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Open Access Highly Accessed Software

PSAIA – Protein Structure and Interaction Analyzer

Josip Mihel1, Mile Šikić1*, Sanja Tomić2, Branko Jeren1 and Kristian Vlahoviček3*

Author Affiliations

1 Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia

2 Rudjer Bošković Institute, Bijenička 54, HR-10000 Zagreb, Croatia

3 Bioinformatics Group, Department of Molecular Biology, Division of Biology, Faculty of Science, University of Zagreb, Horvatovac 102a, 10000 Zagreb, Croatia

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BMC Structural Biology 2008, 8:21  doi:10.1186/1472-6807-8-21

Published: 9 April 2008

Abstract

Background

PSAIA (Protein Structure and Interaction Analyzer) was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites.

Results

In addition to most relevant established algorithms, PSAIA offers a new method PIADA (Protein Interaction Atom Distance Algorithm) for the determination of residue interaction pairs. We found that PIADA produced more satisfactory results than comparable algorithms implemented in PSAIA.

Particular advantages of PSAIA include its capacity to combine different methods to detect the locations and types of interactions between residues and its ability, without any further automation steps, to handle large numbers of protein structures and complexes. Generally, the integration of a variety of methods enables PSAIA to offer easier automation of analysis and greater reliability of results.

PSAIA can be used either via a graphical user interface or from the command-line. Results are generated in either tabular or XML format.

Conclusion

In a straightforward fashion and for large sets of protein structures, PSAIA enables the calculation of protein geometric parameters and the determination of location and type for protein-protein interaction sites. XML formatted output enables easy conversion of results to various formats suitable for statistic analysis.

Results from smaller data sets demonstrated the influence of geometry on protein interaction sites. Comprehensive analysis of properties of large data sets lead to new information useful in the prediction of protein-protein interaction sites.