Topology of protein interaction network shapes protein abundances and strengths of their functional and nonspecific interactions

Abstract

How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous nonfunctional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in a crowded cellular environment. The model cell includes three independent prototypical pathways, whose topologies of protein-protein interaction (PPI) subnetworks are different, but whose contributions to the cell fitness are equal. Model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a "frustration" effect: Strengthening of functional interactions brings about hydrophobic interfaces, which make proteins prone to promiscuous binding. The balancing act is achieved by lowering concentrations of hub proteins while raising solubilities and abundances of functional monomers. On the basis of these principles we generated and analyzed a possible realization of the proteome-wide PPI network in yeast. In this simulation we found that high-throughput affinity capturemass spectroscopy experiments can detect functional interactions with high fidelity only for high-abundance proteins while missing most interactions for low-abundance proteins.genotype-phenotype relationship | a multi-scale evolutionary model cell | evolution of protein interface U nderstanding general design principles that govern biophysics and evolution of protein-protein interactions (PPIs) in living cells remains elusive despite considerable effort. Although strength of interactions between functional partners is undoubtedly a crucial component of a successful PPI (positive design), this factor represents only one aspect of the problem. As with many other design problems, an equally important aspect is negative design, i.e., ensuring that proteins do not make undesirable interactions in crowded cellular environments. The negative design problem for PPIs got some attention only recently (1, 2). Furthermore, interaction between two proteins depends not only on their binding affinity but also on their (and possibly other proteins) concentrations in living cells (2). Therefore, one might expect that control of protein abundances is a third important factor in design and evolution of natural PPIs. Mechanistic insights of how PPIs coevolve with protein abundances could best be gleaned from a detailed bottom-up model, where biophysically realistic thermodynamic properties of proteins and their interactions in crowded cellular environments are coupled with population dynamics of their carrier organisms.Recently we proposed a unique multiscale physics-based microscopic evolutionary model of living cells (3, 4). In the model, the genome of an organism consists of several essential genes that encode simple coarse-grained model proteins. The physicalchemi...

Biophysics of protein evolution and evolutionary protein biophysics

“…Real-life-like properties such as preferred folds and protein families readily emerge from these simple assumptions [308]. Introduction of various protein -protein interactions to this modelling set-up by Shakhnovich and co-workers [345] has provided further rationalization for the emergence of species-like collections of model cells with very similar sequence make-ups, an increased rate of mutation in stress response [346], as well as a trade-off between strengthening functional interactions and avoidance of misinteractions as observed in experimental proteomic data [347]. More recently, Zhang and co-workers [99] developed a related lattice approach to model evolution of protein -protein interactions that offers an explanation of slow evolution of highly expressed proteins in terms of stronger constraints on these proteins to avoid misinteractions.…”

“…The latter selection pressures affect primarily buried residues but can also affect surface residues. Misinteractions may be caused by the same exposed hydrophobic surface residues that are part of the functional protein-protein interactions, leading to an adaptive conflict between increasing the strength of functional interactions and avoiding misinteractions [347]. This conflict can result in further constraints that limit the viability of mutants of highly expressed proteins.…”

“…This conflict can result in further constraints that limit the viability of mutants of highly expressed proteins. 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].…”

“…The expanded concept of molecular phenotype discussed in §2.9 is an attempt towards a better account of this biophysical reality. In this regard, highly simplified yet promising explicit-chain biophysical models have recently been developed to study protein evolution in the context of protein-protein interactions [99,347] ( §3.2.3) We have also summarized explicit-chain biophysical models that take into consideration the ensemble nature of a protein's conformational phenotype, and how these models can provide insights into selection of promiscuous function, bi-stability and structural divergence ( §3.2).…”

“…It would be daunting to account for these interactions in all their complexity at the molecular level. Using simple explicit-chain protein models, conceptual advances were made in elucidating how the biophysical constraint of misinteraction avoidance might impact protein evolution [99,347] ( §3.10). However, investigators have to rely upon abstract descriptions of protein interactions, using model parameters extracted from experimental data on binding and on the effects of enzymatic activities on biochemical reaction rates.…”