Inter-protein residue covariation information unravels physically interacting protein dimers

Evolutionary rate covariation is a reliable predictor of co-functional interactions but not necessarily physical interactions

Co-functional proteins tend to have rates of evolution that covary over time. This correlation between evolutionary rates can be measured over the branches of a phylogenetic tree through methods such as evolutionary rate covariation (ERC), and then used to construct gene networks by the identification of proteins with functional interactions. The cause of this correlation has been hypothesized to result from both compensatory coevolution at physical interfaces and nonphysical forces such as shared changes in selective pressure. This study explores whether coevolution due to compensatory mutations has a measurable effect on the ERC signal. We examined the difference in ERC signal between physically interacting protein domains within complexes compared to domains of the same proteins that do not physically interact. We found no generalizable relationship between physical interaction and high ERC, although a few complexes ranked physical interactions higher than nonphysical interactions. Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC, and we hypothesize that the stronger signal instead comes from selective pressures on the protein as a whole and maintenance of the general function.

The Importance of Weakly Co-Evolving Residue Networks in Proteins is Revealed by Visual Analytics

Small changes in a protein’s core packing produce changes in function, and even small changes in function bias species fitness and survival. Therefore individually deleterious mutations should be evolutionarily coupled with compensating mutations that recover fitness. Co-evolving pairs of mutations should be littered across evolutionary history. Despite longstanding intuition, the results of co-evolution analyses have largely disappointed expectations. Regardless of the statistics applied, only a small majority of the most strongly co-evolving residues are typically found to be in contact, and much of the “meaning” of observed co-evolution has been opaque. In a medium-sized protein of 300 amino acids, there are almost 20 million potentially-important interdependencies. It is impossible to understand this data in textual format without extreme summarization or truncation. And, due to summarization and truncation, it is impossible to identify most patterns in the data. We developed a visualization approach that eschews the common “look at a long list of statistics” approach and instead enables the user to literally look at all of the co-evolution statistics simultaneously. Users of our tool reported visually obvious “clouds” of co-evolution statistics forming distinct patterns in the data, and analysis demonstrated that these clouds had structural relevance. To determine whether this phenomenon generalized, we repeated this experiment in three proteins we had not previously studied. The results provide evidence about how structural constrains have impacted co-evolution, why previous “examine the most frequently co-evolving residues” approaches have had limited success, and additionally shed light on the biophysical importance of different types of co-evolution.

Inter-protein residue covariation information unravels physically interacting protein dimers

BACKGROUND

Predicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown interacting counterparts. Most of co-evolutionary methods discover a combination of physical interplays and functional associations. However, there are only a handful of approaches which specifically infer physical interactions. Hybrid co-evolutionary methods exploit inter-protein residue coevolution to unravel specific physical interacting proteins. In this study, we introduce a hybrid co-evolutionary-based approach to predict physical interplays between pairs of protein families, starting from protein sequences only.

RESULTS

In the present analysis, pairs of multiple sequence alignments are constructed for each dimer and the covariation between residues in those pairs are calculated by CCMpred (Contacts from Correlated Mutations predicted) and three mutual information based approaches for ten accessible surface area threshold groups. Then, whole residue couplings between proteins of each dimer are unified into a single Frobenius norm value. Norms of residue contact matrices of all dimers in different accessible surface area thresholds are fed into support vector machine as single or multiple feature models. The results of training the classifiers by single features show no apparent different accuracies in distinct methods for different accessible surface area thresholds. Nevertheless, mutual information product and context likelihood of relatedness procedures may roughly have an overall higher and lower performances than other two methods for different accessible surface area cut-offs, respectively. The results also demonstrate that training support vector machine with multiple norm features for several accessible surface area thresholds leads to a considerable improvement of prediction performance. In this context, CCMpred roughly achieves an overall better performance than mutual information based approaches. The best accuracy, sensitivity, specificity, precision and negative predictive value for that method are 0.98, 1, 0.962, 0.96, and 0.962, respectively.

CONCLUSIONS

In this paper, by feeding norm values of protein dimers into support vector machines in different accessible surface area thresholds, we demonstrate that even small number of proteins in pairs of multiple alignments could allow one to accurately discriminate between positive and negative dimers.