SNAPP, CRACLe, PoPP: Predicting Protein Interactions

Bush, Stephen James

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March 20, 2019

Creator

Bush, Stephen James
- Affiliation: School of Medicine, Curriculum in Bioinformatics and Computational Biology

Abstract

Protein-Protein Interactions (PPIs) play a central role in all major signaling events that occur in living cells, from DNA replication to complex, post-translational protein-signaling systems. However, many if not most pairs of interacting proteins remain unknown, and the ability to identify and predict protein-protein interaction sites is a key component in systems and structural biology. Computational techniques such as MD simulations and homology- or template-based modeling constitute the main bioinformatics methods applied to study PPIs, and despite many recent developments, fast and reliable predictions of PPI sites remains a challenge. Using computational geometry, we have developed two novel, geometry-based scoring function called Simplicial Neighborhood Analysis of Protein Packing (SNAPP) for the task of analyzing and predicting protein interactions. SNAPP-Surface calculates the likelihood that an amino acid on the surface of a protein will participate in a protein interaction. SNAPP-Surface is used in our novel algorithm and software for predicting protein-protein and protein-peptide binding sites called Critical Residue Analysis and Complementarity Likelihood (CRACLe). CRACLe was designed for accurate and efficient high-throughput screening of individual proteins for potential binding sites. CRACLe can be effectively applied to identify putative binding sites for novel proteins and potentially for building protein-protein networks. SNAPP-Interface is used in our novel protein-peptide docking algorithm called Prediction of Protein-peptide Packing (PoPP) to evaluate protein-peptide interactions. SNAPP-Interface is also useful for discriminating between native-like and decoy protein-protein interactions. The SNAPP, CRACLe, and PoPP software and all curated protein-protein and protein-peptide datasets are freely available at http://chembench.mml.unc.edu/cracle.

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