1.
Computational design of selective peptides to discriminate between similar PDZ domains in an oncogenic pathway.
Zheng, F, Jewell, H, Fitzpatrick, J, Zhang, J, Mierke, DF, Grigoryan, G
Journal of molecular biology. 2015;(2):491-510
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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--for example, 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 has been shown to increase tumorigenicity and M3P6 to 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.
2.
Protein design with a comprehensive statistical energy function and boosted by experimental selection for foldability.
Xiong, P, Wang, M, Zhou, X, Zhang, T, Zhang, J, Chen, Q, Liu, H
Nature communications. 2014;:5330
Abstract
The de novo design of amino acid sequences to fold into desired structures is a way to reach a more thorough understanding of how amino acid sequences encode protein structures and to supply methods for protein engineering. Notwithstanding significant breakthroughs, there are noteworthy limitations in current computational protein design. To overcome them needs computational models to complement current ones and experimental tools to provide extensive feedbacks to theory. Here we develop a comprehensive statistical energy function for protein design with a new general strategy and verify that it can complement and rival current well-established models. We establish that an experimental approach can be used to efficiently assess or improve the foldability of designed proteins. We report four de novo proteins for different targets, all experimentally verified to be well-folded, solved solution structures for two being in excellent agreement with respective design targets.
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Design and designability of protein-based assemblies.
Zhang, J, Zheng, F, Grigoryan, G
Current opinion in structural biology. 2014;:79-86
Abstract
Design of protein-based assemblies is an exciting frontier in molecular engineering. It can be seen as an extension of the protein design problem, but with some added hurdles. In recent years, much of the focus in the field has been on patterning existing protein structural units (proteins, oligomers, or structural motifs) to design diverse assembly geometries, focusing on symmetry to encode both "infinite" lattices and finite-sized supramolecular particles. Despite impressive successes, several key challenges remain. Among these are the specificity problem the need to engineer preference for the intended assembly geometry over all alternatives, and the folding problem--understanding what thermodynamic or kinetic features of assembly subunits and inter-subunit interfaces lead to successfully folding superstructures and how to encode these in the amino-acid sequence. Here we focus on recent results in the context of these two problems, summarizing commonalities in successful approaches. We find that natural designability of assembly elements (i.e., their compatibility with diverse populations of natural amino-acid sequences) may be a unifying property of successful designs.