IgTM: an algorithm to predict transmembrane domains and topology in proteins.
BACKGROUND
Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages.


RESULTS
We obtained values close to 80% in both specificity and sensitivity. Six datasets have been used for the experiments, considering different encodings for the input sequences. An encoding that includes the topology changes in the sequence (from inside and outside the membrane to it and vice versa) allowed us to obtain the best results. This software is publicly available at: http://www.dsic.upv.es/users/tlcc/bio/bio.html


CONCLUSIONS
We compared our results with other well-known methods, that obtain a slightly better precision. However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.
Peris
Piedachu
P
Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camí de Vera s/n Valencia, Spain. pperis@dsic.upv.es
López
Damián
D
Campos
Marcelino
M
D000465:Algorithms; D000595:Amino Acid Sequence; D002462:Cell Membrane / Q000737:chemistry* / Q000648:ultrastructure*; D003198:Computer Simulation; D008565:Membrane Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017434:Protein Structure, Tertiary; D020539:Sequence Analysis, Protein / Q000379:methods*
D008565:Membrane Proteins
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