19] to develop a new design algorithm to study protein-protein and protein-peptide interactions. We validated our algorithm through the design and experimental testing of novel peptide inhibitors. Previously, K* required that a complete partition function be computed for one member of the designed protein complex. While this requirement is generally obtainable for active-site designs, PPI systems are often much larger, precluding the exact determination of the partition function. We have developed proofs that show that the new K* algorithm combinatorially prunes the protein sequence and conformation space and guarantees that a provably-accurate epsilon-approximation to the K* score can be computed. These new proofs yield new algorithms to better model large protein systems, which have been integrated into the K* code base. K* computationally searches for sequence mutations that will optimize the affinity of a given protein complex. The algorithm scores a single protein complex sequence by computing Boltzmann-weighted partition functions over structural molecular ensembles and taking a ratio of the partition functions to find provably-accurate e-approximations to the K* score, which predicts the binding constant. The K* algorithm uses several provable methods to guarantee that it finds the gap-free optimal sequences for the designed protein complex. The algorithm allows for flexible minimization during the conformational search while still maintaining provable guarantees by using the minimization-aware dead-end elimination criterion, minDEE. Further pruning conditions are applied to fully explore the sequence and conformation space. To demonstrate the ability of K* to design protein-peptide interactions, we applied the ensemble-based design algorithm to the CFTR-associated ligand, CAL, which binds to the C-terminus of CFTR, the chloride channel mutated in human patients with cystic fibrosis. K* was retrospectively used to search over a set of peptide ligands that can inhibit the CAL-CFTR interaction, and K* successfully enriched for peptide inhibitors of CAL. We then used K* to prospectively design novel inhibitor peptides. The top-ranked K*-designed peptide inhibitors were experimentally validated in the wet lab and, remarkably, all bound with mu M affinity. The top inhibitor bound with seven-fold higher affinity than the best hexamer peptide inhibitor previously available and with 331-fold higher affinity than the CFTR C-terminus.