The Budapest Amyloid Predictor and Its Applications
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 <inline-formula><math display="inline&quo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/11/4/500 |
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| author | László Keresztes Evelin Szögi Bálint Varga Viktor Farkas András Perczel Vince Grolmusz |
| author_facet | László Keresztes Evelin Szögi Bálint Varga Viktor Farkas András Perczel Vince Grolmusz |
| author_sort | László Keresztes |
| collection | DOAJ |
| description | 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 <inline-formula><math display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-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. |
| first_indexed | 2024-03-10T12:53:14Z |
| format | Article |
| id | doaj.art-66aadf55656d49b5a032c87531689682 |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-10T12:53:14Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-66aadf55656d49b5a032c875316896822023-11-21T12:08:21ZengMDPI AGBiomolecules2218-273X2021-03-0111450010.3390/biom11040500The Budapest Amyloid Predictor and Its ApplicationsLászló Keresztes0Evelin Szögi1Bálint Varga2Viktor Farkas3András Perczel4Vince Grolmusz5PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, HungaryPIT Bioinformatics Group, Eötvös University, H-1117 Budapest, HungaryPIT Bioinformatics Group, Eötvös University, H-1117 Budapest, HungaryMTA-ELTE Protein Modeling Research Group, H-1117 Budapest, HungaryMTA-ELTE Protein Modeling Research Group, H-1117 Budapest, HungaryPIT Bioinformatics Group, Eötvös University, H-1117 Budapest, HungaryThe 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 <inline-formula><math display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-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.https://www.mdpi.com/2218-273X/11/4/500amyloidsupport vector machinessite-specific amyloidogenecityBudapest Amyloid Predictor |
| spellingShingle | László Keresztes Evelin Szögi Bálint Varga Viktor Farkas András Perczel Vince Grolmusz The Budapest Amyloid Predictor and Its Applications Biomolecules amyloid support vector machines site-specific amyloidogenecity Budapest Amyloid Predictor |
| title | The Budapest Amyloid Predictor and Its Applications |
| title_full | The Budapest Amyloid Predictor and Its Applications |
| title_fullStr | The Budapest Amyloid Predictor and Its Applications |
| title_full_unstemmed | The Budapest Amyloid Predictor and Its Applications |
| title_short | The Budapest Amyloid Predictor and Its Applications |
| title_sort | budapest amyloid predictor and its applications |
| topic | amyloid support vector machines site-specific amyloidogenecity Budapest Amyloid Predictor |
| url | https://www.mdpi.com/2218-273X/11/4/500 |
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