Protein domains are basic functional units of proteins. Many protein domains are pervasive among diverse biological processes, yet some are associated with specific pathways. Human complex diseases are generally viewed as pathway-level disorders. Therefore, we hypothesized that pathway-specific domains could be highly informative for human diseases. To test the hypothesis, we developed a network-based scoring scheme to quantify specificity of domain-pathway associations. We first generated domain profiles for human proteins, then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). We found that PSDs contained more pathogenic variants than NSDs. PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions (PPIs) and tend to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in functional failure of pathways. Finally, we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish. Taken together, the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes.

MOTIVATION: Functional protein-protein interaction (PPI) networks elucidate molecular pathways underlying complex phenotypes, including those of human diseases. Extrapolation of domain-domain interactions (DDIs) from known PPIs is a major domain-based method for inferring functional PPI networks. However, the protein domain is a functional unit of the protein. Therefore, we should be able to effectively infer functional interactions between proteins based on the co-occurrence of domains. RESULTS: Here, we present a method for inferring accurate functional PPIs based on the similarity of domain composition between proteins by weighted mutual information (MI) that assigned different weights to the domains based on their genome-wide frequencies. Weighted MI outperforms other domain-based network inference methods and is highly predictive for pathways as well as phenotypes. A genome-scale human functional network determined by our method reveals numerous communities that are significantly associated with known pathways and diseases. Domain-based functional networks may, therefore, have potential applications in mapping domain-to-pathway or domain-to-phenotype associations. AVAILABILITY AND IMPLEMENTATION: Source code for calculating weighted mutual information based on the domain profile matrix is available from www.netbiolab.org/w/WMI CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The identification of differentially expressed proteins (DEPs) observed under specific conditions is one of the key issues in proteomics research. There are currently several ways to detect the changes of a specific protein's expression level in two-dimensional electrophoresis (2-DE) gel images such as statistical analysis and graphical visualization. However, it is quite difficult to handle the information of an individual protein manually by these methods due to the large distortions of patterns in 2-DE images. This paper proposes a method of analyzing DEPs for a specific disease. In order to automatically extract meaningful DEPs in a set of 2-DE gel images, we have designed an exception function that is suitable to measure the anomalous change of the expression level of an individual protein. We present the comparison results of the proposed method versus a Wilcoxon paired t -test that is one of the widely used statistical analysis methods. Several experiments are performed to address not only the effectiveness of the exception function but also the fact that these two methods can compensate each other practically.

The two-dimensional electrophoresis (2-DE) method is widely used in proteomics to separate thousands of proteins in a sample. Given a set of gel images of the same kind, a spot-matching operation identifies the spot of the same protein in each 2-DE gel image. However, since 2-DE gel images are very sensitive to various experimental conditions, spots observed in a 2-DE gel image involve inherent errors such as noises and irregular geometric distortions. Consequently, the accuracy of spot-matching is intensively influenced by these errors. This paper proposes a spot-matching framework to improve the accuracy of 2-DE gel image analysis. The framework includes an automatic landmark extraction module that uses a pattern model of positional and intensive distortions identified in the learning set of gel images. Subsequently, a probabilistic matching adjustment module is employed to produce more reliable spot-matching results based on the selected landmark spots in the first module. In order to confirm the synergy effects of integrating these two modules, the improved accuracy of the proposed framework is analyzed through various experiments on real 2-DE gel images of human liver tissue.