ArticleBioinformatics (Oxford, England)2023
THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- MuFaDDG: a sequence-based multiscale feature fusion framework for protein stability changes prediction.Bioinformatics (Oxford, England) · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Structure-informed machine learning for drug discovery: a task-centric perspective.Briefings in bioinformatics · 2026Review
- FireProtDB 2.0: large-scale manually curated database of the protein stability data.Nucleic acids research · 2026Article
- pLM-SAV: a Δ-embedding approach for predicting pathogenic single amino acid variants.Bioinformatics advances · 2026Article
- Protein language model identifies disordered, conserved motifs implicated in phase separation.eLife · 2025Article
- Advancing the Accuracy of Anti-MRSA Peptide Prediction Through Integrating Multi-Source Protein Language Models.Interdisciplinary sciences, computational life sciences · 2025Article
- Deep-learning structure elucidation from single-mutant deep mutational scanning.Nature communications · 2025Article
- OrgNet: orientation-gnostic protein stability assessment using convolutional neural networks.Bioinformatics (Oxford, England) · 2025Article
- Review
- PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model.iScience · 2024Article
- The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning.Antibodies (Basel, Switzerland) · 2024Article
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11 authors.
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Abstract
motivationQuantitative determination of protein thermodynamic stability is a critical step in protein and drug design. Reliable prediction of protein stability changes caused by point variations contributes to developing-related fields. Over the past decades, dozens of structure-based and sequence-based methods have been proposed, showing good prediction performance. Despite the impressive progress, it is necessary to explore wild-type and variant protein representations to address the problem of how to represent the protein stability change in view of global sequence. With the development of structure prediction using learning-based methods, protein language models (PLMs) have shown accurate and high-quality predictions of protein structure. Because PLM captures the atomic-level structural information, it can help to understand how single-point variations cause functional changes.
resultsHere, we proposed THPLM, a sequence-based deep learning model for stability change prediction using Meta's ESM-2. With ESM-2 and a simple convolutional neural network, THPLM achieved comparable or even better performance than most methods, including sequence-based and structure-based methods. Furthermore, the experimental results indicate that the PLM's ability to generate representations of sequence can effectively improve the ability of protein function prediction. AVAILABILITY AND IMPLEMENTATION: The source code of THPLM and the testing data can be accessible through the following links: https://github.com/FPPGroup/THPLM.
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