ArticlePLoS computational biology2020
Assessing predictors for new post translational modification sites: A case study on hydroxylation.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods.Proteomes · 2025Review
- Machine Learning Methods for Gene Selection in Uveal Melanoma.International journal of molecular sciences · 2024Article
- Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.International journal of molecular sciences · 2022Article
- Current status of PTMs structural databases: applications, limitations and prospects.Amino acids · 2022Review
- Development of an experiment-split method for benchmarking the generalization of a PTM site predictor: Lysine methylome as an example.PLoS computational biology · 2021Article
- DOME: recommendations for supervised machine learning validation in biology.Nature methods · 2021Article
- A global map of associations between types of protein posttranslational modifications and human genetic diseases.iScience · 2021Article
- Comparing a Novel Malformation Syndrome Caused by Pathogenic Variants inFrontiers in cell and developmental biology · 2021Review
- Review
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Post-translational modification (PTM) sites have become popular for predictor development. However, with the exception of phosphorylation and a handful of other examples, PTMs suffer from a limited number of available training examples and sparsity in protein sequences. Here, proline hydroxylation is taken as an example to compare different methods and evaluate their performance on new experimentally determined sites. As a guide for effective experimental design, predictors require both high specificity and sensitivity. However, the self-reported performance may often not be indicative of prediction quality and detection of new sites is not guaranteed. We have benchmarked seven published hydroxylation site predictors on two newly constructed independent datasets. The self-reported performance is found to widely overestimate the real accuracy measured on independent datasets. No predictor performs better than random on new examples, indicating the refined models do not sufficiently generalize to detect new sites. The number of false positives is high and precision low, in particular for non-collagen proteins whose motifs are not conserved. As hydroxylation site predictors do not generalize for new data, caution is advised when using PTM predictors in the absence of independent evaluations, in particular for highly specific sites involved in signalling.
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