RFAmyloid: A Web Server for Predicting Amyloid Proteins

International Journal of Molecular Sciences
Mengting NiuKe Han

Abstract

Amyloid is an insoluble fibrous protein and its mis-aggregation can lead to some diseases, such as Alzheimer's disease and Creutzfeldt⁻Jakob's disease. Therefore, the identification of amyloid is essential for the discovery and understanding of disease. We established a novel predictor called RFAmy based on random forest to identify amyloid, and it employed SVMProt 188-D feature extraction method based on protein composition and physicochemical properties and pse-in-one feature extraction method based on amino acid composition, autocorrelation pseudo acid composition, profile-based features and predicted structures features. In the ten-fold cross-validation test, RFAmy's overall accuracy was 89.19% and F-measure was 0.891. Results were obtained by comparison experiments with other feature, classifiers, and existing methods. This shows the effectiveness of RFAmy in predicting amyloid protein. The RFAmy proposed in this paper can be accessed through the URL http://server.malab.cn/RFAmyloid/.

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Citations

Mar 12, 2020·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Zaida L Almeida, Rui M M Brito
Jul 14, 2020·Frontiers in Bioengineering and Biotechnology·Konda Mani SaravananYanjie Wei
Apr 21, 2020·Computational and Structural Biotechnology Journal·Mengting NiuQin Ma
Dec 6, 2020·Journal of Pharmaceutical Sciences·Tim J Kamerzell, C Russell Middaugh
Dec 10, 2020·Computational and Mathematical Methods in Medicine·Yanjuan LiXiaoyan Liu
Jun 1, 2021·FEBS Open Bio·Marcos Gil-GarciaSalvador Ventura
Jan 31, 2019·Journal of Proteome Research·Yanjuan LiQuan Zou
Jul 4, 2021·Scientific Reports·Puneet RawatM Michael Gromiha

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Methods Mentioned

BETA
feature extraction
protein folding

Software Mentioned

RFAmyloid
LibSVM
Pse
in
FoldAmyloid
LibD3C
SVMProt
HIT
AdaBoost
Pafig

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