AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

• Published in: Scientific reports Vol. 12; no. 1; p. 7697
• Main Authors: Charoenkwan, Phasit, Ahmed, Saeed, Nantasenamat, Chanin, Quinn, Julian M. W., Moni, Mohammad Ali, Lio’, Pietro, Shoombuatong, Watshara
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.05.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/2397; Aggregates; Algorithms; Alzheimer's disease; Amyloidogenic Proteins; Bioinformatics; Computational Biology - methods; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2; Humanities and Social Sciences; Humans; Machine Learning; Movement disorders; multidisciplinary; Neurodegenerative diseases; Parkinson's disease; Proteins; Science; Science (multidisciplinary); Support Vector Machine
• DOI: 10.1038/s41598-022-11897-z
• ISSN: 2045-2322

Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL . It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.