AIMC Topic: Mutagenesis

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Biological features between miRNAs and their targets are unveiled from deep learning models.

Scientific reports Dec 10, 2021
MicroRNAs (miRNAs) are ~ 22 nucleotide ubiquitous gene regulators. They modulate a broad range of essential cellular processes linked to human health and diseases. Consequently, identifying miRNA targets and understanding how they function are critic...
Algorithms Deep Learning Gene Regulatory Networks Computational Biology Humans Mutagenesis Base Pairing Databases, Nucleic Acid RNA Interference RNA, Messenger MicroRNAs Models, Biological Gene Expression Regulation
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Machine learning - Predicting Ames mutagenicity of small molecules.

Journal of molecular graphics & modelling Sep 5, 2021
In modern drug discovery, detection of a compound's potential mutagenicity is crucial. However, the traditional method of mutagenicity detection using the Ames test is costly and time consuming as the compounds need to be synthesised and then tested ...
Mutagens Mutagenesis Machine Learning Computer Simulation Support Vector Machine
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Informed training set design enables efficient machine learning-assisted directed protein evolution.

Cell systems Aug 19, 2021
Directed evolution of proteins often involves a greedy optimization in which the mutation in the highest-fitness variant identified in each round of single-site mutagenesis is fixed. The efficiency of such a single-step greedy walk depends on the ord...
Mutagenesis Machine Learning Proteins Mutation
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In silico saturation mutagenesis of cancer genes.

Nature Jul 28, 2021
Despite the existence of good catalogues of cancer genes, identifying the specific mutations of those genes that drive tumorigenesis across tumour types is still a largely unsolved problem. As a result, most mutations identified in cancer genes acros...
Computer Simulation Precision Medicine Reproducibility of Results Neoplasms Humans Machine Learning Probability Models, Genetic Mutagenesis Mutation Organ Specificity Cell Transformation, Neoplastic Oncogenes
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QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.

Molecular diversity Jun 19, 2021
Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models can be constructed from 2D molecular graphs without computing chemical descriptors. Two graph...
Mutagenesis Neural Networks, Computer Bayes Theorem Algorithms Mutagens Quantitative Structure-Activity Relationship Drug Discovery Deep Learning Models, Theoretical Molecular Structure
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Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning.

Scientific reports Jun 4, 2021
We developed a method to improve protein thermostability, "loop-walking method". Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as ...
Mutagenesis Hydrolases Mutagenesis, Site-Directed Protein Engineering Polymerase Chain Reaction Molecular Conformation Hot Temperature Burkholderia cepacia Proteins Mutation Kinetics Molecular Dynamics Simulation Enzyme Stability Plasmids Lipase Machine Learning Computational Biology Escherichia coli
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MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints.

Interdisciplinary sciences, computational life sciences Jan 27, 2021
An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In ...
Mutagenesis Neural Networks, Computer Drug Discovery Mutagens
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Sequence-to-function deep learning frameworks for engineered riboregulators.

Nature communications Oct 7, 2020
While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules....
Structure-Activity Relationship Natural Language Processing Datasets as Topic Synthetic Biology Base Sequence Genome, Human Genome, Viral Humans Computer Simulation Deep Learning Models, Genetic Mutagenesis Riboswitch Biotechnology Genetic Engineering
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Identification of the human DPR core promoter element using machine learning.

Nature Sep 9, 2020
The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to the initiation of DNA transcription, but the downstream core promoter in humans has been difficult to understand. Here we analyse the human ...
RNA Polymerase II Consensus Sequence TATA Box Models, Genetic Support Vector Machine Promoter Regions, Genetic High-Throughput Nucleotide Sequencing Mutagenesis HeLa Cells Datasets as Topic Transcription, Genetic Humans Cells Gene Expression Regulation Computer Simulation Base Sequence
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A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Cell Jun 6, 2019
Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over ...
RNA Cleavage Gene Expression RNA-Seq Synthetic Biology Base Sequence Humans RNA, Messenger HEK293 Cells Transcriptome Mutagenesis Models, Genetic Deep Learning Polyadenylation Databases, Genetic 3' Untranslated Regions
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