Weighted mutual information analysis substantially improves domain-based functional network models

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: Insuklee@yonsei.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.

[1]  P. Chanson,et al.  Metabolic Syndrome in Cushing’s Syndrome , 2010, Neuroendocrinology.

[2]  E. Sprinzak,et al.  Correlated sequence-signatures as markers of protein-protein interaction. , 2001, Journal of molecular biology.

[3]  Shailesh V. Date,et al.  A Probabilistic Functional Network of Yeast Genes , 2004, Science.

[4]  Prudence Mutowo-Meullenet,et al.  Manual GO annotation of predictive protein signatures: the InterPro approach to GO curation , 2012, Database J. Biol. Databases Curation.

[5]  François Schiettecatte,et al.  OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders , 2014, Nucleic Acids Res..

[6]  Gary D Bader,et al.  Pathway and network analysis of cancer genomes , 2015, Nature Methods.

[7]  Gang Fu,et al.  Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data , 2014, Nucleic Acids Res..

[8]  Andrew D. Moore,et al.  Arrangements in the modular evolution of proteins. , 2008, Trends in biochemical sciences.

[9]  L. Joosten,et al.  The loss of α2β1 integrin suppresses joint inflammation and cartilage destruction in mouse models of rheumatoid arthritis. , 2012, Arthritis and rheumatism.

[10]  Dustin Scheinost,et al.  Serial nonrigid vascular registration using weighted normalized mutual information , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Benjamin J. Raphael,et al.  Network analysis of GWAS data. , 2013, Current opinion in genetics & development.

[12]  V. S. Rao,et al.  Protein-Protein Interaction Detection: Methods and Analysis , 2014, International journal of proteomics.

[13]  Minghua Deng,et al.  Inferring Domain–Domain Interactions From Protein–Protein Interactions , 2002 .

[14]  Jason W Sohn,et al.  Spatially weighted mutual information image registration for image guided radiation therapy. , 2010, Medical physics.

[15]  Sailu Yellaboina,et al.  DOMINE: a comprehensive collection of known and predicted domain-domain interactions , 2010, Nucleic Acids Res..

[16]  A. M. Verbeek,et al.  Relationship between anticoagulant medication and massive intraocular hemorrhage in age-related macular degeneration , 2000, Graefe's Archive for Clinical and Experimental Ophthalmology.

[17]  Zhongming Zhao,et al.  Network-assisted analysis to prioritize GWAS results: principles, methods and perspectives , 2013, Human Genetics.

[18]  D. Louis Collins,et al.  Self-similarity Weighted Mutual Information: A New Nonrigid Image Registration Metric , 2012, MICCAI.

[19]  K. Kleesiek,et al.  Circulating P-, L- and E-selectins in pseudoxanthoma elasticum patients. , 2008, Clinical biochemistry.

[20]  T. Vicsek,et al.  Clique percolation in random networks. , 2005, Physical review letters.

[21]  D. Louis Collins,et al.  Self-similarity weighted mutual information: A new nonrigid image registration metric , 2014, Medical Image Anal..

[22]  C. Chothia,et al.  Evolution of the Protein Repertoire , 2003, Science.

[23]  Kriston L. McGary,et al.  Open Access Method , 2007 .

[24]  Jung Eun Shim,et al.  Network-assisted approaches for human disease research , 2015 .

[25]  Anant Madabhushi,et al.  Spatially weighted mutual information (SWMI) for registration of digitally reconstructed ex vivo whole mount histology and in vivo prostate MRI , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Huaiyu Mi,et al.  The InterPro protein families database: the classification resource after 15 years , 2014, Nucleic Acids Res..

[27]  Bumki Min,et al.  IDDI: integrated domain-domain interaction and protein interaction analysis system , 2012, Proteome Science.

[28]  V. Ingram.  The evolution of a protein. , 1962, Federation proceedings.

[29]  Ni Li,et al.  Gene Ontology Annotations and Resources , 2012, Nucleic Acids Res..

[30]  Simon R. Arridge,et al.  A Nonrigid Registration Framework Using Spatially Encoded Mutual Information and Free-Form Deformations , 2011, IEEE Transactions on Medical Imaging.

[31]  Gary D Bader,et al.  Domain‐mediated protein interaction prediction: From genome to network , 2012, FEBS letters.

[32]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[33]  Jérôme Wojcik,et al.  Protein-protein interaction map inference using interacting domain profile pairs , 2001, ISMB.

[34]  T. Ideker,et al.  The cancer cell map initiative: defining the hallmark networks of cancer. , 2015, Molecular cell.