ArticleData in brief2023
Manually curated dataset of catalytic peptides for ester hydrolysis.
Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Catalytic peptides are low cost biomolecules able to catalyse chemical reactions such as ester hydrolysis. This dataset provides a list of catalytic peptides currently reported in literature. Several parameters were evaluated, including sequence length, composition, net charge, isoelectric point, hydrophobicity, self-assembly propensity and mechanism of catalysis. Along with the analysis of physico-chemical properties, the SMILES representation for each sequence was generated to provide an easy-to-use means of training machine learning models. This offers a unique opportunity for the development and validation of proof-of-concept predictive models. Being a reliable manually curated dataset, it also enables the benchmark for comparison of new models or models trained on automatically gathered peptide-oriented datasets. Moreover, the dataset provides an insight in the currently developed catalytic mechanisms and can be used as the foundation for the development of next-generation peptide-based catalysts.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.