Protein Function Prediction Based on Domain Content

1. Protein Function Prediction Based on Domain Content Ankita Sarangi School of Informatics, IUB Capstone Presentation, May 11, 2009 Advisor : Yuzhen Ye

2. What information can be used for function annotation? • Sequence based approaches • Protein A has function X, and protein B is a homolog (ortholog) of protein A; Hence B has function X • Structure-based approaches • Protein A has structure X, and X has specific structural features; Hence X’s function sites are used to assign function to the Protein A • Motif-based approaches (sequence motifs, 3D motifs) • A group of genes have function X and they all have motif Y; protein A has motif Y; Hence protein A’s function might be related to X • “Guilt-by-association” • Gene A has function X and gene B is often “associated” with gene A, B might have function related to X • Associations • Domain fusion, phylogenetic profiling, PPI, etc.

3. Function Annotation Using Protein Domains • A protein domain is a part of protein sequence and structure that can evolve, function, and exist independently of the rest of the protein chain. • Each domain forms a compact three-dimensional structure and often can be independently folded. • Many proteins consist of several structural domains. • Among relevant sequence features of a protein, domains occupy a key position. They are sequential and structural motifs found independently in different proteins, in different combinations, and as such seem to be the building blocks of proteins

4. Supra-domains • However, it is also known that certain sets of independent domains are frequently found together, which may indicate functional cooperation. • Supra- Domains : A supra-domain is defined as a domain combination in a particular N-to-C-terminal orientation that occurs in at least two different domain architectures in different proteins with: (i) different types of domains at the N and C-terminal end of the combination; or (ii) different types of domains at one end and no domain at the other. ` • A type of Supra-domain are ones whose activity is created at the interface between the two domains of a protein • (Ref: JMB, 2004, 336:809–823) • We may make mistakes if we do function prediction based on individual domains • We know proteins that have domain A and B have function F, what about proteins having domain A or domain B only?

5. My Work • A survey of mis-annotation based on single domains • We are interested to know how serious this problem is in the current annotation system • There is no systematic survey on this so far • Function annotation using domain patterns (domain combinations) instead of individual domains • Utilize the relationship of the predicted functions (as shown in the GO directed acyclic graph of functions) • Provide a web-tool and visualization of the predicted functions and their relationship with domain patterns

6. Protein Domains: SUPERFAMILY • SUPERFAMILY is a database of structural and functional protein annotations for all completely sequenced organisms. • The SUPERFAMILY web site and database provides protein domain assignments, at the SCOP 'superfamily' and 'family' levels, for the predicted protein sequences in over 900 organisms • We made a local copy of this database

7. Protein Function: GO • The GENE ONTOLOGY(GO) project is a collaborative effort to address the need for consistent descriptions of gene products in different databases. • Consists of three structured, controlled vocabularies (ontologies) that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner.

8. A Survey of Mis-Annotations Using Single Domains We looked at several supra-domains listed in this paper: Supra-domains: Evolutionary Units Larger than Single Protein Domains; Voget etal.; J. Mol. Biol. (2004) 336, 809–823

9. Finding Functions for Domains and Domain Combinations Superfamily Use the SCOP ID of the domains to obtain Gene Identifiers associated with the supra-domains as well as their individual domains UniProt ID mapping file (is a tab-delimited table, which includes mappings for 20 different sequence identifier types(example: ENSGALP000, AN7518.2, Afu1g003, gi|41409236|ref|NP_962072.1|)and gene_association.goa_uniprot (GO assignments for the UniProtKnowledgeBase (UniProtKB)) To obtain Swiss prot ID, GO ID Find Gene Ontology functions that are associated with proteins which contain both the domains and the individual domains

10. Supra-domain Example 1 (SCOP ID - 63380) (SCOP ID – 52343) The N-terminal domain binds FAD and the C-terminal domain binds NADPH. The FAD acts as an intermediate in electron transfer between NADPH and substrate, and this domain combination is used by many different enzymes

11. Function Annotation for Proteins with Supra-Domains and Single Domains 100% IEA

12. Function Example : GO:0016491 ((oxidoreductase activity)) • 10 proteins with Supra domains annotated to GO:0016491---- 2491proteins with Supra domains • 3 proteins with Riboflavin Synthase domain-like annotated to GO:0016491 --- 42 proteins with Riboflavin Synthase domain-like • 1 protein with reductase-like, C-terminal NADP-linked domain annotated to GO:0016491--- 47 protein with reductase-like, C-terminal NADP-linked domain • Specific proteins searched and presence and absence of the combined domain was confirmed along with GO ID as well as annotation evidence which was found to be Inferred Electronic Annotation

