Computational Design of Selective Peptides to Discriminate between Similar PDZ Domains in an Oncogenic Pathway

Abstract

Reagents that target protein-protein interactions to rewire signaling are of great relevance in biological research. Computational protein design may offer a means of creating such reagents on demand, but methods for encoding targeting selectivity are sorely needed. This is especially challenging when targeting interactions with ubiquitous recognition modules—e.g., PDZ domains, which bind C-terminal sequences of partner proteins. Here we consider the problem of designing selective PDZ inhibitor peptides in the context of an oncogenic signaling pathway, in which two PDZ domains (NHERF-2 PDZ2—N2P2 and MAGI-3 PDZ6—M3P6) compete for a receptor C-terminus to differentially modulate oncogenic activities. Because N2P2 increases tumorigenicity and M3P6 decreases it, we sought to design peptides that inhibit N2P2 without affecting M3P6. We developed a structure-based computational design framework that models peptide flexibility in binding, yet is efficient enough to rapidly analyze tradeoffs between affinity and selectivity. Designed peptides showed low-micromolar inhibition constants for N2P2 and no detectable M3P6 binding. Peptides designed for reverse discrimination bound M3P6 tighter than N2P2, further testing our technology. Experimental and computational analysis of selectivity determinants revealed significant indirect energetic coupling in the binding site. Successful discrimination between N2P2 and M3P6, despite their overlapping binding preferences, is highly encouraging for computational approaches to selective PDZ targeting, especially because design relied on a homology model of M3P6. Still, we demonstrate specific deficiencies of structural modeling that must be addressed to enable truly robust design. The presented framework is general and can be applied in many scenarios to engineer selective targeting.

Computationally Designed Bispecific Antibodies using Negative State Repertoires

“…It involves aspects of both positive design , as the AB interaction must be favored, and negative design (Hecht et al, 1990), as the AC interaction must be disfavored. Multistate design (MSD) (Davey and Chica, 2012), which designs for multiple protein states simultaneously, has proven itself useful in designing a single protein sequence to adopt multiple conformations (Ambroggio and Kuhlman, 2006; Fromer et al, 2009), in understanding what kind of sequences can adopt multiple conformations (Babor et al, 2011; Humphris and Kortemme, 2007; Willis et al, 2013), and in designing specificity such as when designing a protein to bind one target but to avoid another (Ashworth et al, 2010; Grigoryan et al, 2009; Zheng et al, 2014) or when organizing multimeric assemblies (Fallas and Hartgerink, 2012; Havranek and Harbury, 2003; Lewis et al, 2014). …”

Tertiary structural motif sequence statistics enable facile prediction and design of peptides that bind anti-apoptotic Bfl-1 and Mcl-1

“…Future design studies will help calibrate the methods so that diverse sequences can be obtained with reliably high success rates. Combining dTERMen with a post-analysis procedure that includes all-atom modeling with aggressive conformational search, using peptide redocking [51] or MD simulation [52], could be one way to recognize sequences or mutations that can or cannot be accommodated. Although this would increase the computational costs, such a secondary evaluation could be performed for a modest number of promising candidates designs.…”

Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity

“…Multistate design is particularly challenging when designing positively for some criteria but negatively against others (33), requiring that the goals be treated separately instead of simply combined into a single goal. A variety of algorithmic approaches, based on explicit structural modeling (34-37), sequence potentials derived from the evolutionary record (15), and sequence potentials derived from structural modeling (38), have driven applications such as modifying protein interaction specificity (39)(40)(41), designing peptide inhibitors (42)(43)(44), altering substrate specificity (45)(46)(47), characterizing resistance mechanisms (48), and enabling a single protein to adopt multiple folds (49). We have focused on a Pareto optimization framework (50) as the basis for elucidating and explicitly optimizing trade-offs between criteria and thereby providing the recently well-discussed advantages of provable optimality guarantees, such as enabling more direct interpretation of experimental data according to the driving models without concern for algorithmic bias or sampling failure (17).…”