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Accurate Domain Identification with Structure-Anchored Hidden Markov Models, saHMMs
Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University, Faculty of Science and Technology, Department of Chemistry. Umeå University, Faculty of Science and Technology, Umeå Centre for Molecular Pathogenesis (UCMP).
Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University, Faculty of Science and Technology, High Performance Computing Center North (HPC2N). (UMIT)
Umeå University, Faculty of Science and Technology, Umeå Centre for Molecular Pathogenesis (UCMP) (Faculty of Science and Technology). Umeå University, Faculty of Science and Technology, Department of Chemistry.
2009 (English)In: Proteins: Structure, Function, and Genetics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 76, no 2, 343-352 p.Article in journal (Refereed) Published
Abstract [en]

The ever increasing speed of DNA sequencing widens the discrepancy between the number of known gene products, and the knowledge of their function and structure. Proper annotation of protein sequences is therefore crucial if the missing information is to be deduced from sequence-based similarity comparisons. These comparisons become exceedingly difficult as the pairwise identities drop to very low values. To improve the accuracy of domain identification, we exploit the fact that the three-dimensional structures of domains are much more conserved than their sequences. Based on structure-anchored multiple sequence alignments of low identity homologues we constructed 850 structure-anchored hidden Markov models (saHMMs), each representing one domain family. Since the saHMMs are highly family specific, they can be used to assign a domain to its correct family and clearly distinguish it from domains belonging to other families, even within the same superfamily. This task is not trivial and becomes particularly difficult if the unknown domain is distantly related to the rest of the domain sequences within the family. In a search with full length protein sequences, harbouring at least one domain as defined by the structural classification of proteins database (SCOP), version 1.71, versus the saHMM database based on SCOP version 1.69, we achieve an accuracy of 99.0%. All of the few hits outside the family fall within the correct superfamily. Compared to Pfam_ls HMMs, the saHMMs obtain about 11% higher coverage. A comparison with BLAST and PSI-BLAST demonstrates that the saHMMs have consistently fewer errors per query at a given coverage. Within our recommended E-value range, the same is true for a comparison with SUPERFAMILY. Furthermore, we are able to annotate 232 proteins with 530 nonoverlapping domains belonging to 102 different domain families among human proteins labelled unknown in the NCBI protein database. Our results demonstrate that the saHMM database represents a versatile and reliable tool for identification of domains in protein sequences. With the aid of saHMMs, homology on the family level can be assigned, even for distantly related sequences. Due to the construction of the saHMMs, the hits they provide are always associated with high quality crystal structures. The saHMM database can be accessed via the FISH server at

Place, publisher, year, edition, pages
2009. Vol. 76, no 2, 343-352 p.
Keyword [en]
protein domain, structure alignment, remote homologue, sequence annotation, protein family, protein superfamily
National Category
Biochemistry and Molecular Biology Biophysics
URN: urn:nbn:se:umu:diva-24700DOI: 10.1002/prot.22349OAI: diva2:227210
Jeanette Hargbo.Available from: 2009-07-10 Created: 2009-07-10 Last updated: 2012-04-11Bibliographically approved
In thesis
1. Structural Information and Hidden Markov Models for Biological Sequence Analysis
Open this publication in new window or tab >>Structural Information and Hidden Markov Models for Biological Sequence Analysis
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Bioinformatics is a fast-developing field, which makes use of computational methods to analyse and structure biological data. An important branch of bioinformatics is structure and function prediction of proteins, which is often based on finding relationships to already characterized proteins. It is known that two proteins with very similar sequences also share the same 3D structure. However, there are many proteins with similar structures that have no clear sequence similarity, which make it difficult to find these relationships.

In this thesis, two methods for annotating protein domains are presented, one aiming at assigning the correct domain family or families to a protein sequence, and the other aiming at fold recognition. Both methods use hidden Markov models (HMMs) to find related proteins, and they both exploit the fact that structure is more conserved than sequence, but in two different ways.

Most of the research presented in the thesis focuses on the structure-anchored HMMs, saHMMs. For each domain family, an saHMM is constructed from a multiple structure alignment of carefully selected representative domains, the saHMM-members. These saHMM-members are collected in the so called "midnight ASTRAL set", and are chosen so that all saHMM-members within the same family have mutual sequence identities below a threshold of about 20%.

In order to construct the midnight ASTRAL set and the saHMMs, a pipe-line of software tools are developed. The saHMMs are shown to be able to detect the correct family relationships at very high accuracy,

and perform better than the standard tool Pfam in assigning the correct domain families to new domain sequences. We also introduce the FI-score, which is used to measure the performance of the saHMMs, in order to select the optimal model for each domain family.

The saHMMs are made available for searching through the FISH server, and can be used for assigning family relationships to protein sequences.

The other approach presented in the thesis is secondary structure HMMs (ssHMMs).

