Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Refining Neural Network Predictions for Helical Transmembrane Proteins by Dynamic Programming
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
A Hidden Markov Model for Predicting Transmembrane Helices in Protein Sequences
ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology
A novel composite model approach to improve software quality prediction
Information and Software Technology
Prediction of protein inter-domain linkers using compositional index and simulated annealing
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
A transmembrane helix (TMH) topology prediction is becoming a central problem in bioinformatics because the structure of TM proteins is difficult to determine by experimental means. Therefore, methods which could predict the TMHs topologies computationally are highly desired. In this paper we introduce TMHindex, a method for detecting TMH segments solely by the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index deduced from a combination of the difference in amino acid appearances in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, genetic algorithm was employed to find the optimal threshold value to separate TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in 70 testing protein sequences. The level of accuracy achieved using TMHindex in comparison to recent methods for predicting the topology of TM proteins is a strong argument in favor of our method.