Nonspecific binding limits the number of proteins in a cell and shapes their interaction networks

Abstract

Multicellular organisms, from Caenorhabditis elegans to humans, have roughly the same number of protein encoding genes. We show that the need to prevent disease-causing nonspecific interactions between proteins provides a simple physical reason why organism complexity is not reflected in the number of distinct proteins. By collective evolution of the amino acid sequences of protein binding interfaces we estimate the degree of misbinding as a function of the number of distinct proteins. Protein interaction energies are calculated with an empirical, residue-specific energy function tuned for protein binding. We show that the achievable energy gap favoring specific over nonspecific binding decreases with protein number in a power-law fashion. From the fraction of proteins involved in nonspecific complexes as a function of increasing protein number and decreasing energy gap, we predict the limits these binding requirements place on the number of different proteins that can function effectively in a given cellular compartment. Remarkably, the optimization of binding interfaces favors networks in which a few proteins have many partners, and most proteins have few partners, consistent with a scale-free network topology. We conclude that nonspecific binding adds to the evolutionary pressure to develop scale-free protein-protein interaction networks. T he number of proteins encoded by the genomes of humans and the nematode Caenorhabditis elegans is remarkably similar, ∼20;000 each (1), with comparable numbers for other eukaryotes (2). Large differences in organism complexity are thus reflected far less in proteome size than in gene regulatory networks (3), the degree of compartmentalization (4), the variety of distinct cell types (2), and alternative splicing (5). In this work, we provide a physical explanation for the absence of an increase in protein diversity from simple multicellular organisms to humans. Our approach seeks to capture the fundamental aspects of protein interactions conserved in any functioning cell, namely high binding specificity and minimal aggregation as the proteins participate in a network of binding interactions.The networks of protein-protein interactions, or interactomes (6-9), although distinctive to their individual organisms, manifest global and local characteristics that are shared across species (10, 11). Most notably, the organization of these networks exhibits a scale-free topology (10, 12) with a substantial number of highly connected hub proteins (13). Based on structural (14) and temporal (15) information, the hub proteins can be classified as date hubs or party hubs. In a date hub, multiple binding partners compete for binding to a single interface, where binding to one partner excludes simultaneous binding to any of the others. In a party hub, a protein has multiple binding interfaces that are accessible independent of each other, such that binding is not competitive. Collectively, these network features are relevant functionally by creating robust, modular interactomes (15, 16) an...

Biophysics of protein evolution and evolutionary protein biophysics

“…Within the cellular confine, a given protein can potentially come into contact with a large number of other proteins [108,109]. Although the possibility of non-specific binding probably constitutes a biophysical constraint that might have restricted the number of proteins in a cell [110], natural proteins can function by being remarkably specific binders. This interaction specificity entails not only favourable binding with a protein's target molecule(s) but also extremely unfavourableessentially absence of-binding with many other molecules.…”

“…As a consequence of these biophysical constraints, evolution might have increased the proportion of functional monomeric proteins with hydrophilic surfaces, reduced the abundance of functional multi-chain complexes, weakened the strengths of functional interactions, or increased the degree of disordered protein interactions to minimize exposed hydrophobics while still allowing many interaction partners [230,347]. In other words, such strategies might have contributed to the evolution of interaction network topologies that can better alleviate the conflict between functional interactions and misinteractions [110].…”

Protein abundance is key to distinguish promiscuous from functional phosphorylation based on evolutionary information

“…In yeast for example, while some proteins are present in only a few dozen copies, others are synthesized in over a million copies [45]. Therefore, competition is severe between sites phosphorylated in the context of a specific function and other sites for which phosphorylation has little or no functional consequence [39,[46][47][48][49]. For example, let us consider protein X, whose average abundance is approximately 1000 copies per cell, and whose function requires a phosphorylation at a particular position.…”

Information capacity of specific interactions

“…In 1894, Emil Fischer proposed the "lock and key" model as an analogy for understanding enzyme-substrate specificity (42), focusing on the physical shapes of paired interacting components; mutual information encompasses this idea and can be applicable to a large number of biological systems. In particular, our model is useful for predicting the differences between interacting proteins that use shape complementarity alone and those that combine both shape and electrostatic complementarity (e.g., Dpr-DIP vs. Dscam proteins) (43) and may also be applied to a host of other biological interaction networks (22) where information transmission and pair specificity play critical roles in biological function [e.g., histidine kinase/response regulator proteins (44) and the immune system (25)]. Crucially, the mutual information model provided above is flexible enough to be extended to some of the challenging physics encountered in biology.…”

“…The canonical example is protein-binding interactions (22); the binding interactions between two cognate proteins are specified by their amino acid sequence, which programs binding pockets with complex shape and chemical specificity. Recent efforts (23,24) aim to rationally design these protein interactions for self-assembly.…”