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.

Understanding Supramolecular Assembly of Supercharged Proteins

“…Our results show that assembly of supercharged proteins is governed by both these local attractions as well as long-range electrostatic interactions. Therefore, these findings will be useful for informing the rational design of new hierarchical structures based on predictions of complementary surfaces. − In particular, rational design methods for supercharged proteins should take into consideration long-range electrostatic interactions away from the interface for stabilizing protein complexes, which are known to play a role in kinetic assembly and disassembly processes involving supramolecular protein complexes.…”

Understanding Supramolecular Assembly of Supercharged Proteins

“…From this view, our results will be useful for informing the rational design of new hierarchical structures based on predictions of complementary surfaces. [73][74][75][76][77] In particular, rational design methods for supercharged proteins should take into consideration long-range electrostatic interactions away from the interface for stabilizing protein complexes, which are known to play a role in kinetic assembly and disassembly processes involving supramolecular protein complexes.…”

Geometry-Based Deep Learning in the Natural Sciences

“…The T-cell's receptor mechanism for recognition of peptides is a form of geometrical The T-cell's receptor mechanism for recognition of peptides is a form of geometrical problem. As the biological perspective of protein structure emerges from three-dimensional analysis of atomic interactions, the mechanism of immune cell recognition is also a phenomenon that occurs in three-dimensional space and is reducible to geometrical abstraction [39][40][41][42][43][44]. Therefore, the interface between molecular surfaces can be represented as an abstraction that has explanatory power as a model (Figure 3).…”