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Biosynthetic Pathways

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Deep Learning to Predict the Biosynthetic Gene Clusters in Bacterial Genomes.

Journal of molecular biology
Biosynthetic gene clusters (BGCs) in bacterial genomes code for important small molecules and secondary metabolites. Based on the validated BGCs and the corresponding sequences of protein family domains (Pfams), Pfam functions and clan information, w...

Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP.

Nature communications
The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, is developed to predict the biosynthet...

Design, Evaluation, and Implementation of Synthetic Isopentyldiol Pathways in .

ACS synthetic biology
Isopentyldiol (IPDO) is an important raw material in the cosmetic industry. So far, IPDO is exclusively produced through chemical synthesis. Growing interest in natural personal care products has inspired the quest to develop a biobased process. We p...

Enabling pathway design by multiplex experimentation and machine learning.

Metabolic engineering
The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures tha...

Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform.

Biotechnology advances
The rapid development of synthetic biology has significantly improved the capabilities of mono-culture systems in converting different substrates into various value-added bio-chemicals through metabolic engineering. However, overexpression of biosynt...

Unlocking plant bioactive pathways: omics data harnessing and machine learning assisting.

Current opinion in biotechnology
Plant bioactives hold immense potential in the medicine and food industry. The recent advancements in omics applied in deciphering specialized metabolic pathways underscore the importance of high-quality genome releases and the wealth of data in meta...

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.

ACS synthetic biology
Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically breaking down molecules into readily available building block compounds. Having a long history in chemistry, retro-biosynthesis has also been used in the fi...

Deep learning in template-free de novo biosynthetic pathway design of natural products.

Briefings in bioinformatics
Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway d...

Toward an integrated omics approach for plant biosynthetic pathway discovery in the age of AI.

Trends in biochemical sciences
Elucidating plant biosynthetic pathways is key to advancing a sustainable bioeconomy by enabling access to complex natural products through synthetic biology. Despite progress from genomic, transcriptomic, and metabolomic approaches, much multiomics ...

Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.

Nucleic acids research
Biosynthetic gene clusters (BGCs), key in synthesizing microbial secondary metabolites, are mostly hidden in microbial genomes and metagenomes. To unearth this vast potential, we present BGC-Prophet, a transformer-based language model for BGC predict...