title: N-Terminal myristoylation predictions by ensembles of neural networks.
abstract: N-terminal myristoylation is a post-translational modification that causes the addition of a myristate to a glycine in the N-terminal end of the amino acid chain. This work presents neural network (NN) models that learn to discriminate myristoylated and nonmyristoylated proteins. Ensembles of 25 NNs and decision trees were trained on 390 positive sequences and 327 negative sequences. Experiments showed that NN ensembles were more accurate than decision tree ensembles. Our NN predictor evaluated by the leave-one-out procedure, obtained a false positive error rate equal to 2.1%. That was better than the PROSITE pattern for myristoylation for which the false positive error rate was 22.3%. On a recent version of Swiss-Prot (41.2), the NN ensemble predicted 876 myristoylated proteins, while 1150 proteins were predicted by the PROSITE pattern for myristoylation. Finally, compared to the well-known NMT predictor, the NN predictor gave similar results. Our tool is available under http://www.expasy.org/tools/myristoylator/myristoylator.html.
authors.affiliation: Swiss Institute of Bioinformatics, Geneva, Switzerland. Guido.Bologna@isb-sib.ch
authors.forename: Guido
authors.initials: G
authors.lastname: Bologna
authors.forename: Cédric
authors.initials: C
authors.lastname: Yvon
authors.forename: Séverine
authors.initials: S
authors.lastname: Duvaud
authors.forename: Anne-Lise
authors.initials: AL
authors.lastname: Veuthey
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry / Q000378:metabolism*; D001185:Artificial Intelligence; D016208:Databases, Factual; D005998:Glycine / Q000378:metabolism; D009227:Myristic Acids / Q000378:metabolism; D016571:Neural Networks, Computer; D011336:Probability; D011499:Protein Processing, Post-Translational; D012680:Sensitivity and Specificity
chemical_list: D000596:Amino Acids; D009227:Myristic Acids; D005998:Glycine