Comput. Biol. Med. - Disulfide connectivity prediction based on structural information without a prior knowledge of the bonding state of cysteines.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(2212) result(1239) propos(1039) }
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Resumo

Previous studies predicted the disulfide bonding patterns of cysteines using a prior knowledge of their bonding states. In this study, we propose a method that is based on the ensemble support vector machine (SVM), with the structural features of cysteines extracted without any prior knowledge of their bonding states. This method is useful for improving the predictive performance of disulfide bonding patterns. For comparison, the proposed method was tested with the same dataset SPX that was adopted in previous studies. The experimental results demonstrate that bridge classification and disulfide connectivity predictions achieve 96.5% and 89.2% accuracy, respectively, using the ensemble SVM model, which outperforms the traditional method (51.5% and 51.0%, respectively) and the model that is based on a single-kernel SVM classifier (94.6% and 84.4%, respectively). For protein chain and residue classifications, the sensitivity, specificity, and accuracy of ensemble and single-kernel SVM approaches are better than those of the traditional methods. The predictive performances of the ensemble SVM and single-kernel models are identical, indicating that the ensemble model can converge to the single-kernel model for some applications.

Resumo Limpo

previous studi predict disulfid bond pattern cystein use prior knowledg bond state studi propos method base ensembl support vector machin svm structur featur cystein extract without prior knowledg bond state method use improv predict perform disulfid bond pattern comparison propos method test dataset spx adopt previous studi experiment result demonstr bridg classif disulfid connect predict achiev accuraci respect use ensembl svm model outperform tradit method respect model base singlekernel svm classifi respect protein chain residu classif sensit specif accuraci ensembl singlekernel svm approach better tradit method predict perform ensembl svm singlekernel model ident indic ensembl model can converg singlekernel model applic

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