AIMC Topic: Codon, Terminator

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Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids.

Nature communications
The pyrrolysyl-tRNA synthetase (PylRS) is widely used to incorporate noncanonical amino acids (ncAAs) into proteins. However, the yields of most ncAA-containing protein  remain low due to the limited activity of PylRS variants. Here, we apply machine...

Deep learning modeling mA deposition reveals the importance of downstream cis-element sequences.

Nature communications
The N-methyladenosine (mA) modification is deposited to nascent transcripts on chromatin, but its site-specificity mechanism is mostly unknown. Here we model the mA deposition to pre-mRNA by iM6A (intelligent mA), a deep learning method, demonstratin...

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning.

Molecules (Basel, Switzerland)
A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused by premature termination codon (PTC) mutations in the dystrophin gene, leading to the production of a truncated, non-functional dystrophin polypeptide. PTC-suppressing com...

Predicting functional consequences of SNPs on mRNA translation via machine learning.

Nucleic acids research
The functional impact of single nucleotide polymorphisms (SNPs) on translation has yet to be considered when prioritizing disease-causing SNPs from genome-wide association studies (GWAS). Here we apply machine learning models to genome-wide ribosome ...