Simultaneous prediction of binding free energy and specificity for PDZ domain–peptide interactions

Crivelli, Joseph J.; Lemmon, Gordon; Kaufmann, Kristian; Meiler, Jens

doi:10.1007/s10822-013-9696-9

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…The structure-guided, energy-based discriminator has the advantage of being generalizable, relatively unbiased and is able to recapitulate key interactions that stabilize the peptidase -peptide interface as well as predict novel interactions not present in the training data. Success in using structure-based energetic signatures and molecular docking for binding partner identification has been achieved for several peptide recognition modules such as SH3 and PDZ domains (42)(43)(44)(45)(46), major histocompatibility complex (47) and for the enzymes methyltransferase (48), farnesyltransferase (35), and HIV protease (49,50). We show here that a structure-based approach, guided by the knowledge of mechanism, can be successfully integrated with machine learning to predict substrates for a mechanistically diverse enzyme family such as proteases with high accuracy.…”

Section: Discussionmentioning

confidence: 99%

Large-Scale Structure-Based Prediction and Identification of Novel Protease Substrates Using Computational Protein Design

Pethe

Rubenstein

Khare

2017

Journal of Molecular Biology

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Characterizing the substrate specificity of protease enzymes is critical for illuminating the molecular basis of their diverse and complex roles in a wide array of biological processes. Rapid and accurate prediction of their extended substrate specificity would also aid in the design of custom proteases capable of selectively and controllably cleaving biotechnologically or therapeutically relevant targets. However, current in silico approaches for protease specificity prediction, rely on, and are therefore limited by, machine learning of sequence patterns in known experimental data. Here, we describe a general approach for predicting peptidase substrates de novo using protein structure modeling and biophysical evaluation of enzyme-substrate complexes. We construct atomic resolution models of thousands of candidate substrate-enzyme complexes for each of five model proteases belonging to the four major protease mechanistic classes-serine, cysteine, aspartyl, and metallo-proteases-and develop a discriminatory scoring function using enzyme design modules from Rosetta and AMBER's MMPBSA. We rank putative substrates based on calculated interaction energy with a modeled near-attack conformation of the enzyme active site. We show that the energetic patterns obtained from these simulations can be used to robustly rank and classify known cleaved and uncleaved peptides and that these structural-energetic patterns have greater discriminatory power compared to purely sequence-based statistical inference. Combining sequence and energetic patterns using machine-learning algorithms further improves classification performance, and analysis of structural models provides physical insight into the structural basis for the observed specificities. We further tested the predictive capability of the model by designing and experimentally characterizing the cleavage of four novel substrate motifs for the hepatitis C virus NS3/4 protease using an in vivo assay. The presented structure-based approach is generalizable to other protease enzymes with known or modeled structures, and complements existing experimental methods for specificity determination.

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Section: Discussionmentioning

confidence: 99%

Large-Scale Structure-Based Prediction and Identification of Novel Protease Substrates Using Computational Protein Design

Pethe

Rubenstein

Khare

2017

Journal of Molecular Biology

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

Zheng

Jewell

Fitzpatrick

et al. 2015

Journal of Molecular Biology

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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.

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Simultaneous prediction of binding free energy and specificity for PDZ domain–peptide interactions

Cited by 2 publications

References 45 publications

Large-Scale Structure-Based Prediction and Identification of Novel Protease Substrates Using Computational Protein Design

Large-Scale Structure-Based Prediction and Identification of Novel Protease Substrates Using Computational Protein Design

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

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