Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

The field of synthetic biology is evolving at a fast pace. It is advancing beyond single-gene alterations in single hosts to the logical design of complex circuits and the development of integrated synthetic genomes. Recent breakthroughs in deep learning, which is increasingly used in de novo assembly of DNA components with predictable effects, are also aiding the discipline. Despite advances in computing, the field is still reliant on the availability of precharacterized DNA parts, whether natural or synthetic, to regulate gene expression in bacteria and make valuable compounds. In this review, we discuss the different bacterial synthetic biology methodologies employed in the creation of 5 0 regulatory regionspromoters, untranslated regions and 5 0 -end of coding sequences. We summarize methodologies and discuss their significance for each of the functional DNA components, and highlight the key advances made in bacterial engineering by concentrating on their flaws and strengths. We end the review by outlining the issues that the discipline may face in the near future.

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“…(Lou et al ., 2012 ), B. ii. (Qi et al ., 2012 ), C. (Valeri et al ., 2020 ), D. (Höllerer et al ., 2020 ).…”

Section: Context Dependency Challengementioning

confidence: 99%

Importance of the 5′ regulatory region to bacterial synthetic biology applications

Tietze

Lale

2021

Microbial Biotechnology

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“…Strong selection in S. cerevisiae leads to plasmid copy variation [298,299] Role in regulating protein folding [300] Review of codon usage tables [301] Ribosome binding sites (RBS) RBS calculator [302] Machine learning in E. coli [303] Multiprotein RBS optimisation in various bacteria [304] Review of RBS calculator [305] Phenotypic recording with deep learning, using more than 2.7 M sequence-function pairs [306] Translation initiation optimisation Reviews [228,307,308] Significant increase in serine overproduction…”

Section: Codon Usagementioning

confidence: 99%

Intelligent host engineering for metabolic flux optimisation in biotechnology

Munro

Kell

2021

Biochemical Journal

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Optimising the function of a protein of length N amino acids by directed evolution involves navigating a ‘search space’ of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is ‘making such biology predictable’. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

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“…Another important area of optimisation is in microbial biotechnology, whether in finding the best growth medium [186], subsets of genes to manipulate to increase productivity [187,188], or optimal sequences for generating host [189] or protein properties [190,191]. Each of these represents a combinatorial search problem [8,9].…”

Section: Optimisationmentioning

confidence: 99%

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

Kell

Samanta

Swainston

2020

Biochemical Journal

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The number of ‘small’ molecules that may be of interest to chemical biologists — chemical space — is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved ‘forward’ problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). ‘Deep’ (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.

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Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Cited by 42 publications

References 60 publications

Importance of the 5′ regulatory region to bacterial synthetic biology applications

Importance of the 5′ regulatory region to bacterial synthetic biology applications

Intelligent host engineering for metabolic flux optimisation in biotechnology

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

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