ArticlePloS one2018
Role of simple descriptors and applicability domain in predicting change in protein thermostability.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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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.
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Who cites it
8 citing papers in PubMed.
- Enzyme miniaturization: Revolutionizing future biocatalysts.Biotechnology advances · 2025Review
- Investigating the Interaction between Excipients and Monoclonal Antibodies PGT121 and N49P9.6-FR-LS: A Comprehensive Analysis.Molecular pharmaceutics · 2025Article
- Predicting Mutational Effects on CaJournal of the American Chemical Society · 2024Article
- Accurate Prediction of Enzyme Thermostabilization with Rosetta Using AlphaFold Ensembles.Journal of chemical information and modeling · 2023Article
- The role of data imbalance bias in the prediction of protein stability change upon mutation.PloS one · 2023Article
- Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures.Molecular biotechnology · 2021Review
- A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.Briefings in bioinformatics · 2020Review
- Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset.ACS omega · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The melting temperature (Tm) of a protein is the temperature at which half of the protein population is in a folded state. Therefore, Tm is a measure of the thermostability of a protein. Increasing the Tm of a protein is a critical goal in biotechnology and biomedicine. However, predicting the change in melting temperature (dTm) due to mutations at a single residue is difficult because it depends on an intricate balance of forces. Existing methods for predicting dTm have had similar levels of success using generally complex models. We find that training a machine learning model with a simple set of easy to calculate physicochemical descriptors describing the local environment of the mutation performed as well as more complicated machine learning models and is 2-6 orders of magnitude faster. Importantly, unlike in most previous publications, we perform a blind prospective test on our simple model by designing 96 variants of a protein not in the training set. Results from retrospective and prospective predictions reveal the limited applicability domain of each model. This study highlights the current deficiencies in the available dTm dataset and is a call to the community to systematically design a larger and more diverse experimental dataset of mutants to prospectively predict dTm with greater certainty.
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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.