- The document discusses using protein features like ELMs (linear motifs) and domains to help predict protein function, as the function is unknown for 40% of human proteins. - It proposes integrating ELMs with other data like protein interaction networks, gene expression data, literature co-mentions to help extract likely ELM-mediated interactions and transfer functional associations between species. - Challenges mentioned include reducing the high false positive rate and developing better ELM models and filters to improve the predictive power for protein function.

1. From ELMs to Function: Interaction Networks and Feature Spaces Lars Juhl Jensen EMBL Heidelberg

2. Function unknown for 40% of human proteins

3. 1AOZ (129 aa) vs. 1PLC (99 aa) scoring matrix: BLOSUM50, gap penalties: -12/-2 15.5% identity; Global alignment score: -23 10 20 30 40 50 60 1AOZ SQIRHYKWEVEYMFWAPNCNENIVMGINGQFPGPTIRANAGDSVVVELTNKLHTEGVVIH .. .. : ... . . ..: . :...: . .: ...:. 1PLC ---------IDVLLGA---DDGSLAFVPSEFS-----ISPGEKIVFK-NNAGFPHNIVFD 10 20 30 40 70 80 90 100 110 120 1AOZ WHGILQRGTPWADGTASISQCAINPGETFFYNFTVDNPGTFFYHGHLGMQRSAGLYGSLI .: :. . . : . :::: .. . .:. : : ::. :.. 1 PLC EDSI-PSGVDASKISMSEEDLLNAKGETFEVALSNKGEYSFYCSPHQG----AGMVGKVT 50 60 70 80 90 1AOZ VDPPQGKKE :. 1PLC VN-------

4. Structural similarity can be deceiving: Two structures from the Cupredoxin superfamily Enzyme Non-enzyme

5. ProtFun: Prediction of protein function from post-translational modifications

6. Protein features determine function # Functional category 1AOZ 1PLC Amino_acid_biosynthesis 0.126 0.070 Biosynthesis_of_cofactors 0.100 0.075 Cell_envelope 0.429 0.032 Cellular_processes 0.057 0.059 Central_intermediary_metabolism 0.063 0.041 Energy_metabolism 0.126 0.268 Fatty_acid_metabolism 0.027 0.072 Purines_and_pyrimidines 0.439 0.088 Regulatory_functions 0.102 0.019 Replication_and_transcription 0.052 0.089 Translation 0.079 0.150 Transport_and_binding 0.032 0.052 # Enzyme/nonenzyme Enzyme 0.773 0.310 Nonenzyme 0.227 0.690 # Enzyme class Oxidoreductase (EC 1.-.-.-) 0.077 0.077 Transferase (EC 2.-.-.-) 0.260 0.099 Hydrolase (EC 3.-.-.-) 0.114 0.071 Lyase (EC 4.-.-.-) 0.025 0.020 Isomerase (EC 5.-.-.-) 0.010 0.068 Ligase (EC 6.-.-.-) 0.017 0.017

9. And now for something completely different: Protein association networks Genomic Neighborhood Species Co-occurrence Gene Fusions Database Imports Exp. Interaction Data Co-expression Literature co-occurrence

11. 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 Transfer associations across species

12. Co-mentioning in the scientific literature Associate abstracts with species Identify gene names in title/abstract Count (co-)occurrences of genes Test significance of associations Calibrate against KEGG maps Transfer associations across species

13. Extracting transient interactions through data integration

17. Thank you!