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Mutagenesis

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A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans.

PLoS genetics
Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rat...

Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase.

International journal of biological macromolecules
Enhancing the thermostability of enzymes is crucial for industrial applications. Methods such as directed evolution are often limited by the huge sequence space and combinatorial explosion, making it difficult to obtain optimal mutants. In recent yea...

DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application.

Regulatory toxicology and pharmacology : RTP
The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the In...

Mechanistic Task Groupings Enhance Multitask Deep Learning of Strain-Specific Ames Mutagenicity.

Chemical research in toxicology
The Ames test is a gold standard mutagenicity assay that utilizes various strains with and without S9 fraction to provide insights into the mechanisms by which a chemical can mutate DNA. Multitask deep learning is an ideal framework for developing Q...

Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures.

Scientific reports
Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less...

Rapid protein stability prediction using deep learning representations.

eLife
Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate pred...

Genotoxicity evaluation of titanium dioxide nanoparticles using the mouse lymphoma assay and the Ames test.

Mutation research. Genetic toxicology and environmental mutagenesis
Titanium dioxide nanoparticles (TiO-NPs) are widely used in the cosmetics, health, and food industries, but their safety and genotoxicity remain a matter of debate. We investigated whether TiO-NPs could induce gene mutations in mouse lymphoma L5178Y ...

Identification of Clonal Hematopoiesis Driver Mutations through In Silico Saturation Mutagenesis.

Cancer discovery
Clonal hematopoiesis (CH) is a phenomenon of clonal expansion of hematopoietic stem cells driven by somatic mutations affecting certain genes. Recently, CH has been linked to the development of hematologic malignancies, cardiovascular diseases, and o...

SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models.

Bioinformatics (Oxford, England)
SUMMARY: The increasing development of sequence-based machine learning models has raised the demand for manipulating sequences for this application. However, existing approaches to edit and evaluate genome sequences using models have limitations, suc...

Multitask Deep Neural Networks for Ames Mutagenicity Prediction.

Journal of chemical information and modeling
The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of , the vast majority of the published in silico models ...