AIMC Topic: Protein Biosynthesis

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Predicting photosynthetic bacteria-derived protein synthesis from wastewater using machine learning and causal inference.

Bioresource technology
Causal inference-assisted machine learning was used to predict photosynthetic bacterial (PSB) protein production capacity and identify key factors. The extreme gradient boosting algorithm effectively predicted protein content, while the gradient boos...

Current limitations in predicting mRNA translation with deep learning models.

Genome biology
BACKGROUND: The design of nucleotide sequences with defined properties is a long-standing problem in bioengineering. An important application is protein expression, be it in the context of research or the production of mRNA vaccines. The rate of prot...

Optimizing 5'UTRs for mRNA-delivered gene editing using deep learning.

Nature communications
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...

Prioritization of genes for translation: a computational approach.

Expert review of proteomics
INTRODUCTION: Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, a...

Integrated multiplexed assays of variant effect reveal determinants of catechol-O-methyltransferase gene expression.

Molecular systems biology
Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in...

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides.

Nature communications
Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low...

DeepTESR: A Deep Learning Framework to Predict the Degree of Translational Elongation Short Ramp for Gene Expression Control.

ACS synthetic biology
Controlling translational elongation is essential for efficient protein synthesis. Ribosome profiling has revealed that the speed of ribosome movement is correlated with translational efficiency in the translational elongation ramp. In this work, we ...

Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.

Nature communications
Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic...

Computed structures of core eukaryotic protein complexes.

Science (New York, N.Y.)
Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coe...

Protein Abundance Prediction Through Machine Learning Methods.

Journal of molecular biology
Proteins are responsible for most physiological processes, and their abundance provides crucial information for systems biology research. However, absolute protein quantification, as determined by mass spectrometry, still has limitations in capturing...