Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins

Saitô, Yutaka; Oikawa, Misaki; Nakazawa, Hikaru; Niide, Teppei; Kameda, Tomoshi; Tsuda, Koji; Umetsu, Mitsuo

doi:10.1021/acssynbio.8b00155

Cited by 132 publications

(141 citation statements)

References 43 publications

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“…higher-throughput methods such as deep mutational scanning (36) serve as valuable test beds for validating the latest machinelearning algorithms for regression (37,38) and design (39) that require more data. An evolution strategy similar in spirit to that described here was recently applied to the evolution of GFP fluorescence (40). However, the implementations are quite different.…”

Section: Discussionmentioning

confidence: 99%

“…However, the implementations are quite different. Saito et al (40) used Gaussian processes to rank sequences based on their probability of improvement, or the probability that a variant outperforms those in the training set. We take a different approach of identifying the optimal variants, focusing efforts in the area of sequence space with highest fitness.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning-assisted directed protein evolution with combinatorial libraries

Kan

Lewis

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

468

411

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To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine-learning models trained on tested variants provide a fast method for testing sequence space computationally. We validated this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (i.e., stereodivergence) of a new-to-nature carbene Si–H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79% ee (enantiomeric excess). By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine learning-assisted directed protein evolution with combinatorial libraries

Kan

Lewis

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

468

411

View full text Add to dashboard Cite

show abstract

“…The most beneficial mutations can then be fixed, deleterious mutations can be eliminated from the pool of considered mutations, and new mutations can be added to the pool. 13 Alternately, the model can be used to select combinations of mutations that have a high probability of improving function 45 or to directly predict highly improved variants. 10 Learning and selection can also be performed directly over sequences.…”

Section: Using Sequence-function Predictions To Guide Explorationmentioning

confidence: 99%

Machine-learning-guided directed evolution for protein engineering

2019

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Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.

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“…In recent years, various parameter optimization methods [17] such as Bayesian optimization (BO) and evolutionary algorithms have been proposed in the field of machine learning and applied to a wide range of actual problems such as parameter optimization of deep neural networks [18,19], combination of materials [20], and protein design [21]. Most parameter optimization techniques effectively find the optimal parameter.…”

Section: Introductionmentioning

confidence: 99%

Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning

Kanada¹,

Tokuhisa

Tsuda

et al. 2020

Biomolecules

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Accompanied with an increase of revealed biomolecular structures owing to advancements in structural biology, the molecular dynamics (MD) approach, especially coarse-grained (CG) MD suitable for macromolecules, is becoming increasingly important for elucidating their dynamics and behavior. In fact, CG-MD simulation has succeeded in qualitatively reproducing numerous biological processes for various biomolecules such as conformational changes and protein folding with reasonable calculation costs. However, CG-MD simulations strongly depend on various parameters, and selecting an appropriate parameter set is necessary to reproduce a particular biological process. Because exhaustive examination of all candidate parameters is inefficient, it is important to identify successful parameters. Furthermore, the successful region, in which the desired process is reproducible, is essential for describing the detailed mechanics of functional processes and environmental sensitivity and robustness. We propose an efficient search method for identifying the successful region by using two machine learning techniques, Bayesian optimization and active learning. We evaluated its performance using F1-ATPase, a biological rotary motor, with CG-MD simulations. We successfully identified the successful region with lower computational costs (12.3% in the best case) without sacrificing accuracy compared to exhaustive search. This method can accelerate not only parameter search but also biological discussion of the detailed mechanics of functional processes and environmental sensitivity based on MD simulation studies.

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Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins

Cited by 132 publications

References 43 publications

Machine learning-assisted directed protein evolution with combinatorial libraries

Machine learning-assisted directed protein evolution with combinatorial libraries

Machine-learning-guided directed evolution for protein engineering

Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning

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