PEP-FOLD3: fasterde novostructure prediction for linear peptides in solution and in complex

Clicks: 21
ID: 300902
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #911 of 1,306 articles by views in Nucleic Acids Research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,306 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Structure determination of linear peptides of 5–50 amino acids in aqueous solution and interacting with proteins is a key aspect in structural biology. PEP-FOLD3 is a novel computational framework, that allows both (i) de novo free or biased prediction for linear peptides between 5 and 50 amino acids, and (ii) the generation of native-like conformations of peptides interacting with a protein when the interaction site is known in advance. PEP-FOLD3 is fast, and usually returns solutions in a few minutes. Testing PEP-FOLD3 on 56 peptides in aqueous solution led to experimental-like conformations for 80% of the targets. Using a benchmark of 61 peptide–protein targets starting from the unbound form of the protein receptor, PEP-FOLD3 was able to generate peptide poses deviating on average by 3.3Å from the experimental conformation and return a native-like pose in the first 10 clusters for 52% of the targets. PEP-FOLD3 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3.
Reference Key
openalex_W2344689512 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal Nucleic Acids Research
Year 2016
DOI
10.1093/nar/gkw329
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.