Optimization of specificity in a cellular protein interaction network by negative selection

Zarrinpar, Ali; Park, Sang‐Hyun; Lim, Wendell A.

doi:10.1038/nature02178

Cited by 260 publications

(314 citation statements)

References 28 publications

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“…Hence, our result suggests that binding sites have evolved for binding partners via common motifs. This result is supported by previous studies showing that binding site motifs of interacting partners can act as determinants of specificity [12,[40][41][42]. Our next goal was to study the role of hot spots in the interplay between binding affinity and specificity.…”

Section: Hot Spot Distribution On Interfacessupporting

confidence: 75%

Energetic determinants of protein binding specificity: Insights into protein interaction networks

2009

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One of the challenges of the postgenomic era is to provide a more realistic representation of cellular processes by combining a systems biology description of functional networks with information on their interacting components. Here we carried out a systematic large-scale computational study on a structural protein-protein interaction network dataset in order to dissect thermodynamic characteristics of binding determining the interplay between protein affinity and specificity. As expected, interactions involving specific binding sites display higher affinities than those of promiscuous binding sites. Next, in order to investigate a possible role of modular distribution of hot spots in binding specificity, we divided binding sites into modules previously shown to be energetically independent. In general, hot spots that interact with different partners are located in different modules. We further observed that common hot spots tend to interact with partners exhibiting common binding motifs, whereas different hot spots tend to interact with partners with different motifs. Thus, energetic properties of binding sites provide insights into the way proteins modulate interactions with different partners. Knowledge of those factors playing a role in protein specificity is important for understanding how proteins acquire additional partners during evolution. It should also be useful in drug design.

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Section: Hot Spot Distribution On Interfacessupporting

confidence: 75%

Energetic determinants of protein binding specificity: Insights into protein interaction networks

2009

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“…One example is protein-protein interactions mediated by specific binding domains (43,44). In this case, positive and negative modes of regulation correspond to the activation of proteins by either the binding or unbinding of a regulatory domain.…”

Section: Discussionmentioning

confidence: 99%

Rules for biological regulation based on error minimization

Shinar

Dekel

Tlusty

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

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The control of gene expression involves complex mechanisms that show large variation in design. For example, genes can be turned on either by the binding of an activator (positive control) or the unbinding of a repressor (negative control). What determines the choice of mode of control for each gene? This study proposes rules for gene regulation based on the assumption that free regulatory sites are exposed to nonspecific binding errors, whereas sites bound to their cognate regulators are protected from errors. Hence, the selected mechanisms keep the sites bound to their designated regulators for most of the time, thus minimizing fitness-reducing errors. This offers an explanation of the empirically demonstrated Savageau demand rule: Genes that are needed often in the natural environment tend to be regulated by activators, and rarely needed genes tend to be regulated by repressors; in both cases, sites are bound for most of the time, and errors are minimized. The fitness advantage of error minimization appears to be readily selectable. The present approach can also generate rules for multi-regulator systems. The error-minimization framework raises several experimentally testable hypotheses. It may also apply to other biological regulation systems, such as those involving protein-protein interactions.biological physics ͉ complex networks ͉ systems biology ͉ transcriptional regulation B iological regulation systems convert input signals into specified outputs. The same input-output relationship can generally be carried out by several different mechanisms. For example, transcription regulation is carried out by regulatory proteins that bind specific sites in the promoter region of the regulated genes. A gene that is fully expressed only in the presence of a signal ( Fig. 1) can be regulated by two different mechanisms (1). In the first mechanism, called positive control, an activator binds the promoter to turn on expression. In the second mechanism, called negative control, a repressor binds the promoter to turn expression off. These two mechanisms realize the same input-output relationship: Expression is turned on by the binding of an activator in the positive mode of control and by the unbinding of a repressor in the negative mode of control. More generally, a gene controlled by N regulators, each of which can be either an activator or a repressor, has 2 N possible mechanisms for a given input-output mapping.Among these equivalent mechanisms, evolutionary selection chooses one for each system. Are there rules that govern the selection of mechanisms in biological systems? One possibility is that evolution chooses randomly between equivalent designs. Hence, the selected mechanism is determined by historical precedent. Another possibility is that general principles exist that govern the choice of mechanism in each system.The question of rules for gene regulation was raised by M. A. Savageau (2-6) in his pioneering study of transcriptional control. Savageau found that the mode of control is correlated with the demand...

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“…Notably, direct mutagenesis of these determinants of specificity has been used to rewire the entire histidine kinase signaling system in bacteria in a predictive manner 54 . Recent follow-up work indicates that mutations in determinants of specificity prevent cross-talk and allow protein family expansions 55 , in a process similar to the one powered by negative selection over Src homology 3 (SH3) protein domains that show similar specificity 56 . We propose that similar studies in human signaling networks, coupled with mapping of cancer mutations on these determinants of specificity, would shed new light on whether signaling rewiring is a general principle of oncogenesis and tumor progression, knowledge of which would in turn be critical as molecular therapies target proteins and their networks and not genes.…”

Section: Personalized Cancer Network Biologymentioning

confidence: 99%