Simplified geometric representations of protein structures identify complementary interaction interfaces
Abstract
Protein‐protein interactions are critical to protein function, but three‐dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large‐scale prediction and assembly of 3D structures of multi‐protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a reduced representation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and evolutionary rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner‐specific potential surface complementarity. On two available benchmarks of 185 overall known protein complexes, we observe predictions comparable to other structure‐based tools at correctly identifying protein interaction surfaces. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor‐ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å Cα‐RMSD. Thus, a shape reduction of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 11, 2020
- Source ID
- 10.1002/prot.26020
Entities
People
- Caitlyn L McCafferty
- David W Taylor
- Edward Marcotte
Organizations
- Army Research Office
- Cancer Prevention and Research Institute of Texas
- Dell Medical School at The University of Texas at Austin
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Institute of General Medical Sciences
- National Science Foundation
- Robert A. Welch Foundation
- Robert J Kleberg Jr and Helen C Kleberg Foundation
- University of Texas at Austin