ArticleNucleic acids research2021
FireProtDB: database of manually curated protein stability data.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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
65 citing papers in PubMed.
- Aligning protein-generative models to experimental fitness with ProteinDPO.Nature methods · 2026Article
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 2026Article
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Molecular Adaptations of Thermophilic Proteins-From Mechanistic Understanding to Machine Learning Approaches.ChemistryOpen · 2026Review
- Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations.bioRxiv : the preprint server for biology · 2026Article
- MuFaDDG: a sequence-based multiscale feature fusion framework for protein stability changes prediction.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.bioRxiv : the preprint server for biology · 2026Article
- HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.bioRxiv : the preprint server for biology · 2026Article
- FireProtDB 2.0: large-scale manually curated database of the protein stability data.Nucleic acids research · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- Interpretable Thermodynamic Score-based Classification of Relaxation Excursions.bioRxiv : the preprint server for biology · 2025Article
- Enriching stabilizing mutations through automated analysis of molecular dynamics simulations using BoostMut.Protein science : a publication of the Protein Society · 2025Article
- Machine learning applied to biocatalysis research.Nature communications · 2025Article
- CDKL5 Deficiency Disorder: Revealing the Molecular Mechanism of Pathogenic Variants.International journal of molecular sciences · 2025Article
- Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence.Briefings in bioinformatics · 2025Article
- Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN.Proceedings of machine learning research · 2025Article
- Reliable prediction of protein-protein binding affinity changes upon mutations with Pythia-PPI.National science review · 2025Article
- VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data.Acta pharmaceutica Sinica. B · 2025Article
- Neural network conditioned to produce thermophilic protein sequences can increase thermal stability.Scientific reports · 2025Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevated temperatures or in the presence of salts. Since experimental screening for stabilizing mutations is typically laborious and expensive, in silico predictors are often used for narrowing down the mutational landscape. The recent advances in machine learning and artificial intelligence further facilitate the development of such computational tools. However, the accuracy of these predictors strongly depends on the quality and amount of data used for training and testing, which have often been reported as the current bottleneck of the approach. To address this problem, we present a novel database of experimental thermostability data for single-point mutants FireProtDB. The database combines the published datasets, data extracted manually from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries on the level of a protein or mutation and (ii) the construction of highly customized and machine learning-friendly datasets using advanced searching and filtering. The database is freely available at https://loschmidt.chemi.muni.cz/fireprotdb.
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.