Defining and searching for structural motifs using DeepView/Swiss-PdbViewer

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Abstract

Background

Today, recognition and classification of sequence motifs and protein folds is a mature field, thanks to the availability of numerous comprehensive and easy to use software packages and web-based services. Recognition of structural motifs, by comparison, is less well developed and much less frequently used, possibly due to a lack of easily accessible and easy to use software.

Results

In this paper, we describe an extension of DeepView/Swiss-PdbViewer through which structural motifs may be defined and searched for in large protein structure databases, and we show that common structural motifs involved in stabilizing protein folds are present in evolutionarily and structurally unrelated proteins, also in deeply buried locations which are not obviously related to protein function.

Conclusions

The possibility to define custom motifs and search for their occurrence in other proteins permits the identification of recurrent arrangements of residues that could have structural implications. The possibility to do so without having to maintain a complex software/hardware installation on site brings this technology to experts and non-experts alike.

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Most cited references 44

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PISCES: a protein sequence culling server.

Guoli Wang, Roland L. Dunbrack (2003)

PISCES is a public server for culling sets of protein sequences from the Protein Data Bank (PDB) by sequence identity and structural quality criteria. PISCES can provide lists culled from the entire PDB or from lists of PDB entries or chains provided by the user. The sequence identities are obtained from PSI-BLAST alignments with position-specific substitution matrices derived from the non-redundant protein sequence database. PISCES therefore provides better lists than servers that use BLAST, which is unable to identify many relationships below 40% sequence identity and often overestimates sequence identity by aligning only well-conserved fragments. PDB sequences are updated weekly. PISCES can also cull non-PDB sequences provided by the user as a list of GenBank identifiers, a FASTA format file, or BLAST/PSI-BLAST output.

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Least-squares fitting of two 3-d point sets.

K. Arun, T S Huang, S. Blostein (1987)

Two point sets {pi} and {p'i}; i = 1, 2,..., N are related by p'i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.

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CATH--a hierarchic classification of protein domain structures.

C. A. Orengo, A D Michie, S. Jones … (1997)

Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can also be assigned. The ever increasing number of known protein structures is too large to classify all proteins manually, therefore, automatic methods are needed for fast evaluation of protein structures. We present a semi-automatic procedure for deriving a novel hierarchical classification of protein domain structures (CATH). The four main levels of our classification are protein class (C), architecture (A), topology (T) and homologous superfamily (H). Class is the simplest level, and it essentially describes the secondary structure composition of each domain. In contrast, architecture summarises the shape revealed by the orientations of the secondary structure units, such as barrels and sandwiches. At the topology level, sequential connectivity is considered, such that members of the same architecture might have quite different topologies. When structures belonging to the same T-level have suitably high similarities combined with similar functions, the proteins are assumed to be evolutionarily related and put into the same homologous superfamily. Analysis of the structural families generated by CATH reveals the prominent features of protein structure space. We find that nearly a third of the homologous superfamilies (H-levels) belong to ten major T-levels, which we call superfolds, and furthermore that nearly two-thirds of these H-levels cluster into nine simple architectures. A database of well-characterised protein structure families, such as CATH, will facilitate the assignment of structure-function/evolution relationships to both known and newly determined protein structures.

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Author and article information

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Journal ID (iso-abbrev): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central

ISSN (Electronic): 1471-2105

Publication date Collection: 2012

Publication date (Electronic): 23 July 2012

Volume: 13

Page: 173

Affiliations

[1 ]Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland

[2 ]Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland

Article

Publisher ID: 1471-2105-13-173

DOI: 10.1186/1471-2105-13-173

PMC ID: 3436773

PubMed ID: 22823337

SO-VID: 98676d1a-4be3-4c7f-93a8-bd12d1e94383

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Defining and searching for structural motifs using DeepView/Swiss-PdbViewer

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Abstract

Background

Results

Conclusions

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Genetoberfest

Most cited references 44

PISCES: a protein sequence culling server.

Least-squares fitting of two 3-d point sets.

CATH--a hierarchic classification of protein domain structures.

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