The low complexity of minimotif patterns results in a high false-positive prediction rate, hampering protein function prediction. A multi-filter algorithm, trained and tested on a linear regression model, support vector machine model, and neural network model, using a large dataset of verified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives. An optimal threshold for the best accuracy reaches an overall accuracy above 90%, while a stringent threshold for the best specificity generates less than 1% false positives or even no false positives and still produces more than 90% true positives for the linear regression and neural network models. The minimotif multi-filter with its excellent accuracy represents the state-of-the-art in minimotif prediction and is expected to be very useful to biologists investigating protein function and how missense mutations cause disease.
Amino acid sequence; Biology; Genetics; Minimotifs; Peptides; Proteins
Biology | Computer Sciences | Life Sciences | Molecular Biology | Structural Biology
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Merlin, J. C.,
Gryk, M. R.,
Achieving High Accuracy Prediction of Minimotifs.
PLoS One, 7(9),