Protein Interaction Energy Landscapes are Shaped by Functional and also Non-functional Partners

Highlights

• Functional and nonfunctional protein interactions undergo severe competition.

• We study how this competition shapes the protein surfaces’ interaction propensity.

• We present 2D energy maps reflecting the interaction propensity of protein surfaces.

• The interaction propensity of the whole protein surface is conserved during evolution.

• Strikingly this feature holds for functional but also nonfunctional protein pairs.

Abstract

In the crowded cell, a strong selective pressure operates on the proteome to limit the competition between functional and non-functional protein-protein interactions. We developed an original theoretical framework in order to interrogate how this competition constrains the behavior of proteins with respect to their partners or random encounters. Our theoretical framework relies on a two-dimensional (2D) representation of interaction energy landscapes, with 2D energy maps, which reflect in a synthetic way the spatial distribution of the interaction propensity of a protein surface for another protein. We realized the interaction propensity mapping of proteins' surfaces in interaction with functional and arbitrary partners and asked whether the distribution of their interaction propensity is conserved during evolution. Therefore, we performed several thousands of cross-docking simulations to systematically characterize the energy landscapes of 103 proteins interacting with different sets of homologs, corresponding to their functional partner’s family or arbitrary protein families. Then, we systematically compared the energy maps resulting from the docking of each protein with the different protein families of the dataset. Strikingly, we show that the interaction propensity not only of the binding sites but also of the rest of the surface is conserved for docking partners belonging to the same protein family. Interestingly, this observation holds for docked proteins corresponding to true but also arbitrary partners. Our theoretical framework enables the characterization of the energy behavior of a protein in interaction with hundreds of proteins and opens the way for the characterization of the behavior of proteins in a specific environment.

Introduction

Biomolecular interactions and particularly protein-protein interactions (PPIs) are central for many physiological processes and are of utmost importance for the functioning of the cell. As such, they have brought about a wealth of studies over the last decades [[1], [2], [3], [4], [5]]. The concentration of proteins in a cell has been estimated to be approximately 2–4 million proteins per cubic micron [6]. In such a highly crowded environment, proteins constantly encounter each other, and numerous nonspecific interactions are then likely to occur [[7], [8], [9], [10]]. For example, in the cytosol of S. cerevisiae, a protein can encounter up to 2000 different proteins [11]. In this complex jigsaw puzzle, each protein has evolved to bind the right piece(s) in the right way at the right moment (positive design), and to prevent misassembly and non-functional interactions (negative design) [[12], [13], [14], [15], [16]].

Positive design constrains the physicochemical properties and the evolution of protein-protein interfaces. Indeed, a strong selection pressure operates on binding sites to maintain the functional assembly, including the functional partner and the functional binding mode. For example, homologs sharing at least 30% sequence identity almost invariably interact in the same way [17]. Conversely, the negative design prevents proteins from being trapped in the numerous competing non-functional interactions inherent to the crowded environment of the cell. Many studies have described the relationship between the propensity of proteins for promiscuous interactions and their abundances or surface properties [[18], [19], [20], [21]]. Particularly, it has been shown that misinteraction avoidance shapes the evolution and physicochemical properties of abundant proteins, resulting in a slower evolution and less sticky surfaces than what is observed for less abundant ones [18,[22], [23], [24], [25], [26]]. The whole surface of abundant proteins is thus constrained, preventing them from engaging in deleterious non-specific interactions that could dramatically impact the cell at high concentrations [25]. For example, it has been shown in E. coli that the net charge, as well as the charge distribution on protein surfaces, affect the diffusion coefficients of proteins in the cytoplasm [19,27]; positively charged proteins move up to 100 times more slowly as they get caught in nonspecific interactions with ribosomes, which are negatively charged, and which, thus shape the composition of the cytoplasmic proteome [27].

All these studies show that both positive and negative designs operate on the whole protein surface. Binding sites are constrained to maintain functional assemblies (i.e., functional binding modes with functional partners) while the rest of the surface is constrained to avoid non-functional assemblies. These constraints should, therefore, shape the energy landscapes of functional but also, non-functional interactions to prevent the latter from prevailing over the former. This should have consequences on the evolution of the whole protein surface propensity to interact with its environment since non-interacting surfaces are in constant competition with functional binding sites. We can thus hypothesize that the distribution over the protein surface of its interaction propensity is constrained during evolution in order to ensure that proteins correctly bind functional partners and do not bind to other proteins to form non-functional assemblies.

In this work, we focus on protein surfaces as a “proxy” for regulating functional and non-functional PPIs. We investigate interaction energy landscapes of proteins with native and non-native partners and ask whether the distribution over the protein surface of its interaction propensity is conserved during evolution. With this aim in mind, we performed large-scale docking simulations to characterize interactions involving either native or native-related (i.e., partners of their homologs) partners or arbitrary partners. Docking simulations have the advantage of being able to simulate the interaction of a protein with arbitrary partners, which is very difficult to address with experimental approaches. Docking algorithms are now fast enough for large-scale applications and allow for the characterization of interaction energy landscapes for thousand of protein couples. Typically, a docking simulation takes from a few minutes to a couple of hours on modern processors [[28], [29], [30]], opening the way for extensive cross-docking experiments [[31], [32], [33], [34], [35]]. Here, we performed a cross-docking experiment involving 103 proteins docked with their native or native-related partners and their homologs, as well as arbitrary partners and their homologs. We represented the interaction energy landscapes resulting from each docking calculation with a two dimensional (2D) energy map in order to (i) realize the interaction propensity mapping of a protein surface in interaction with any protein and (ii) easily compare the energy maps resulting from the docking of a same protein with different sets of homologous partners. Thus, we characterize the evolution of the distribution over a protein surface of its interaction propensity for native or arbitrary partners.