ArticleBioinformatics (Oxford, England)2024
TemStaPro: protein thermostability prediction using sequence representations from protein language models.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 45 citations in OpenAlex.
- Article
- Molecular Adaptations of Thermophilic Proteins-From Mechanistic Understanding to Machine Learning Approaches.ChemistryOpen · 2026Review
- Heavy-chain immune repertoire sequencing enables language-model prediction of antigen-specific antibodies.Research square · 2026Article
- In Silico Design and Computational Characterization of Novel Chimeric Multiepitope Antigens for Mpox Serosurveillance: An Immunoinformatics Approach.Health science reports · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Prediction of plant phase-separating proteins using positive-unlabeled learning.Genome biology · 2026Article
- AI-enabled protein design facilitates future plant research and crop breeding.Plant physiology · 2026Review
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.Briefings in bioinformatics · 2026Article
- Supervised Learning of Protein Melting Temperature: Cross-Species vs. Species-Specific Prediction.Proteins · 2025Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.bioRxiv : the preprint server for biology · 2025Article
- Machine learning-guided discovery of thermophilic carbonic anhydrases from environmental metagenomes.Scientific reports · 2025Article
- aMLProt: an automated machine learning library for protein applications.Bioinformatics (Oxford, England) · 2025Article
- Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE.Bioinformatics (Oxford, England) · 2025Article
- One-Pot Isothermal Nucleic Acid Amplification Assisted CRISPR/Cas Detection Technology: Challenges, Strategies, and Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Unexplored regions of the protein sequence-structure map revealed at scale by a library of foldtuned language models.bioRxiv : the preprint server for biology · 2025Article
- Advancing the Accuracy of Anti-MRSA Peptide Prediction Through Integrating Multi-Source Protein Language Models.Interdisciplinary sciences, computational life sciences · 2025Article
- Kinetic analysis and engineering of thermostable Cas12a for nucleic acid detection.Nucleic acids research · 2025Article
- Prediction and design of thermostable proteins with a desired melting temperature.Scientific reports · 2025Article
- Enzyme-Embedded Biodegradable Plastic for Sustainable Applications: Advances, Challenges, and Perspectives.ACS applied bio materials · 2025Review
- Enhancing recombinant growth factor and serum protein production for cultivated meat manufacturing.Microbial cell factories · 2025Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
motivationReliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep learning methods for sequence analysis. These methods can facilitate training on more data and, possibly, enable the development of more versatile thermostability predictors for multiple ranges of temperatures.
resultsWe applied the principle of transfer learning to predict protein thermostability using embeddings generated by protein language models (pLMs) from an input protein sequence. We used large pLMs that were pre-trained on hundreds of millions of known sequences. The embeddings from such models allowed us to efficiently train and validate a high-performing prediction method using over one million sequences that we collected from organisms with annotated growth temperatures. Our method, TemStaPro (Temperatures of Stability for Proteins), was used to predict thermostability of CRISPR-Cas Class II effector proteins (C2EPs). Predictions indicated sharp differences among groups of C2EPs in terms of thermostability and were largely in tune with previously published and our newly obtained experimental data. AVAILABILITY AND IMPLEMENTATION: TemStaPro software and the related data are freely available from https://github.com/ievapudz/TemStaPro and https://doi.org/10.5281/zenodo.7743637.
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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.