Transmembrane helix prediction in proteins using hydrophobicity properties and higher-order statistics
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki GR-54124, Greece.Computers in Biology and Medicine (Impact Factor: 1.24). 07/2008; 38(8):867-80. DOI: 10.1016/j.compbiomed.2008.05.003
Prediction of the transmembrane (TM) helices is important in the study of membrane proteins. A novel method to predict the location and length of both single and multiple TM helices in human proteins is presented. The proposed method is based on a combination of hydrophobicity and higher-order statistics, resulting in a TM prediction tool, namely K(4)HTM. A training dataset of 117 human single TM proteins and two test-datasets containing 499 and 484 human single and multiple TM proteins, respectively, were drawn from the SWISS-PROT public database and used for the optimisation and evaluation of K(4)HTM. Validation results showed that K(4)HTM correctly predicts the entire topology for 99.68% and 93.08% of the sequences in the single and multiple test-datasets, respectively. These results compare favourably with existing methods, such as SPLIT4, TMHMM2, WAVETM and SOSUI, constituting an alternative approach to the TM helix prediction problem.
- [Show abstract] [Hide abstract]
ABSTRACT: Transmembrane proteins are some special and important proteins in cells. Because of their importance and specificity, the prediction of the transmembrane regions has very important theoretical and practical significance. At present, the prediction methods are mainly based on the physicochemical property and statistic analysis of amino acids. However, these methods are suitable for some environments but inapplicable for other environments. In this paper, the multi-sources information fusion theory has been introduced to predict the transmembrane regions. The proposed method is test on a data set of transmembrane proteins. The results show that the proposed method has the ability of predicting the transmembrane regions as a good performance and powerful tool.Journal of Electronics (China) 03/2012; 29(1-2). DOI:10.1007/s11767-012-0797-8
- [Show abstract] [Hide abstract]
ABSTRACT: The identification of transmembrane segments in protein sequences is an important issue in the field of bioinformatics. In this study a method is proposed for the discrimination between proteins with single and multiple transmembrane segments, combining chemical and statistical features of the proteins with higher-order statistics and morphological analysis for protein categorisation. The method was tested on human proteins, extracted from public available databases and the results have shown an efficiency of the proposed algorithm to correctly classify the sequences under study into two classes, for a wide range of transmembrane segment lengths. This paves the way for a more efficient analysis of transmembrane proteins taking into account the individual features and patterns occurring within proteins with single and multiple transmembrane segments.Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:1351-4. DOI:10.1109/IEMBS.2008.4649415
- Introduction to Protein Structure Prediction: Methods and Algorithms, 09/2010: pages 107 - 135; , ISBN: 9780470882207
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.