From sequence to function and back – High-throughput sequence-function mapping in synthetic biology

Höllerer, Simon; Desczyk, Charlotte; Muro, Ricardo Farrera; Jeschek, Markus

doi:10.1016/j.coisb.2023.100499

Cited by 4 publications

(1 citation statement)

References 75 publications

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“…Once a library has been generated, it is often challenging to measure a sufficiently large set of sequence-activity data. In some cases, high-throughput assays such as fluorescence-activated cell sorting can be combined with deep sequencing to obtain very large data sets. , However, most enzymatic reactions of industrial relevance require more laborious analytical procedures to obtain a readout for activity. Moreover, the need to also obtain sequence information on all tested variants can lead to prohibitive costs if conventional Sanger sequencing is used.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Vornholt,

Mutný,

Schmidt

et al. 2024

ACS Cent. Sci.

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Tailored enzymes are crucial for the transition to a sustainable bioeconomy. However, enzyme engineering is laborious and failure-prone due to its reliance on serendipity. The efficiency and success rates of engineering campaigns may be improved by applying machine learning to map the sequence-activity landscape based on small experimental data sets. Yet, it often proves challenging to reliably model large sequence spaces while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning, which we applied to engineer an artificial metalloenzyme (ArM) catalyzing a new-to-nature hydroamination reaction. Combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds. Critical characteristics of our pipeline include the cost-effective generation of information-rich data sets, the integration of an explorative round to improve the model’s performance, and the inclusion of experimental noise. Our approach led to an order-of-magnitude boost in the hit rate while making efficient use of experimental resources. Search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.

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Section: Introductionmentioning

confidence: 99%

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Vornholt,

Mutný,

Schmidt

et al. 2024

ACS Cent. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Vornholt,

Mutný,

Schmidt

et al. 2024

Preprint

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Tailored enzymes hold great potential to accelerate the transition to a sustainable bioeconomy. Yet, enzyme engineering remains challenging as it relies largely on serendipity and is, therefore, highly laborious and prone to failure. The efficiency and success rates of engineering campaigns may be improved substantially by applying machine learning to construct a comprehensive representation of the sequence-activity landscape from small sets of experimental data. However, it often proves challenging to reliably model a large protein sequence space while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning and model-guided library design. We applied this strategy to efficiently engineer an artificial metalloenzyme (ArM) catalysing a new-to-nature hydroamination reaction. By combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds according to user-defined objectives. Crucial characteristics of our enhanced enzyme engineering pipeline include i) the cost-effective generation of information-rich experimental data sets, ii) the integration of an explorative round to improve the performance of the model, as well as iii) the consideration of experimental noise during modelling. Our approach led to an order-of-magnitude boost in the hit rate of screening while making efficient use of experimental resources. Smart search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.

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Decoding Stability and Epistasis in Human Myoglobin by Deep Mutational Scanning and Codon-level Machine Learning

Küng,

Protsenko,

Vanella

et al. 2024

Preprint

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Understanding the linkage between protein sequence and phenotypic expression level is crucial in biotechnology. Machine learning algorithms trained with deep mutational scanning (DMS) data have significant potential to improve this understanding and accelerate protein engineering campaigns. However, most machine learning (ML) approaches in this domain do not directly address effects of synonymous codons or positional epistasis on predicted expression levels. Here we used yeast surface display, deep mutational scanning, and next-generation DNA sequencing to quantify the expression fitness landscape of human myoglobin and train ML models to predict epistasis of double codon mutants. When fed with near comprehensive single mutant DMS data, our algorithm computed expression fitness values for double codon mutants using ML-predicted epistasis as an intermediate parameter. We next deployed this predictive model to screen > 3·106unseen double codon mutantsin silicoand experimentally tested highly ranked candidate sequences, finding 14 of 16 with significantly enhanced expression levels. Our experimental DMS dataset combined with codon level epistasis-based ML constitutes an effective method for bootstrapping fitness predictions of high order mutational variants using experimental data from variants of lower order.

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From sequence to function and back – High-throughput sequence-function mapping in synthetic biology

Cited by 4 publications

References 75 publications

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Decoding Stability and Epistasis in Human Myoglobin by Deep Mutational Scanning and Codon-level Machine Learning

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