ArticleGenes2021
A Deep-Learning Sequence-Based Method to Predict Protein Stability Changes Upon Genetic Variations.
Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Epistasis among clustered lineage-specific amino acid substitutions in the Drosophila Trio protein.PLoS genetics · 2026Article
- JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention.Communications biology · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- PPAC: Predicting Protein-Protein Affinity Changes Induced by Amino Acid Mutations Using Protein Large Language Models.ACS omega · 2025Article
- Epistasis among clustered lineage-specific adaptive amino acid substitutions in the Drosophila Trio protein.bioRxiv : the preprint server for biology · 2025Article
- Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence.Briefings in bioinformatics · 2025Article
- Pervasive Divergence in Protein Thermostability is Mediated by Both Structural Changes and Cellular Environments.Molecular biology and evolution · 2025Article
- Mass balance approximation of unfolding boosts potential-based protein stability predictions.Protein science : a publication of the Protein Society · 2025Article
- Assessing the predicted impact of single amino acid substitutions in MAPK proteins for CAGI6 challenges.Human genetics · 2025Article
- ProstaNet: A Novel Geometric Vector Perceptrons-Graph Neural Network Algorithm for Protein Stability Prediction in Single- and Multiple-Point Mutations with Experimental Validation.Research (Washington, D.C.) · 2025Article
- DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning.Bioinformatics (Oxford, England) · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Enhancing predictions of protein stability changes induced by single mutations using MSA-based language models.Bioinformatics (Oxford, England) · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.Bioinformatics (Oxford, England) · 2024Article
- Transfer learning to leverage larger datasets for improved prediction of protein stability changes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.Journal of chemical information and modeling · 2024Article
- Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset.Protein science : a publication of the Protein Society · 2024Article
- Editorial: Computational and experimental protein variant interpretation in the era of precision medicine.Frontiers in molecular biosciences · 2024Article
- PROSTATA: a framework for protein stability assessment using transformers.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several studies have linked disruptions of protein stability and its normal functions to disease. Therefore, during the last few decades, many tools have been developed to predict the free energy changes upon protein residue variations. Most of these methods require both sequence and structure information to obtain reliable predictions. However, the lower number of protein structures available with respect to their sequences, due to experimental issues, drastically limits the application of these tools. In addition, current methodologies ignore the antisymmetric property characterizing the thermodynamics of the protein stability: a variation from wild-type to a mutated form of the protein structure (XW→XM) and its reverse process (XM→XW) must have opposite values of the free energy difference (ΔΔGWM=-ΔΔGMW). Here we propose ACDC-NN-Seq, a deep neural network system that exploits the sequence information and is able to incorporate into its architecture the antisymmetry property. To our knowledge, this is the first convolutional neural network to predict protein stability changes relying solely on the protein sequence. We show that ACDC-NN-Seq compares favorably with the existing sequence-based methods.
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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.