Presyncodon, a Web Server for Gene Design with the Evolutionary Information of the Expression Hosts

Tian, Jian; Li, Qingbin; Chu, Xiaoyu; Wu, Ningfeng

doi:10.3390/ijms19123872

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…71 To this end, modern NP discovery makes use of biological engineering principles to regulate and optimize NP biosynthetic pathways. Indeed, ML and DL approaches have shown promise in assisting with promotor prediction, 72 promotor design, 73,74 codon optimization, 75 and protein engineering. 76 For example, Kotopka and Smolke 73 exemplified how DL can be used to predict promoter activity and generate novel ones.…”

Section: Revisiting the Waksman Platform With MLmentioning

confidence: 99%

Antibiotic discovery in the artificial intelligence era

Lluka

Stokes

2022

Annals of the New York Academy of Sciences

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As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution—artificial intelligence—offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models—namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high‐quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.

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Section: Revisiting the Waksman Platform With MLmentioning

confidence: 99%

Antibiotic discovery in the artificial intelligence era

Lluka

Stokes

2022

Annals of the New York Academy of Sciences

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“…By using Presyncodon, the authors designed eGFP and mApple and expressed in E. coli, which gave 2.3-and 1.7-fold higher fluorescence, respectively, than their counterparts that were optimized by only using high-frequency-usage codons. Later on, the same research group further extended this method to Bacillus subtilis and S. cerevisiae (Tian et al, 2018).…”

Section: Optimization Of Enzymes In a Natural Product Biosynthetic Pathway Codon Optimization Of Biosynthetic Genesmentioning

confidence: 99%

Computational Tools for Discovering and Engineering Natural Product Biosynthetic Pathways

2020

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Natural products (NPs), also known as secondary metabolites, are produced in bacteria, fungi, and plants. NPs represent a rich source of antibacterial, antifungal, and anticancer agents. Recent advances in DNA sequencing technologies and bioinformatics unveiled nature's great potential for synthesizing numerous NPs that may confer unprecedented structural and biological features. However, discovering novel bioactive NPs by genome mining remains a challenge. Moreover, even with interesting bioactivity, the low productivity of many NPs significantly limits their practical applications.Here we discuss the progress in developing bioinformatics tools for efficient discovery of bioactive NPs. In addition, we highlight computational methods for optimizing the productivity of NPs of pharmaceutical importance.

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“…Codon optimization tools can be used to increase protein expression towards improving the efficiency of manufacturing such products [ 15 ]. Codon optimization techniques that are based on biological indexes replace synonymous codons with the most abundant codon found in the host organism’s genome [ 17 ]. Our review shows that many industry-standard tools employ the aforementioned strategy, causing unintended consequences for the cell, such as an imbalanced tRNA pool [ 10 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

ICOR: improving codon optimization with recurrent neural networks

Jain

Mauro

et al. 2023

BMC Bioinformatics

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Background In protein sequences—as there are 61 sense codons but only 20 standard amino acids—most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid sequence, their selection can dramatically affect the expression of the resulting protein. Codon optimization of synthetic DNA sequences is important for heterologous expression. However, existing solutions are primarily based on choosing high-frequency codons only, neglecting the important effects of rare codons. In this paper, we propose a novel recurrent-neural-network based codon optimization tool, ICOR, that aims to learn codon usage bias on a genomic dataset of Escherichia coli. We compile a dataset of over 7,000 non-redundant, high-expression, robust genes which are used for deep learning. The model uses a bidirectional long short-term memory-based architecture, allowing for the sequential context of codon usage in genes to be learned. Our tool can predict synonymous codons for synthetic genes toward optimal expression in Escherichia coli. Results We demonstrate that sequential context achieved via RNN may yield codon selection that is more similar to the host genome. Based on computational metrics that predict protein expression, ICOR theoretically optimizes protein expression more than frequency-based approaches. ICOR is evaluated on 1,481 Escherichia coli genes as well as a benchmark set of 40 select DNA sequences whose heterologous expression has been previously characterized. ICOR’s performance is measured across five metrics: the Codon Adaptation Index, GC-content, negative repeat elements, negative cis-regulatory elements, and codon frequency distribution. Conclusions The results, based on in silico metrics, indicate that ICOR codon optimization is theoretically more effective in enhancing recombinant expression of proteins over other established codon optimization techniques. Our tool is provided as an open-source software package that includes the benchmark set of sequences used in this study.

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Presyncodon, a Web Server for Gene Design with the Evolutionary Information of the Expression Hosts

Cited by 8 publications

References 22 publications

Antibiotic discovery in the artificial intelligence era

Antibiotic discovery in the artificial intelligence era

Computational Tools for Discovering and Engineering Natural Product Biosynthetic Pathways

ICOR: improving codon optimization with recurrent neural networks

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