AIMC Topic: Gene Editing

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Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.

Cell research Jul 5, 2024
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we ...
Gene Editing Protein Engineering Deep Learning Humans Mutation
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Machine learning prediction of prime editing efficiency across diverse chromatin contexts.

Nature biotechnology Jun 21, 2024
The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all ed...
Humans Machine Learning Chromatin RNA, Guide, CRISPR-Cas Systems Animals Gene Editing
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Optimizing 5'UTRs for mRNA-delivered gene editing using deep learning.

Nature communications Jun 20, 2024
mRNA therapeutics are revolutionizing the pharmaceutical industry, but methods to optimize the primary sequence for increased expression are still lacking. Here, we design 5'UTRs for efficient mRNA translation using deep learning. We perform polysome...
Humans Cell Line HEK293 Cells Deep Learning RNA, Messenger Polyribosomes Gene Editing Protein Biosynthesis 5' Untranslated Regions
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Artificial Intelligence and Computational Biology in Gene Therapy: A Review.

Biochemical genetics Apr 18, 2024
One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, de...
Genetic Therapy Computational Biology Gene Editing Humans Artificial Intelligence Machine Learning Genomics
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DNA shape features improve prediction of CRISPR/Cas9 activity.

Methods (San Diego, Calif.) Apr 17, 2024
The CRISPR/Cas9 genome editing technology has transformed basic and translational research in biology and medicine. However, the advances are hindered by off-target effects and a paucity in the knowledge of the mechanism of the Cas9 protein. Machine ...
Gene Editing Nucleic Acid Conformation CRISPR-Cas Systems Machine Learning Humans Epigenesis, Genetic Neural Networks, Computer DNA Bayes Theorem
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CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.

International journal of molecular sciences Apr 17, 2024
CRISPR/Cas9 is a powerful genome-editing tool in biology, but its wide applications are challenged by a lack of knowledge governing single-guide RNA (sgRNA) activity. Several deep-learning-based methods have been developed for the prediction of on-ta...
Humans Neural Networks, Computer Deep Learning CRISPR-Cas Systems Gene Editing RNA, Guide, CRISPR-Cas Systems
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CRISPR-M: Predicting sgRNA off-target effect using a multi-view deep learning network.

PLoS computational biology Mar 14, 2024
Using the CRISPR-Cas9 system to perform base substitutions at the target site is a typical technique for genome editing with the potential for applications in gene therapy and agricultural productivity. When the CRISPR-Cas9 system uses guide RNA to d...
Gene Editing Neural Networks, Computer CRISPR-Cas Systems Deep Learning RNA, Guide, CRISPR-Cas Systems
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Expanding genome editing scopes with artificial intelligence.

Science bulletin Oct 28, 2023
Gene Editing Artificial Intelligence CRISPR-Cas Systems
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Effector translocation and rational design of disease resistance.

Trends in microbiology Sep 29, 2023
The effector repertoire of a pathogen is dynamically evolving. However, the effector translocation mechanism, partly elucidated recently, may be conserved. By targeting the effector translocation machinery, rather than the individual evolving effecto...
Gene Editing Disease Resistance Artificial Intelligence Plant Diseases Humans
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Prediction of base editor off-targets by deep learning.

Nature communications Sep 2, 2023
Due to the tolerance of mismatches between gRNA and targeting sequence, base editors frequently induce unwanted Cas9-dependent off-target mutations. Here, to develop models to predict such off-targets, we design gRNA-off- target pairs for adenine bas...
Deep Learning Gene Editing Adenine Cytosine
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