ArticleBiomolecules2021
The Budapest Amyloid Predictor and Its Applications.
Article in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Assessment of Aggregation- and Condensation-Prone Regions of Proteins Involved in Neurodegenerative, Neurological and Mental-State Diseases.Biomolecules · 2026Article
- Experimentally validated deep learning control of protein aggregation.Communications chemistry · 2026Article
- Global Analysis of Aggregation Determinants in Small Protein Domains.bioRxiv : the preprint server for biology · 2025Article
- Navigating Homogeneous Graph Paths Through Amyloidogenic and Non-Amyloidogenic Hexapeptides.Journal of computational chemistry · 2025Article
- Unravelling the Complexity of Amyloid Peptide Core Interfaces.Journal of chemical information and modeling · 2024Article
- Proteomic Evidence for Amyloidogenic Cross-Seeding in Fibrinaloid Microclots.International journal of molecular sciences · 2024Article
- Enhancing protein aggregation prediction: a unified analysis leveraging graph convolutional networks and active learning.RSC advances · 2024Article
- AggreProt: a web server for predicting and engineering aggregation prone regions in proteins.Nucleic acids research · 2024Article
- Stability of Protein Pharmaceuticals: Recent Advances.Pharmaceutical research · 2024Review
- Advanced computational approaches to understand protein aggregation.Biophysics reviews · 2024Review
- Article
- Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis.International journal of molecular sciences · 2023Review
- ENTAIL: yEt aNoTher amyloid fIbrils cLassifier.BMC bioinformatics · 2022Article
- A spatiotemporal reconstruction of theeLife · 2022Article
- Succinct Amyloid and Nonamyloid Patterns in Hexapeptides.ACS omega · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-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, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.