AIMC Topic: Protein Biosynthesis

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[Research progress in mechanism models and artificial intelligence models for protein expression systems].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Proteins are the basic building blocks of life. Studying the protein expression mechanism is essential for understanding the cellular organization principles and the development of biotechnology. Protein expression, involving transcription, translati...

A deep learning model trained on expressed transcripts across different tissue types reveals cell-type codon-optimization preferences.

Nucleic acids research
Species-specific differences in protein translation can affect the design of protein-based drugs. Consequently, efficient expression of recombinant proteins often requires codon optimization. Publicly available optimization tools do not always result...

Exploring the potential of large language model-based chatbots in challenges of ribosome profiling data analysis: a review.

Briefings in bioinformatics
Ribosome profiling (Ribo-seq) provides transcriptome-wide insights into protein synthesis dynamics, yet its analysis poses challenges, particularly for nonbioinformatics researchers. Large language model-based chatbots offer promising solutions by le...

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.

Nucleic acids research
Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene e...

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 ...

Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning.

Nucleic acids research
It has been known for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synt...

Predicting protein condensate formation using machine learning.

Cell reports
Membraneless organelles are liquid condensates, which form through liquid-liquid phase separation. Recent advances show that phase separation is essential for cellular homeostasis by regulating basic cellular processes, including transcription and si...

A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential.

Nucleic acids research
The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, featu...