13. Example : GO:0016491 Supra –Domains: Riboflavin Synthase domain-like, Ferredoxin reductase-like, C-terminal NADP-linked domain • Protein Name : Oxidoreductase FAD-binding domain protein Gene Ontology : Biological Process: GO:005514 is_a child of GO:0008152 molecular function: GO:0016491 PFAM domains: PF00970. FAD_binding PF00175 NAD_binding Evidence : IEA (Inferred Electronic Annotation) Proteins: A4FHX1 , A1UCP3, A4T5V2, A3PWD0 ,Q1BCA1 • Protein Name : Sulfide dehydrogenase (Flavoprotein) subunit SudA sulfide dehydrogenase (Flavoprotein) subunit SudB Gene Ontology: Biological Process: GO:005514 is_a child of GO:0008152 molecular function: GO:0016491 PFAM Domains : PF00175. NAD_binding PF07992. Pyr_redox (FAD_pyr_nucl-diS_OxRdtase.) Evidence : IEA (Inferred Electronic Annotation) Proteins: Q2J1U9, Q13CJ3, Q5PB24 • Protein Name : Dihydroorotate dehydrogenase electron transfer subunit, putative Gene Ontology: Biological Process: GO:005514 is_a child of GO:0008152 molecular function: GO:0016491 PFAM domains: PF00970. FAD_binding PF00175. NAD_binding Evidence : IEA (Inferred Electronic Annotation)   Proteins: A3CN91, Q73P17 Riboflavin Synthase domain-like • Protein Name : Putative uncharacterized protein Gene Ontology: Biological Process: GO:005514 is_a child of GO:0008152 molecular function: GO:0016491 PFAM Domains : PF07992 - Pyr_redox (Q0A5G3) OR PF08021. FAD_binding (A4FEM2, A1WVX7 ) Evidence : IEA (Inferred Electronic Annotation) Proteins: Q0A5G3, A4FEM2, A1WVX7 Ferredoxinreductase-like, C-terminal NADP-linked domain • Protein Name: Protein-P-II uridylyltransferase Gene Ontology: Biological Process: GO:0008152 is a parent of GO:0006807 molecular function: GO:0016491 PFAM: PF01966 - NAD Binding Evidence : IEA (Inferred Electronic Annotation) Protein: Q6MLQ2 • Ref: http://www.uniprot.org/uniprot/

14. Supra-domain Example 2 PreATP-grasp domain (SCOP ID = 52440) Glutathione synthetase ATP-binding domain-like (SCOP ID = 56059) Lots of different enzymes forming carbon–nitrogen bonds have this combination of domains. Both domains contribute to substrate binding and the active site, and the C-terminal domain binds ATP as well as the other substrate;

15. Function Annotation for Proteins with Supra-Domains and Single Domains 75% IEA

16. RESULTS • Functional annotations were found to be shared by proteins having the Supra-domains as well as the single domains. • The percentage of proteins having Supra-domains were much higher than single domains. • Since, both domains are required for the function of the protein, the functions assigned to single domain proteins may be said to be mis-annotated. • This study gave us motivation of developing a computational tool for function annotation based on domain combinations (domain patterns) instead of individual domains

17. A Novel Computational Tool for Function Annotation Using Domain Content • Utilize the relationship of the predicted functions (as shown in the GO directed acrylic graph of functions) • Provide a web-tool and visualization of the predicted functions and their relationship with domain patterns

18. Probabilistic Approach • Functional annotation term F (in this case a Gene Ontology) and a domain set D. The probability that a protein exhibiting D would possess F is modeled as P(F|D)=P(D|F)P(F)/P(D) (i.e., posterior probability of a function given a set of domains D; P(D|F), P(F), and P(D) can be learned from proteins with known functions) Ref: Predicting protein function from domain content; Forslund et al;Bioinformatics,Vol. 24 no. 15 2008, pages 1681–1687

19. Datasets Used • Gene Ontology database • gene_association.goa_uniprot • Swisspfam

20. Methodology For an input domain pattern (pfam domains): • All the Pfam pattern containing the given pattern are extracted (e.g., if input domain pattern is A + B, all the domain patterns that contain this domain pattern will be considered, such as A + B + C, etc) • GO function associated with all the domain patterns are extracted • Calculate the probability using P(F|D)=P(D|F)P(F)/P(D) • number of proteins that occurs with the domain pattern possessing the function • If the percentage probabilities lie close to one another than the parent GO function is found and a diagram depicting a sum of the distance of the parent from the two children is printed; otherwise the GO terms that have P(F|D) >= 0.9 * Max{P(F|D)} are extracted • Summary graph providing all the GO functions associated with the pattern search

21. Example Domain Input: PF03446 PF00393

22. Example Domain Input: PF02801

23. Conclusion • A survey of annotation based on single domains • Function annotation using domain patterns (domain combinations) instead of individual domains • To DO: • Do a more thorough survey with the annotation studies of single domains • Define all the relationships between the GO ID’s in the Summary Graph • Refine and test the computational tool.

24. Acknowledgements • I would like to thank: Dr. Yuzhen Ye Faculty of the Department of Bioinformatics Drs. Dalkilic, Kim, Hahn, Radivojac, Tang Linda Hostetter and Rachel Lawmaster Family and Friends