Research On Prediction Of Protein Domains Interaction From Statistics

We described "domains interaction exclusion forecast", a method for inferring domain interactions from databases of interacting proteins, "domains interaction exclusion forecast" features a log odds score, Eij, reflecting confidence that domains i and j interact. We analyzed the potential domain interactions underlying 26,032 protein interactions. lastly , high-confidence domain interactions were inferred, and were evaluated using known domain interactions in the Protein Data Bank. This method may prove useful in guiding experiment-based discovery of previously unrecognized domain interactions.Post-genomic biological discoveries have confirmed that proteins function in extended networks. In particular, many proteins must physically bind to other proteins, either stably or transiently, to perform their functions. The functions of proteins are therefore inseparable from their interactions.For each protein to interact with its appropriate network neighbors, the highly specific recognition events must occur. Interaction specificity results from the binding of a modular domain to another domain or smaller peptide motif in the target protein. In the context of protein interaction, such domains and peptides act as recognition elements;we refer to these simply as 'domains'. The patterns of domain interactions are repeated within organisms and across taxa, suggesting that recognition patterns are conserved throughout biology. Such patterns constitute a 'protein recognition code', and it may be that many of these recognition patterns remain to be discovered.Protein-protein interactions can be determined experimentally. However, the specific domain interactions are usually not detected,and require further analysis to determine. It is therefore difficult to know which segment of a protein, often just a fraction of its total length, interacts directly with its biological partners.High-throughput protein interaction studies and databases of protein interactions present an opportunity to discover domain interaction patterns through statistical analysis of domain co-occurrence in interacting proteins. The idea is to find pairs of domains that co-occur significantly more often in interacting protein pairs than in non-interacting pairs.However, such bioinformatic discovery of domain interaction patterns is complicated by the lack of data on which protein pairs interact and which do not.We describe a statistical approach called "domains interaction exclusion forecast" to infer domain interactions from the incomplete interactomes of multiple organisms. This method extends earlier related methods , and adds a likelihood ratio test to assess the contribution of each potential domain interaction to the likelihood of a set of observed protein interactions.It consists of three steps: (i) compile protein interaction data and compute Sij the frequency of interaction of each domain pair i and j, relative to the abundance of domains i and j in the data , (ii) using Sij as an initial guess, apply the expectation maximization (EM) algorithm to obtain a maximum likelihood estimate of ij, the probability of interaction of each potentially interacting domain pair i and j evaluated in the context of any other domains occurring in the same proteins as domains i and j , and (iii) exclude all possible interactions of domains i and j from the mixture of competing hypotheses, rerun EM, evaluate the change in likelihood, and express this as a log odds score, Eij, reflecting confidence that domains i and j interact. A high Eij indicates that there is extensive evidence in protein interaction data supporting the hypothesis that domains i and jinteract;a low Eij suggests that competing hypotheses (other potential domain interactions) are roughly as good at explaining the observed protein interactions.We show that domain pairs inferred to interact with high E are significantly enriched among domain pairs known to interact in the Protein Data Bank (PDB), demonstrating "domains interaction exclusion forecast" is ability to identify physically interacting domain pairs. This method can also infer highly specific domain interactions by screening for domain pairs with a low 6 and high E.Lastly, through experiment and analyzing the result, we explored it is ability to discover previously unrecognized domain interactions by screening for interactions withhigh E involving domains with unknown function. And it seems that "domains interaction exclusion forecast" can be used to mine protein interaction databases for evidence of conserved, highly specific domain interactions.