The document discusses various methods for predicting protein function, including homology-based transfer of annotation and prediction of functional motifs and domains. Homology-based transfer can infer molecular function from sequence similarity, but biological process is only transferable between orthologs. Orthologs can be detected through phylogenetic trees or automated methods like InParanoid. Each protein domain contributes to molecular function, while short motifs like phosphorylation sites are also important. Functional annotation involves describing proteins at the molecular, biological process, and cellular component levels.

1. Prediction of protein function Lars Juhl Jensen EMBL Heidelberg

3. Why do we need to predict function?

10. Homology-based transfer of annotation Lars Juhl Jensen EMBL Heidelberg

14. Orthologs vs. paralogs

24. Definition of orthologous groups

33. Function prediction from protein domains Lars Juhl Jensen EMBL Heidelberg

54. Prediction of functional motifs from sequence Lars Juhl Jensen EMBL Heidelberg

56. Insulin Receptor Substrate 1

74. Signal peptide or propeptide

75. Signal peptide or propeptide Propeptide cleavage Signal peptide cleavage

76. Wrong start codon

78. Feature-based prediction of protein function Lars Juhl Jensen EMBL Heidelberg

80. The concept of ProtFun

82. Function prediction on the human prion sequence ############## ProtFun 1.1 predictions ############## >PRIO_HUMAN # Functional category Prob Odds Amino_acid_biosynthesis 0.020 0.909 Biosynthesis_of_cofactors 0.032 0.444 Cell_envelope 0.146 2.393 Cellular_processes 0.053 0.726 Central_intermediary_metabolism 0.130 2.063 Energy_metabolism 0.029 0.322 Fatty_acid_metabolism 0.017 1.308 Purines_and_pyrimidines 0.528 2.173 Regulatory_functions 0.013 0.081 Replication_and_transcription 0.020 0.075 Translation 0.035 0.795 Transport_and_binding => 0.831 2.027 # Enzyme/nonenzyme Prob Odds Enzyme 0.250 0.873 Nonenzyme => 0.750 1.051 # Enzyme class Prob Odds Oxidoreductase (EC 1.-.-.-) 0.070 0.336 Transferase (EC 2.-.-.-) 0.031 0.090 Hydrolase (EC 3.-.-.-) 0.057 0.180 Isomerase (EC 4.-.-.-) 0.020 0.426 Ligase (EC 5.-.-.-) 0.010 0.313 Lyase (EC 6.-.-.-) 0.017 0.334

84. Prediction performance on cellular role categories

85. Prediction performance on enzyme categories

86. Predictive performance on Gene Ontology categories

89. Secreted proteins are typically small

90. ROC plot for SecretomeP

91. Similar properties of classically and non-classically secreted proteins

97. SecretomeP feature usage

99. Mapping category performances onto input features

104. Prediction of functional interaction networks Lars Juhl Jensen EMBL Heidelberg

109. Functional interaction networks

111. Phylogenetic profiles

115. Cell Cellulosomes Cellulose

116. Formalizing the phylogenetic profile method Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps

117. Gene neighborhood

118. Gene neighborhood Identify runs of adjacent genes with the same direction Score each gene pair based on intergenic distances Calibrate against KEGG maps Infer associations in other species

119. Gene fusion

120. Gene fusion Find in A genes that match a the same gene in B Exclude overlapping alignments Calibrate against KEGG maps Calculate all-against-all pairwise alignments

122. Data integration

124. Physical protein interactions Make binary representation of complexes Yeast two-hybrid data sets are inherently binary Calculate score from number of (co-)occurrences Calculate score from non-shared partners Calibrate against KEGG maps Infer associations in other species Combine evidence from experiments

125. Binary representations of purification data

127. Mining microarray expression databases Re-normalize arrays by modern method to remove biases Build expression matrix Combine similar arrays by PCA Construct predictor by Gaussian kernel density estimation Calibrate against KEGG maps Infer associations in other species

129. Co-occurrence in the scientific texts Associate abstracts with species Identify gene names in title/abstract Count (co-)occurrences of genes Test significance of associations Calibrate against KEGG maps Infer associations in other species

132. Multiple types of interactions

137. The power of cross-species transfer and evidence integration

138. The power of cross-species transfer and evidence integration

139. The power of cross-species transfer and evidence integration

140. The power of cross-species transfer and evidence integration

141. The power of cross-species transfer and evidence integration

142. The power of cross-species transfer and evidence integration

143. The big challenge

144. Prediction of “mode of action”