DisruPPI: structure-based computational redesign algorithm for protein binding disruption

Abstract

MotivationDisruption of protein–protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein–Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.ResultsTwo existing methods for predicting mutational effects on binding, FoldX and INT5, were demonstrated to be quite precise in selecting disruptive mutations from the SKEMPI and AB-Bind databases of experimentally determined changes in binding free energy. DisruPPI was implemented to use an INT5-based disruption score integrated with an AMBER-based stability assessment and was applied to disrupt protein interactions in a set of different targets representing diverse applications. In retrospective evaluation with three different case studies, comparison of DisruPPI-designed variants to published experimental data showed that DisruPPI was able to identify more diverse interaction-disrupting and stability-preserving variants more efficiently and effectively than previous approaches. In prospective application to an interaction between enhanced green fluorescent protein (EGFP) and a nanobody, DisruPPI was used to design five EGFP variants, all of which were shown to have significantly reduced nanobody binding while maintaining function and thermostability. This demonstrates that DisruPPI may be readily utilized for effective removal of known epitopes of therapeutically relevant proteins.Availability and implementationDisruPPI is implemented in the EpiSweep package, freely available under an academic use license.Supplementary information Supplementary data are available at Bioinformatics online.

Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition

“…We consider these as the experimental evaluation of the hot residues from the antigenic heatmap. We note that, in other settings, mutations (individual or combination) could be computationally designed to evaluate the disruption of binding while preserving antigenic stability [ 9 , 61 ]. Thus, while we used a large number of experimental measurements in this study, we expect that a much smaller number of tests would suffice in practice, e.g., 3–5 variants sufficed to localize individual Ab epitopes in previous computationally directed studies [ 9 ].…”

“…As presented, mutations are selected to assess the predicted antigenic hot spots and deconvolve which hot spots are associated with which Abs. While this has been done before based simply on maximizing binding disruption according to the models [ 9 , 61 ], more refined metrics and optimization techniques could be developed, e.g., using an information-theoretic approach to maximize what is learned about the communities and hotspots for a given experimental “budget” (e.g., maximum-relevance, minimum-redundancy) [ 68 ]. Revised dock bins and antigenic hotspots.…”

A sound strategy for homology modeling-based affinity maturation of a HIF-1α single-domain intrabody

“…In this case, developing a method or a sound strategy to focus on nanobody affinity maturation is an urgent problem that must be solved. With the growth of antibody-antigen complex structural databases and the development of algorithms for binding affinity prediction, computer-aided rational design has been a tractable choice for the maturation of antibody affinity characteristics (30)(31)(32)(33).…”

A Chimeric Antigen Receptor That Binds to a Conserved Site on MICA

“…EpiScope (25,26) was employed to identify the B2 epitope on MICA. B2 was modeled using the Prediction of ImmunoGlobulin Structure modeling server (27).…”

“…We have shown in an earlier study that the B2 scFv and NKG2D bound to different sites on MICA using competitive binding assays (7). To localize potential B2 scFv binding sites on MICA, we used EpiScope (25,26) to design mutational variants of MICA so as to test possible B2/MICA binding modes. The ClusPro webserver was used to hypothesize binding modes, generating 30 docking models of B2 to MICA (30).…”