It is a general truism that cellular events are mediated by proteins. It is a further truth that proteins do not function in isolation, but work to accomplish their function in ‘cooperation’ often as part of large macromolecular assemblies. These assemblies are created and coordinated through large networks of mostly transient protein-protein interactions (PPIs). Much research effort has been expended in attempting to elucidate the specifics and means of protein interactions, and much recent Bioinformatics research is dedicated to the prediction and validation of PPIs.
The study in question here  examines a particular class of protein-protein interaction, and looks to elucidate the precise mechanisms by which binding strength and specificity are determined. The class of PPIs being studied are those where a globular domain in one protein recognises and binds to a linear peptide from another. This type of transient, peptide-mediated interaction is underrepresented in high-throughput datasets . It has been shown that, while bonding between linear motifs and globular domains are sufficient for binding, they are not enough to explain the high degree of interaction specificity that has been observed in vivo. What then confers the specificity? (Pbs2 in yeast, for instance, only binds to the SH3 domain of Sho1, and does not interact with any of the 26 other SH3 domains found in yeast. ) The answer, according to Stein and Aloy, is context. This context includes the spacial and temporal location of the proteins concerned (thus limiting the available binding partners), but also the residues that surround the linear binding motif which contribute to the environment of the interaction, and the overall energy of binding.
In order to assess what role the residue context (not spatial or temporal) plays in determining the specificity of PPIs, Stein and Aloy systematically identified all peptide-globular domain interactions (using the ELM database of motifs) of known structure from the PDB, and used them to investigate the contribution of the motif itself and its context to the global binding energy. They ended up with a set of 390 interactions of known structure, that they used for their analysis.
What they found, using the FoldX Package to perform in silico alanine scanning experiments, is that the residues of the binding motif itself are responsible for, on average, 79% of the global binding energy (between just 12% and 99.7%, depending on the type of interaction). The remaining 21% (on average) is contributed by the residues of the context.
The second major finding of the paper is that, within a group of domain-peptide interactions, the position of the motif within the interaction is relatively ‘fixed’ (RMSD – 2.5 ± 3.2Å), whereas there is more flexibility in context placement (RMSD – 4.2 ± 4.4Å). This reinforces the idea that the motif is necessary and sufficient for actual binding to take place (since it is more restrained, both sequentially and spacially), but the context is required to ensure specificity of a given reaction.
Their final observation, that in 5% of cases sequence conservation of <30% was sufficient to allow for exchange of binding partners, is another important one. This suggests that it is extremely difficult to predict any potential cross-reactions that may occur purely from sequence alignments. Therefore structural knowledge is required (whether experimental or modelled) in order to make successful predictions of domain-peptide interactions. Indeed they cite instances where exploiting structural knowledge has been useful for the prediction of domain-domain interactions (though I fear they missed the, clearly vitally important, work of Cockell et al (2007) ).
The suggestion is made that the context has evolved, not to maximise binding strength, but binding specificity. This is supported by the observation that the motif sequence, although not being completely responsible for the global binding energy, is often nearly optimal, and also by the relative inflexibilty of the motifs in structural terms. This has clear implications for both predicted, and experimentally determined PPIs. These implications are not pointed out in the paper, which is largely positive in tone, but I feel they are important.
Where predictions of interactions have been made using linear motifs as the guiding factor, context will not (or maybe very rarely) have been considered. Therefore it may be the case that while a given interaction is technically feasible, and the motif is sufficient for binding to occur, the lack of the correct context means that the interaction is actually unlikely to be found in vivo.
This is also true for experimentally determined interactions. In an experiment such as a yeast 2 hybrid screen (for example), 2 proteins are bought together in excess in the often foreign environment of a yeast nucleus. In these circumstances, a match between a globular domain and the appropriate motif partner may well lead to binding and reporter activation, regardless of context, simply due to the fact that no other proteins are around to compete that have a more suitable context for binding.
I enjoyed this paper, it is unusual to find a paper that is largely about binding energies and dissociation constants that doesn’t include a huge amount of laughably complicated mathematics, and Stein and Aloy strike the right balance I think. They make a valid point while summing up that knowledge of how transient PPIs occur, and are mediated, is cruicial for both systems and synthetic biology (ie understanding and modelling regulatory processes, and designing new circuits). This paper does contribute to that understanding significantly.
1. Amelie Stein, Patrick Aloy (2008). Contextual Specificity in Peptide-Mediated Protein Interactions PLoS ONE, 3 (7) DOI: 10.1371/journal.pone.0002524
2. T PAWSON, R LINDING (2005). Synthetic modular systems – reverse engineering of signal transduction FEBS Letters, 579 (8), 1808-1814 DOI: 10.1016/j.febslet.2005.02.013
3. Ali Zarrinpar, Sang-Hyun Park, Wendell A. Lim (2003). Optimization of specificity in a cellular protein interaction network by negative selection Nature, 426 (6967), 676-680 DOI: 10.1038/nature02178
4. S. J. Cockell, B. Oliva, R. M. Jackson (2007). Structure-based evaluation of in silico predictions of protein protein interactions using Comparative Docking Bioinformatics, 23 (5), 573-581 DOI: 10.1093/bioinformatics/btl661