AIMC Topic: Mutation, Missense

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Deep learning-based assessment of missense variants in the gene presented with bilateral congenital cataract.

BMJ open ophthalmology Jan 14, 2025
OBJECTIVE: We compared the protein structure and pathogenicity of clinically relevant variants of the gene with AlphaFold2 (AF2), Alpha Missense (AM), and ThermoMPNN for the first time.
Humans Phenotype Deep Learning Cataract Mutation, Missense Female Male
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Toward trustable use of machine learning models of variant effects in the clinic.

American journal of human genetics Nov 18, 2024
There has been considerable progress in building models to predict the effect of missense substitutions in protein-coding genes, fueled in large part by progress in applying deep learning methods to sequence data. These models have the potential to e...
Humans Mutation, Missense Computer Simulation Models, Genetic Deep Learning Genetic Variation Machine Learning
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Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning.

International journal of molecular sciences Oct 30, 2024
Understanding the effect of single-missense mutations on protein stability is crucial for clinical decision-making and therapeutic development. The impact of these mutations on protein stability and 3D structure remains underexplored. Here, we develo...
Humans Machine Learning Eye Diseases Mutation, Missense Clinical Relevance Eye Proteins Protein Stability cis-trans-Isomerases
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Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.

Nature communications Sep 27, 2024
Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To ad...
Intrinsically Disordered Proteins Mutation, Missense Phase Separation Machine Learning Computational Biology Humans
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AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.

Human genomics Sep 11, 2024
Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. L...
Breast Neoplasms Genetic Predisposition to Disease Polymorphism, Single Nucleotide Software Female Mutation, Missense Computational Biology Databases, Genetic Humans Artificial Intelligence
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Enhancing Missense Variant Pathogenicity Prediction with MissenseNet: Integrating Structural Insights and ShuffleNet-Based Deep Learning Techniques.

Biomolecules Sep 2, 2024
The classification of missense variant pathogenicity continues to pose significant challenges in human genetics, necessitating precise predictions of functional impacts for effective disease diagnosis and personalized treatment strategies. Traditiona...
Mutation, Missense Humans Computational Biology ROC Curve Proteins Deep Learning
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Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.

Human genomics Aug 27, 2024
We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence align...
Humans Computational Biology Proteins Mutation, Missense Machine Learning Software
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PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network.

Structure (London, England : 1993) Mar 19, 2024
Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we...
Mutation, Missense Point Mutation Humans DNA Glycosylases Neural Networks, Computer Myoglobin Protein Stability Tumor Suppressor Protein p53 Databases, Protein Models, Molecular
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Prediction of protein structure and AI.

Journal of human genetics Jan 4, 2024
AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-functi...
Computational Biology Humans Proteins Models, Molecular Protein Folding Protein Conformation Artificial Intelligence Mutation, Missense Deep Learning
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Predicting pathogenic protein variants.

Science (New York, N.Y.) Sep 19, 2023
Machine-learning algorithm uses structure prediction to spot disease-causing mutations.
Protein Conformation Sequence Analysis, DNA Disease Machine Learning Genetic Variation Mutant Proteins Mutation, Missense Algorithms
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