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

Abstract

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 ε-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 ε-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 μM 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.

Abbreviations used: PPI, protein-protein interaction; CFTR, Cystic fibrosis transmembrane conductance regulator; CAL, CFTR-associated ligand; DEE, Dead-end elimination; MC, Monte Carlo; CF, cystic fibrosis; NHERF1, Na⁺/H⁺ Exchanger Regulatory Factor 1; GMEC, global minimum energy conformation; BLU, biochemical light unit; ROC, receiver operating curve; AUC, area under the curve.

This work is supported by the following grants from the National Institutes of Health: R01 GM-78031 to B.R.D. and R01 DK075309 to D.R.M.