protein function prediction based on domain content

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Ankita Sarangi School of Informatics, IUB Capstone Presentation, May 11, 2009 Advisor : Yuzhen Ye

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Protein Function Prediction Based on Domain Content. Ankita Sarangi School of Informatics, IUB Capstone Presentation, May 11, 2009 Advisor : Yuzhen Ye. What information can be used for function annotation?. Sequence based approaches - PowerPoint PPT Presentation

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Page 1: Protein Function Prediction Based on Domain Content

Ankita Sarangi

School of Informatics, IUBCapstone Presentation,

May 11, 2009

Advisor : Yuzhen Ye

Page 2: Protein Function Prediction Based on Domain Content

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.

Page 3: Protein Function Prediction Based on Domain Content

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

Page 4: Protein Function Prediction Based on Domain Content

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?

Page 5: Protein Function Prediction Based on Domain Content

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

Page 6: Protein Function Prediction Based on Domain Content

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

Page 7: Protein Function Prediction Based on Domain Content

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.

Page 8: Protein Function Prediction Based on Domain Content

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

Page 9: Protein Function Prediction Based on Domain Content
Page 10: Protein Function Prediction Based on Domain Content

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

(SCOP ID - 63380)

(SCOP ID – 52343)

Page 11: Protein Function Prediction Based on Domain Content

100% IEA

Page 12: Protein Function Prediction Based on Domain Content

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

Page 13: Protein Function Prediction Based on Domain Content

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 proteinGene Ontology: Biological Process: GO:005514 is_a child of GO:0008152molecular function: GO:0016491PFAM Domains : PF07992 - Pyr_redox (Q0A5G3)

OR PF08021. FAD_binding (A4FEM2, A1WVX7 )Evidence : IEA (Inferred Electronic Annotation)Proteins: Q0A5G3, A4FEM2, A1WVX7

Ferredoxin reductase-like, C-terminal NADP-linked domain

  Protein Name: Protein-P-II

uridylyltransferaseGene Ontology: Biological Process: GO:0008152 is a parent of GO:0006807

  molecular function: GO:0016491

PFAM: PF01966 - NAD BindingEvidence : IEA (Inferred Electronic Annotation) Protein: Q6MLQ2

Ref: http://www.uniprot.org/uniprot/

Page 14: Protein Function Prediction Based on Domain Content

PreATP-grasp domain(SCOP ID = 52440)

Glutathione synthetase ATP-binding domain-like (SCOP ID = 56059)

Lots of different enzymes forming carbon–nitrogen bondshave this combination of domains. Both domains contribute tosubstrate binding and the active site, and the C-terminaldomain binds ATP as well as the other substrate;

Page 15: Protein Function Prediction Based on Domain Content

75% IEA

Page 16: Protein Function Prediction Based on Domain Content

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

Page 17: Protein Function Prediction Based on 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

Page 18: Protein Function Prediction Based on Domain Content

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

Page 19: Protein Function Prediction Based on Domain Content

Gene Ontology database gene_association.goa_uniprot Swisspfam

Page 20: Protein Function Prediction Based on Domain Content

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

Page 21: Protein Function Prediction Based on Domain Content
Page 22: Protein Function Prediction Based on Domain Content
Page 23: Protein Function Prediction Based on Domain Content

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.

Page 24: Protein Function Prediction Based on Domain Content

I would like to thank:Dr. Yuzhen Ye

Faculty of the Department of BioinformaticsDrs. Dalkilic, Kim, Hahn, Radivojac, Tang Linda Hostetter and Rachel Lawmaster

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