ArticleBioinformatics advances2024
TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms.
Article in Bioinformatics advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Article
- Dynamics-informed multigraph neural networks for protein thermostability prediction and residue-level interpretation.iScience · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Lignocellulose-mediated selection of potential halophilic PET-degrading enzymes from mangrove soil.Nature communications · 2026Article
- Protein Language Model-Guided Engineering of a 2,3-Butanediol Dehydrogenase for the Enantioselective Synthesis of Cyclic α-Hydroxy Ketones.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- NbBayesLM: bayesian prediction of nanobody thermostability using protein language model.Frontiers in bioinformatics · 2026Article
- Supervised Learning of Protein Melting Temperature: Cross-Species vs. Species-Specific Prediction.Proteins · 2025Article
- Machine learning-guided discovery of thermophilic carbonic anhydrases from environmental metagenomes.Scientific reports · 2025Article
- Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity.ACS catalysis · 2025Article
- Enzyme-Embedded Biodegradable Plastic for Sustainable Applications: Advances, Challenges, and Perspectives.ACS applied bio materials · 2025Review
- ESMStabP: A Regression Model for Protein Thermostability Prediction.bioRxiv : the preprint server for biology · 2025Article
- BacDive in 2025: the core database for prokaryotic strain data.Nucleic acids research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Understanding protein thermostability is essential for numerous biotechnological applications, but traditional experimental methods are time-consuming, expensive, and error-prone. Recently, deep learning (DL) techniques from natural language processing (NLP) was extended to the field of biology, since the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar. Results: In this study, we developed TemBERTure, a DL framework that predicts thermostability class and melting temperature from protein sequences. Our findings emphasize the importance of data diversity for training robust models, especially by including sequences from a wider range of organisms. Additionally, we suggest using attention scores from Deep Learning models to gain deeper insights into protein thermostability. Analyzing these scores in conjunction with the 3D protein structure can enhance understanding of the complex interactions among amino acid properties, their positioning, and the surrounding microenvironment. By addressing the limitations of current prediction methods and introducing new exploration avenues, this research paves the way for more accurate and informative protein thermostability predictions, ultimately accelerating advancements in protein engineering. Availability and implementation: TemBERTure model and the data are available at: https://github.com/ibmm-unibe-ch/TemBERTure.
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.