The Budapest Amyloid Predictor and its Applications

TL;DR

This paper introduces the Budapest Amyloid Predictor, a support vector machine-based tool for predicting amyloid-forming hexapeptides, achieving accuracy comparable to neural network models while offering better interpretability for biochemical insights.

Contribution

The paper presents a novel SVM-based amyloid predictor that matches neural network accuracy and enhances interpretability of the decision process.

Findings

Achieves over 84% accuracy in predicting amyloid hexapeptides.

SVM-based predictor is as effective as neural network models.

Provides a more interpretable model for biochemical analysis.

Abstract

The amyloid state of proteins is widely studied with relevancy in neurology, biochemistry, and biotechnology. In contrast with amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and anti-parallel $\beta$-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed by using mostly artificial neural networks (ANNs) as the underlying computational techniques. From a good neural network-based predictor, it is a very difficult task to identify those attributes of the input amino acid sequence, which implied the decision of the network. Here we present a Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84\%, i.e., it is at least as good as the published ANN-based tools. Unlike the artificial neural networks, the decision of the SVMs are much easier to analyze, and from a good predictor, we can infer rich biochemical knowledge. Availability and Implementation: The Budapest Amyloid Predictor webserver is freely available at https://pitgroup.org/bap.