BackgroundSeveral computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species.ResultsWe developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM) model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV) and hepatitis C virus (HCV), our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO) annotations of proteins, we predicted new interactions between virus proteins and human proteins.ConclusionsEncoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1) it enables a prediction model to achieve a better performance than other representations, (2) it generates feature vectors of fixed length regardless of the sequence length, and (3) the same representation is applicable to different types of proteins.
Biological processes are often performed by a group of proteins rather than by individual proteins, and proteins
in a same biological group form a densely connected subgraph in a protein-protein interaction network. Therefore,
finding a densely connected subgraph provides useful information to predict the function or protein complex of uncharacterized proteins in the highly connected subgraph. We have developed an efficient algorithm and program for finding cliques and near-cliques in a protein-protein interaction network. Analysis of the interaction network of yeast proteins using the algorithm demonstrates that 59% of the near-cliques identified by our algorithm have at least one function shared by all the proteins within a near-clique, and that 56% of the near-cliques show a good agreement with the experimentally determined protein complexes catalogued in MIPS.
Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein-protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein-protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.
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