| Autor: |
Akos Godo, Kota Aoki, Atsushi Nakagawa, Yasushi Yagi |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 10, Pp 108354-108365 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3213670 |
| Popis: |
Despite constant advances in X-ray crystallography, the resolution of the acquired electron density maps still poses a serious limit on protein protein structural model building efforts. Furthermore, the currently available toolkits require hours or even days for model building. Methods capable of processing a large volume of samples in a short time which can also handle low resolution samples are needed. This work proposes a neural network-based approach to locate and classify residues in crystallographic electron density maps automatically in a single forward pass without relying on the protein’s residue sequence. Our proposed method shows an average 23.53% increase in accuracy over our previous approach and also compares favorably to currently available toolkits. It can process protein samples in seconds on consumer-grade hardware saving significant time and resources. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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