1. Protein Classification A comparison of function inference techniques

2. Why do we need automated classification? Sequencing a genome is only the first step. Between 35-50% of the proteins in sequenced genomes have no assigned functionality. Direct observation of function is costly, time consuming, and difficult.

3. Protein Domains The tertiary structure of many proteins is built from several domains. Often each domain has a separate function to perform for the protein, such as: binding a small ligand (e.g., a peptide in the molecule shown here) spanning the plasma membrane (transmembrane proteins)transmembrane proteins containing the catalytic site (enzymes)enzymes DNA-binding (in transcription factors) providing a surface to bind specifically to another protein In some (but not all) cases, each domain in a protein is encoded by a separate exon in the gene encoding that protein.

4. Inference through sequence similarity ProtoMap: Automatic Classification of Protein Sequences, a Hierarchy of Protein Families, and Local Maps of the Protein Space (1999)

5. Final Goal

6. Observations Sometimes you don’t know where the domains are. It is generally accepted that two sequences with over 30% identity are likely to have the same fold. Homologous proteins have similar functions. Homology is a transitive relationship.

7. Departures Authors do not attempt to define protein domains or motifs. Not dependant on predefined groups or classifications. Chart the space of all proteins in SWISSPROT, as opposed to individual families Produce global organization of sequences.

8. Algorithm Overview We construct a weighted graph where the nodes are protein sequences and the edges are similarity scores. Cluster the network considering only those edges above some threshold. Decrease similarity threshold and repeat.

9. Measuring Sequence Similarity Expectation value used. This the normalized probability of the similarity occurring at random. Lower value implies logarithmically stronger similarity.

10. Blosum62 Scoring Matrix

11. Finding Homologies Very difficult to distinguish a clear threshold between homology and chance similarity. Authors chose e =.1,.1, and.001 for SW, FASTA, and BLAST, respectively. Spent a lot of time empirically determining these thresholds.

12. Clustering Clustering is done iteratively. Start with a threshold of E < 10 -100 Cluster and increase threshold by a factor of 10 5 Sublinear threshold prevents the collapse of sequence space

13. ProtoMap: Results Produces well-defined groups which correlate strongly to protein families in PROSITE and Pfam.

14. Results: Immunoglobin Superfamily

15. ProtoMap: Limitations Analysis performs poorly by families dominated by short/local domains (PH, EGF, ER_TARGET, C2, SH2, SH3, ect…) High scoring, low complexity segments can lead to nonhomogeneous clusters. “Hard” clustering vs. “Soft” clustering Has difficulty classifying multidomain proteins.

16. ProtoMap: Future Directions 3D structure/fold Biological function Domain content Cellular location Tissue specificity Source organism Metabolic pathways

17. Inference through protein interaction networks Functional Classification of Proteins for the Prediction of Cellular Function from a Protein- Protein Interaction Network (2003)

18. PRODISTIN Very similar to ProtoMap, only the data used to produce the graph is a list of binary protein-protein interactions instead of sequence similarity scores Sequence similarity not a dominating factor in PRODISTIN clusters

21. PRODISTIN Results

22. Problems with PRODISTIN Paucity of protein-protein interaction data (average # of connections = 2.6) Either very robust or very indiscriminant

23. Problems: Multidomain and Nonlocal Proteins protein kinases hydrolases ubiquitin… PRODISTIN: Present problems in clustering by biochemical function ProtoMap: Can create undesired connection among unrelated groups

24. Scale-Free Networks P(linking to node i) Node connection probability follows a power law distribution Maximum degree of separation grows as O(lg n) Highly robust under noise, except at hubs and superhubs.

25. The Internet

26. The Movies

27. Social Networks

28. Metabolic Networks http://biocomplexity.indiana.edu/research/bionet/ The E. coli metabolic network is scale-free. Actually, the metabolic networks of all organisms in all three domains of life appear to be scale-free (43 examined) The network diameter of all 43 metabolic networks is the same, irrespective of the number of proteins involved. Is this counter-intuitive? Yes.

29. Protein Domain Networks http://mbe.oupjournals.org/cgi/content/full/18/9/1694 Protein Domains – Nature’s take on writing modular code Reconciles apparent paradox of a fixed network diameter across species – despite vast differences in complexity (some human proteins have 130 domains) Occurrence of specific protein domains in multidomain proteins is scale-free.

30. Protein Domain Graphs Prosite domains have a distribution following the power-law function f(x) = a(b + x) -c, with c =.89. There are few highly connected domains and many rarely connected ones. ProDom and Pfam domains follow the power function y = 2.5 for ProDom y = 1.7 for Pfam

31. Hub Domains in Signaling Pathways

32. Conclusions The accuracy of both ProtoMap and PRODISTIN is limited because they make the tacit assumption of a random network topology. Protein-Protein interaction networks have scale-free topology, foiling PRODISTIN Protein Domain networks have scale-free topology, foiling ProtoMap Any protein classification algorithm that performs better than ProtoMap is probably going to have to address this issue.