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Prediction of proprotein convertase cleavage sites
Duckert P, Brunak S, Blom N.. Protein engineering, design & selection : PEDS, 2004-01-01
Topic: Disease mechanisms in lab and animal studies
Many secretory proteins and peptides are synthesized as inactive precursors that in addition to signal peptide cleavage undergo post-translational processing to become biologically active polypeptides. Precursors are usually cleaved at sites composed of single or paired basic amino acid residues by members of the subtilisin/kexin-like proprotein convertase (PC) family. In mammals, seven members have been identified, with furin being the one first discovered and best characterized. Recently, the involvement of furin in diseases ranging from Alzheimer's disease and cancer to anthrax and Ebola fever has created additional focus on proprotein processing. We have developed a method for prediction of cleavage sites for PCs based on artificial neural networks. Two different types of neural networks have been constructed: a furin-specific network based on experimental results derived from the literature, and a general PC-specific network trained on data from the Swiss-Prot protein database. The method predicts cleavage sites in independent sequences with a sensitivity of 95% for the furin neural network and 62% for the general PC network. The ProP method is made publicly available at http://www.cbs.dtu.dk/services/ProP.
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APA 7th
Duckert, P., Brunak, S., & Blom, N. (2004). Prediction of proprotein convertase cleavage sites. Protein engineering, design & selection : PEDS. https://doi.org/10.1093/protein/gzh013
Vancouver
Duckert P, Brunak S, Blom N. Prediction of proprotein convertase cleavage sites. Protein engineering, design & selection : PEDS. 2004. doi:10.1093/protein/gzh013. PMID: 14985543.
BibTeX
@article{duckert2004-14985543,
author = {Duckert, P. and Brunak, S. and Blom, N.},
title = {{Prediction of proprotein convertase cleavage sites}},
journal = {Protein engineering, design \& selection : PEDS},
year = {2004},
doi = {10.1093/protein/gzh013},
pmid = {14985543},
url = {https://doi.org/10.1093/protein/gzh013}
}
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Key findings
- Two different types of neural networks have been constructed: a furin-specific network based on experimental results derived from the literature, and a general PC-specific network trained on data from the Swiss-Prot protein database.
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Study details
- Evidence strengthLow
How this study scored 36/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 21 of the 58 points we could judge it on, which is 36 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Protein engineering, design & selection : PEDS) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2004 — older than 20 years, still counted |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 14985543) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 21 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 21 of the 58 points we could judge it on is 36 out of 100. |
| Total | 36 / 100 | — |
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