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

TitleDisruPPI: structure-based computational redesign algorithm for protein binding disruption.
Publication TypeJournal Article
Year of Publication2018
AuthorsChoi Y, Furlon JM, Amos RB, Griswold KE, Bailey-Kellogg C
JournalBioinformatics
Volume34
Issue13
Paginationi245-i253
Date Published2018 Jul 01
ISSN1367-4811
Abstract

Motivation: Disruption 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.

Results: Two 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 implementation: DisruPPI is implemented in the EpiSweep package, freely available under an academic use license.

Supplementary information: Supplementary data are available at Bioinformatics online.

DOI10.1093/bioinformatics/bty274
Alternate JournalBioinformatics
PubMed ID29949961
PubMed Central IDPMC6022686
Grant ListR01 GM098977 / GM / NIGMS NIH HHS / United States