ArticleBioinformatics (Oxford, England)2024
DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning.
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 14 papers.
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14 citing papers in PubMed.
- RINAMI: Residue-attributed interpretable neural network for predicting absolute folding free energy by merging structure and sequence information.Protein science : a publication of the Protein Society · 2026Article
- Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.Protein science : a publication of the Protein Society · 2026Article
- LoMuS: low-rank adaptation with sequence multi-representation improves protein stability prediction.Bioinformatics (Oxford, England) · 2026Article
- dUTPase is essential in zebrafish development and possesses several single-nucleotide variants with pronounced structural and functional consequences.FEBS open bio · 2026Article
- MuFaDDG: a sequence-based multiscale feature fusion framework for protein stability changes prediction.Bioinformatics (Oxford, England) · 2026Article
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Deciphering gain-of-function from loss-of-function variants with AlphaMissense: A case study with the mechanosensitive PIEZO1 ion channel protein.Biochemistry and biophysics reports · 2026Article
- JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention.Communications biology · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.Briefings in bioinformatics · 2026Article
- pLM-SAV: a Δ-embedding approach for predicting pathogenic single amino acid variants.Bioinformatics advances · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.bioRxiv : the preprint server for biology · 2025Article
- Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models.Briefings in bioinformatics · 2025Article
- CDKL5 Deficiency Disorder: Revealing the Molecular Mechanism of Pathogenic Variants.International journal of molecular sciences · 2025Article
- The structural view of the protein PGD-219aa encoded by the circular RNA CircPGD.Journal of molecular modeling · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
motivationThe knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimental approaches and allow a fast screening of large datasets of variations.
resultsIn this work, we present DDGemb, a novel method combining protein language model embeddings and transformer architectures to predict protein ΔΔG upon both single- and multi-point variations. DDGemb has been trained on a high-quality dataset derived from literature and tested on available benchmark datasets of single- and multi-point variations. DDGemb performs at the state of the art in both single- and multi-point variations. AVAILABILITY AND IMPLEMENTATION: DDGemb is available as web server at https://ddgemb.biocomp.unibo.it. Datasets used in this study are available at https://ddgemb.biocomp.unibo.it/datasets.
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