findMySequence : a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM
© Grzegorz Chojnowski et al. 2022..
Although experimental protein-structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method's application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
IUCrJ - 9(2022), Pt 1 vom: 01. Jan., Seite 86-97 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chojnowski, Grzegorz [VerfasserIn] |
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| Links: |
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| Themen: |
Bioinformatics |
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| Anmerkungen: |
Date Revised 09.04.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1107/S2052252521011088 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM335938418 |
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| 100 | 1 | |a Chojnowski, Grzegorz |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a findMySequence |b a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM |
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| 500 | |a Date Revised 09.04.2022 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © Grzegorz Chojnowski et al. 2022. | ||
| 520 | |a Although experimental protein-structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method's application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a SIMBAD | |
| 650 | 4 | |a bioinformatics | |
| 650 | 4 | |a cryo-EM | |
| 650 | 4 | |a findMySequence | |
| 650 | 4 | |a neural networks | |
| 650 | 4 | |a protein sequences | |
| 650 | 4 | |a protein structures | |
| 650 | 4 | |a structure determination | |
| 700 | 1 | |a Simpkin, Adam J |e verfasserin |4 aut | |
| 700 | 1 | |a Leonardo, Diego A |e verfasserin |4 aut | |
| 700 | 1 | |a Seifert-Davila, Wolfram |e verfasserin |4 aut | |
| 700 | 1 | |a Vivas-Ruiz, Dan E |e verfasserin |4 aut | |
| 700 | 1 | |a Keegan, Ronan M |e verfasserin |4 aut | |
| 700 | 1 | |a Rigden, Daniel J |e verfasserin |4 aut | |
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