Predicting Protein Phenotypes Based on Protein-Protein Interaction Network

Background Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins. Methodology/Principal Findings Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked acording to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%. Conclusions/Significance The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms.

[1]  Gary D Bader,et al.  Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry , 2002, Nature.

[2]  Hao Lin,et al.  Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. , 2009, Protein and peptide letters.

[3]  A. Fraser,et al.  A first-draft human protein-interaction map , 2004, Genome Biology.

[4]  1 A New Perspective on V 3 Phenotype Prediction , 2002 .

[5]  Yu-Dong Cai,et al.  Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties , 2010, PloS one.

[6]  Tetsuo Shibuya,et al.  New kernel methods for phenotype prediction from genotype data. , 2010, Genome informatics. International Conference on Genome Informatics.

[7]  Yu Shyr,et al.  The prediction of interferon treatment effects based on time series microarray gene expression profiles , 2008, Journal of Translational Medicine.

[8]  A. Goffeau,et al.  The uses of genome-wide yeast mutant collections , 2004, Genome Biology.

[9]  Shoshana J. Wodak,et al.  CYGD: the Comprehensive Yeast Genome Database , 2004, Nucleic Acids Res..

[10]  Hung-Ming Wang,et al.  Proteomics of the radioresistant phenotype in head-and-neck cancer: Gp96 as a novel prediction marker and sensitizing target for radiotherapy. , 2010, International journal of radiation oncology, biology, physics.

[11]  Yanzhi Guo,et al.  Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. , 2009, Journal of theoretical biology.

[12]  K. Chou,et al.  Prediction of protein structural classes. , 1995, Critical reviews in biochemistry and molecular biology.

[13]  Himanshu Sinha,et al.  Sequential Elimination of Major-Effect Contributors Identifies Additional Quantitative Trait Loci Conditioning High-Temperature Growth in Yeast , 2008, Genetics.

[14]  K. Chou,et al.  Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms , 2008, Nature Protocols.

[15]  Gerald R. Fink,et al.  Unipolar cell divisions in the yeast S. cerevisiae lead to filamentous growth: Regulation by starvation and RAS , 1992, Cell.

[16]  P. Aloy,et al.  Relation between amino acid composition and cellular location of proteins. , 1997, Journal of molecular biology.

[17]  Y. Ohya,et al.  Comprehensive and quantitative analysis of yeast deletion mutants defective in apical and isotropic bud growth , 2009, Current Genetics.

[18]  Vesteinn Thorsson,et al.  Prediction of phenotype and gene expression for combinations of mutations. , 2007, Molecular systems biology.

[19]  Zhanchao Li,et al.  Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. , 2007, Journal of theoretical biology.

[20]  H. Lehrach,et al.  A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome , 2005, Cell.

[21]  K. Chou,et al.  Recent progress in protein subcellular location prediction. , 2007, Analytical biochemistry.

[22]  Kriston L. McGary,et al.  Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes , 2007, Genome Biology.

[23]  B. A. Fong,et al.  Correlation among genotype, phenotype, and biochemical markers in Gaucher disease: implications for the prediction of disease severity. , 2002, Molecular genetics and metabolism.

[24]  A. Foulkes,et al.  Characterizing the Relationship Between HIV‐1 Genotype and Phenotype: Prediction‐Based Classification , 2002, Biometrics.

[25]  K. Chou.  Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001 .

[26]  Paul Shannon,et al.  Derivation of genetic interaction networks from quantitative phenotype data , 2005, Genome Biology.

[27]  Christian von Mering,et al.  STRING 8—a global view on proteins and their functional interactions in 630 organisms , 2008, Nucleic Acids Res..

[28]  S. D. dos Santos,et al.  An amphibian‐derived, cationic, α‐helical antimicrobial peptide kills yeast by caspase‐independent but AIF‐dependent programmed cell death , 2007, Molecular microbiology.

[29]  J. Heitman,et al.  Signal Transduction Cascades Regulating Fungal Development and Virulence , 2000, Microbiology and Molecular Biology Reviews.

[30]  Xiaoyong Zou,et al.  Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. , 2009, Protein and peptide letters.

[31]  K. Chou.  Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001, Proteins.

[32]  James R. Knight,et al.  A Protein Interaction Map of Drosophila melanogaster , 2003, Science.

[33]  T. Bathen,et al.  MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status , 2007, Breast Cancer Research and Treatment.

[34]  G. Lubec,et al.  Identification of enzymes and activity from two-dimensional gel electrophoresis , 2007, Nature Protocols.

[35]  Shungao Xu,et al.  Improved prediction of coreceptor usage and phenotype of HIV-1 based on combined features of V3 loop sequence using random forest. , 2007, Journal of microbiology.

[36]  Julian Peto,et al.  Prediction of BRCA1 Status in Patients with Breast Cancer Using Estrogen Receptor and Basal Phenotype , 2005, Clinical Cancer Research.

[37]  Pierre Lecocq,et al.  A comparison of HIV‐1 drug susceptibility as provided by conventional phenotyping and by a phenotype prediction tool based on viral genotype , 2009, Journal of medical virology.

[38]  A. Fire,et al.  Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans , 1998, Nature.

[39]  E. Winzeler,et al.  Functional analysis of the yeast genome by precise deletion and parallel phenotypic characterization. , 2000, Novartis Foundation symposium.

[40]  K. Chou,et al.  Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks , 2010, PloS one.

[41]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[42]  Christoph Kaleta,et al.  Phenotype prediction in regulated metabolic networks , 2008, BMC Systems Biology.

[43]  中尾 光輝,et al.  KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース) , 2000 .

[44]  T. Dwyer,et al.  Does the addition of information on genotype improve prediction of the risk of melanoma and nonmelanoma skin cancer beyond that obtained from skin phenotype? , 2004, American journal of epidemiology.

[45]  R. Swanstrom,et al.  Improved success of phenotype prediction of the human immunodeficiency virus type 1 from envelope variable loop 3 sequence using neural networks. , 2001, Virology.

[46]  S. L. Wong,et al.  A Map of the Interactome Network of the Metazoan C. elegans , 2004, Science.

[47]  S. L. Wong,et al.  Towards a proteome-scale map of the human protein–protein interaction network , 2005, Nature.

[48]  Tao Huang,et al.  Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles , 2009, PloS one.

[49]  Lin Lu,et al.  A novel computational approach to predict transcription factor DNA binding preference. , 2009, Journal of proteome research.

[50]  B. Westermann,et al.  Role of essential genes in mitochondrial morphogenesis in Saccharomyces cerevisiae. , 2005, Molecular biology of the cell.

[51]  L. Bacheler,et al.  Prediction of HIV-1 drug susceptibility phenotype from the viral genotype using linear regression modeling. , 2007, Journal of virological methods.

[52]  Roded Sharan,et al.  A genome-wide screen for essential yeast genes that affect telomere length maintenance , 2009, Nucleic acids research.

[53]  J. Becker,et al.  Genomewide Screen Reveals a Wide Regulatory Network for Di/Tripeptide Utilization in Saccharomyces cerevisiae , 2006, Genetics.

[54]  G. Church,et al.  A global view of pleiotropy and phenotypically derived gene function in yeast , 2005, Molecular systems biology.

[55]  Jacques Corbeil,et al.  A new perspective on V3 phenotype prediction. , 2003, AIDS research and human retroviruses.