Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method. conjoint triad ͉ support vector machine T he molecular bases of cellular operations are sustained largely by different types of interactions among proteins. Thus, a major goal of functional genomics is to determine protein interaction networks for whole organisms (1). However, only recently has it become possible to combine the traditional study of proteins as isolated entities with the analysis of large protein interaction networks by using microarray and proteomic approaches (2, 3). Such kinds of studies are significantly important because many of the functions of complex systems seem to be more closely determined by their interactions rather than by the characteristics of their individual components (4). For example, metabolic pathways, signaling cascades, and transcription control processes involve complicated interaction networks (5). Recently, interaction networks have begun to be appreciated because it is necessary to address the general principles of biological systems by means of systems biology (6). Moreover, the study of protein interaction networks has been driven by potentially practical applications in drug discovery, because it might provide great insights into mechanisms of human diseases. This study may revolutionize the pipeline of drug discovery, because drugs discovered based on the protein interaction network may specifically modulate the disease-related pathway rather than simply inhibit or activate the functions of an individual target protein (7, 8) . Determining accurate cellular protein interaction networks with experimental methods in combination with computational approaches therefore has become a major theme of functional genomics and proteomics efforts (9). An impressive set of experimental techniques has been developed for the systematic analysis of protein-protein interactions (PPIs), including yeast two-hybrid-based methods (10), mass spectrometry (11), and protein chips (12) and hybrid approaches (13). Several binding reaction-detected methods, based on the presumption that the binding of one protein to another provokes a variety of biophysical changes, have been developed (14). These technologies recently identified hundreds of potentially interacting proteins and complexes in several species such as yeast (15), Drosophila (16), and Helicobacter pylori (17). Ulrich et al. (18) presented a large-scale two-hybrid map of Ͼ3,000 putative human PPIs. These data will serve as an important source of information regarding individual protein partners and offer preliminary insight into the global molecular organization of human cells. However, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks (19). Therefore, computational methods for the prediction of PPIs have an important role (20). A number of computational methods have been developed for the prediction of PPIs. Computational methods based on genomic information, such as phylogenetic profiles, predict PPIs by accounting for the pattern of the presence or absence of a given gene in a set of genomes (21, 22). The main limitation of these approaches is that they can be applied only to completely sequenced genomes, which is the precondition to rule out the absence of a given gene. Similarly, they cannot be used with the essential proteins that are common to most organisms (23). The prediction of functional relationships between two proteins according to their corresponding adjacency of genes is another popular approach. This method is directly applicable only to bacteria, in which the genome order is relatively more relevant (24). Park et al. (25) tried to find protein interaction partners by viewing interactions between protein domains in terms of the interactions between structural families of evolutionarily related domains. Sprinzak and Margalit (26) put forward another indirect interaction prediction method, digging out signature feature related to interactions rather than domain interaction information from the protein sequences via protein classification. However, these methods are not universal, because the accuracy and reliability of these methods depend on the information of protein homology or interaction marks of the protein partners. It is virtually axiomatic that ''sequence specifies structure,'' which gives rise to an assumption that knowledge of the amino acid sequence alone might be sufficient to estimate the interacting propensity between two proteins for a specific biological function (27). Accordingly, prediction of PPIs based only on
Shen et al. REF were among the first to predict protein-protein interaction using only sequence information.
Predicting protein-protein interactions based only on sequences information
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Biology Medicine