Volume 10 Supplement 13
Highlights from the Fifth International Society for Computational Biology (ISCB) Student Council Symposium
Prediction of supersecondary structure in αhelical and βbarrel transmembrane proteins
 Van Du Tran^{1}Email author,
 Philippe Chassignet^{1} and
 JeanMarc Steyaert^{1}
DOI: 10.1186/1471210510S13O3
© Tran et al; licensee BioMed Central Ltd. 2009
Published: 19 October 2009
Background
Transmembrane proteins are divided into two main classes based upon their conformation: βbarrels and αhelical bundles. Computational methods based on learning are poorly tractable since the transmembrane structures are difficult to be determined by standard experimental methods. Generally, those structures are not only a series of βstrands or αhelices where each is bonded to the ones immediately before and after in the primary sequence, but they may contain Greek key, sometimes Jelly roll, motifs as well. This level of structure may be described as a permutation of the order of the bonded segments [1].
Methods
The algorithm is implemented for general βbarrels or αbundles in which the Greek key motifs may occur as a topological signature. The protein folding problem is modeled into finding the longest closed path in a graph G( , ℰ, ν, ω) with respect to some given permutation, where:

V is the set of vertices which represent segments (amino acid subsequences) satisfying given conformational constraints (e.g. strand length, propensity to be a βstrand). A simple Markov model is used to discard the segments unlikely to be a βstrand or an αhelix.

E is the set of edges connecting every two segments satisfying given adjacency constraints (e.g. length, flexibility, propensity of the turn or loop in between).

ν, φ and λ are weight functions defined for every segment, pairing segments and turn or loop respectively, based on hydrophobicity, electrostatic interaction and environmental effects (e.g. extracellular, intracellular effect, membrane thickness). These functions are tuned accordingly to the studied class of proteins.
By dynamic programming, the algorithm runs in O(n^{3}) for an identity permutation, and at most O(n^{5}) for the Greek key motifs, where n is the number of amino acids. Finally, a threedimensional structure is constructed using the geometric criteria.
Results
The prediction accuracy, for the class of βbarrel transmembrane proteins, evaluated by the percentage of wellpredicted residues, reaches 70–85%. The number of strands is found correctly, whereas another main geometric characteristic of βbarrel, the shear number, is relatively suitable. The running time for a graph of 1400 vertices (potential segments in a sequence of 180 amino acids) is about 40 seconds. These results compare favorably to existing works as [2–4].
Conclusion
Our algorithm can be used to predict the structure of different families of proteins and is tested with the βbarrel transmembrane proteins. We consider to carry out a screening on genomes of certain species such as Paramecium and Neisseria meningitidis.
Authors’ Affiliations
References
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Copyright
This article is published under license to BioMed Central Ltd.