These HMMs are designed to use both the sequence and the predicted secondary structure of a query protein when scoring it against the model.

A rigorous benchmark is used, which shows that HMMs made from multiple sequences result in better fold recognition than those based on single sequences. Adding secondary structure information to the HMMs improves the ability of fold recognition further, both when using true and predicted secondary structures for the query sequence.

Abstract [sv]

Bioinformatik är ett område där datavetenskapliga och statistiska metoder används för att analysera och strukturera biologiska data. Ett viktigt område inom bioinformatiken försöker förutsäga vilken tredimensionell struktur och funktion ett protein har, utifrån dess aminosyrasekvens och/eller likheter med andra, redan karaktäriserade, proteiner. Det är känt att två proteiner med likande aminosyrasekvenser också har liknande tredimensionella strukturer. Att två proteiner har liknande strukturer behöver dock inte betyda att deras sekvenser är lika, vilket kan göra det svårt att hitta strukturella likheter utifrån ett proteins aminosyrasekvens.

Den här avhandlingen beskriver två metoder för att hitta likheter mellan proteiner, den ena med fokus på att bestämma vilken familj av proteindomäner, med känd 3D-struktur, en given sekvens tillhör, medan den andra försöker förutsäga ett proteins veckning, d.v.s. ge en grov bild av proteinets struktur.

Båda metoderna använder s.k. dolda Markov modeller (hidden Markov models, HMMer), en statistisk metod som bland annat kan användas för att beskriva proteinfamiljer. Med hjälp en HMM kan man förutsäga om en viss proteinsekvens tillhör den familj modellen representerar. Båda metoderna använder också strukturinformation för att öka modellernas förmåga att känna igen besläktade sekvenser, men på olika sätt.

Det mesta av arbetet i avhandlingen handlar om strukturellt förankrade HMMer (structure-anchored HMMs, saHMMer). För att bygga saHMMerna används strukturbaserade sekvensöverlagringar, vilka genereras utifrån hur proteindomänerna kan läggas på varandra i rymden, snarare än utifrån vilka aminosyror som ingår i deras sekvenser. I varje proteinfamilj används bara ett särskilt, representativt urval av domäner. Dessa är valda så att då sekvenserna jämförs parvis, finns det inget par inom familjen med högre sekvensidentitet än ca 20%. Detta urval görs för att få så stor spridning som möjligt på sekvenserna inom familjen. En programvaruserie har utvecklats för att välja ut representanter för varje familj och sedan bygga saHMMer baserade på dessa.

Det visar sig att saHMMerna kan hitta rätt familj till en hög andel av de testade sekvenserna, med nästan inga fel. De är också bättre än den ofta använda metoden Pfam på att hitta rätt familj till helt nya proteinsekvenser.

saHMMerna finns tillgängliga genom FISH-servern, vilken alla kan använda via Internet för att hitta vilken familj ett intressant protein kan tillhöra.

Den andra metoden som presenteras i avhandlingen är sekundärstruktur-HMMer, ssHMMer, vilka är byggda från vanliga multipla sekvensöverlagringar, men också från information om vilka sekundärstrukturer proteinsekvenserna i familjen har. När en proteinsekvens jämförs med ssHMMen används en förutsägelse om sekundärstrukturen, och den beräknade sannolikheten att sekvensen tillhör familjen kommer att baseras både på sekvensen av aminosyror och på sekundärstrukturen.

Vid en jämförelse visar det sig att HMMer baserade på flera sekvenser är bättre än sådana baserade på endast en sekvens, när det gäller att hitta rätt veckning för en proteinsekvens. HMMerna blir ännu bättre om man också tar hänsyn till sekundärstrukturen, både då den riktiga sekundärstrukturen används och då man använder en teoretiskt förutsagd.

Place, publisher, year, edition, pages
Umeå: Datavetenskap, 2008. 92 p.
Report / UMINF, ISSN 0348-0542 ; 08.05
HMM, structure alignment, protein structure, secondary structure, remote homologue, annotation, domain family, protein family, protein superfamily, protein fold recognition
National Category
Bioinformatics (Computational Biology)
urn:nbn:se:umu:diva-1629 (URN)978-91-7264-369-7 (ISBN)
Public defence
2008-05-23, MA121, MIT-huset, Umeå universitet, 901 87 Umeå, 10:15 (English)
Jeanette Hargbo.Available from: 2008-04-29 Created: 2008-04-29 Last updated: 2009-08-13Bibliographically approved

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Tångrot, JeanetteKågström, BoSauer, Uwe
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Department of Computing ScienceDepartment of ChemistryUmeå Centre for Molecular Pathogenesis (UCMP)High Performance Computing Center North (HPC2N)Umeå Centre for Molecular Pathogenesis (UCMP) (Faculty of Science and Technology)
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Proteins: Structure, Function, and Genetics
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