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Motivation: Similarity based methods have been widely used in order to

infer the properties of genes and gene products containing little or no

experimental annotation. The most popular ones are the sequence

similarity search methods such as BLAST. New approaches that overcome

the limitations of the methods that relying solely upon sequence similarity

are rising. One of these novel approaches is the comparison of the

organization/architecture of the structural domains in the proteins. The

idea is that the shared structural units may indicate shared evolutionary

and functional properties associated between these units.

Results: Here we propose a new algorithm for the comparison of domain

architectures in order to identify similarities and to propagate functional

annotations between the proteins in the UniProt Database. The method

“UniProt Domain Architecture Alignment” is unique from previous

approaches in three major ways: (i) the use of InterPro Database for the

domain annotation, (ii) the incorporation of the domain weights into the

dynamic programming step, and (iii) the inclusion of information regarding

non-annotated regions in the proteins into the domain architectures. The

performance of the method was measured through the identification of

orthology using the OMA database (F1 score: 0.62). The results indicated

the effectiveness of the approach for similarity detection. We plan to

integrate the algorithm into a learning based system for the automatic

annotation of uncharacterized proteins in the UniProtKB/TrEMBL database.

ABSTRACT

Generation of the Domain Architectures:

1) Collect the hits for each protein from InterPro.

2) Remove all non-domain type hits.

3) Order the domain hits sequentially.

4) Merge the hits from the same InterPro hierarchy into single hits using

the condensed view algorithm provided by this resource.

5) Treat the overlapping hits from unrelated InterPro entries.

6) Add the stretches of residues without domain hits (> 30 a.a.) as “GAP”

domains in the DAs.

Domain weighting:

Inverse domain frequency:

Neighboring domain count:

Term frequency:

Domain hit sizes:

Domain similarity measure:

Weight matrix:

Final scoring matrix:

Weighted Domain Architecture Alignment:

Needleman-Wunsch Global Sequence Alignment algorithm (Needleman and

Wunsch, 1970) is the core of the proposed DA alignment method:

• Modification of the algorithm in order to work with 7137 distinct

InterPro domains as its alphabet instead of 20 amino acids.

• Integration of the domain weights into a scoring matrix in order to

direct the alignments to achieve maximum weighted scores.

• Scoring of the non-annotated regions on proteins as gaps during the

alignment.

METHODOLOGY RESULTS & DISCUSSION

InterPro Domains, DAs and DA Alignment

Domain annotation coverage

difference b/w domain databases:

Statistics about the directionality in DAs:

Evaluation of the performance of the method

The performance of the proposed method in identification of orthologous

protein sequences proteins from Orthologous Matrix project (OMA) release

March 2014 (Altenhoff, et al., 2011).

The randomly selected UniProtKB/SwissProt proteins from the OMA groups

were subjected to the DA alignment procedure.

The performance of the method was evaluated by measuring its ability to

identify the orthologous proteins as orthologs usually share the same

function.

CONCLUSIONS

Here we proposed a new approach in the field of protein function

prediction. The method is distinguished from all previous approaches in

three main aspects:

1) Different types of domain weights are integrated into the scoring

matrix to direct the alignment of DAs to an optimal solution.

2) The information pertaining to the non-annotated regions of the

proteins are integrated into DAs and thus scored during the alignment.

3) InterPro is used as the domain resource in order to increase the

coverage of domain annotation on the protein sequences.

The results of the ortholog sequence analysis suggest that the proposed

approach can identify the functional relationships between proteins.

As future work, we are planning to use the method in a pipeline for the

automatic annotation of the proteins in the UniPRotKB/TrEMBL database.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

Correspondence: tuncadogan@gmail.com

Tunca Doğan1, Alex Bateman1, Maria J. Martin1

UniProt Domain Architecture Alignment: A New Approach for Protein Similarity Search using InterPro Domain Annotation

INTRODUCTION

• Discovery of functional properties for proteins is a key step in

biomedical research.

• Experimental identification of proteins is still a quite laborious and

expensive task.

• This led to many computational methods being developed to infer the

unknown properties of the proteins based on their sequence similarities

to experimentally annotated proteins (i.e. BLAST, PSI-BLAST).

• Different approaches have been tried lately, especially in the field of

protein function prediction, to augment the performance of sequence

methods.

• One of these approaches is the study of protein domains: the structural

building blocks in proteins that are able to function and fold

independently from the rest of the protein.

• The concept of domain architectures (DA), defined as the

organizational properties of a protein regarding the domains it

contains.

• Here we present the UniProt Domain Architecture Alignment procedure

for the detection of functional similarities between proteins containing

domain annotation:

Four types of attributes are incorporated into the measurement of

the domain architectural similarities: domain content, order,

position and recurrence.

The proposed method incorporates domain information from the

InterPro database in order to increase the domain information

coverage on the proteins.

Figure 1. Different types of overlapping domain hits on protein sequences

Figure 2. Resolution process for the overlap hits.

Figure 3. Domain hit statistics of UniProtKB/SwissProt proteins from various databases

Figure 4. The fraction of overlap hits by InterPro domains on the residues of all UniProtKB/SwissProt proteins

Overlap domain hits problem in

the InterPro database:

Figure 5. Co-occurrence frequencies of a selection of domain pairs, hit together on UniProtKB/SwissProt proteins (InterPro accessions of the domains are shown at the top of the bars).

Table 1. Performance results of the proposed method in the identification of orthologous proteins in OMA groups.

ACKNOWLEDGEMENTS

T.D. thanks Andrew Nightingale for the editorial work on the manuscript.

Funding: This work was supported by TUBITAK BIDEB-2219 post-doctoral

research fellowship program.

Nt : total number of proteins in the test set Nd : number of proteins containing domain d

Ed : total number of distinct neighboring domains to d

Nd,p : domain copy number of domain d in protein p Dp : total number of domains in protein p

Zmin(d1,d2) & Zmax(d1,d2) : sizes of the shorter and longer hits respectively; of domain d in protein 1 and in protein 2 Zav : average size of all domain hits on all proteins in the set

Od,e : similarity ratio between domain d and domain e

Ap1,p2, Cp1,p2, Fp1,p2, Sp1,p2 & Ip1,p2 : local weight matrices

Rp1,p2 : raw scoring matrix Wp1,p2 : general weight matrix between proteins 1 and 2

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