Enhancing a<i>de novo</i>enzyme activity by computationally-focused ultra-low-throughput screening

Risso, Valeria A.; Romero‐Rivera, Adrian; Gutierrez-Rus, Luis I.; Ortega‐Muñoz, Mariano; Santoyo‐González, Francisco; Gavira, José A.; Sanchez-Ruiz, José M.; Kamerlin, Shina Caroline Lynn

doi:10.1039/d0sc01935f

Cited by 34 publications

(46 citation statements)

References 108 publications

(218 reference statements)

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“…If the improvements seen during the evolution of their Go-playing reinforcement-learning-based programs [54,165,166] are anything of a guide, we may soon anticipate considerable further improvements. Similar comments might be made about the activities of specific protein sequences [167][168][169][170].…”

Section: Protein Structure Predictionmentioning

confidence: 93%

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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Section: Protein Structure Predictionmentioning

confidence: 93%

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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“…Modeling enzyme variants in the presence of the cognate substrate has shown to be helpful for both activity and specificity engineering ( Jha et al, 2015 ; Grisewood et al, 2017 ; Risso et al, 2020 ). Hence, obtaining an enzyme–substrate complex with accurate substrate-binding poses or transition state complex is the first step in the proposed computational pipeline.…”

Section: Module 2: Building the Enzyme–substrate Complexmentioning

confidence: 99%

“…On the downside, CADEE needs a calibrated reference state based on experimentally tested mutations to rigorously parameterize the EVB force field for high-quality prediction ( Amrein et al, 2017 ). Interestingly, the EVB approach was successfully applied to identify variants of a de novo Kemp eliminase enzyme ( Figure 3D ) generated by FuncLib ( Risso et al, 2020 ; see also Module 6).…”

Section: Module 5: Engineering Activity and Specificity Of Enzymesmentioning

confidence: 99%

“…Further optimization of biocatalysts can expand the solution space of an enzyme and enable the identification of novel synthetic pathways for biomanufacturing of chemicals ( Erb et al, 2017 ). Hence, engineering enzymes for tailored substrate specificity ( Amer et al, 2020 ; Eser et al, 2020 ), catalytic efficiency ( Risso et al, 2020 ), and stability ( Goldenzweig et al, 2016 ; Yu et al, 2017 ) are important for the implementation of novel biosynthetic systems.…”

Section: Introductionmentioning

confidence: 99%

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Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

Scherer

Fleishman

Jones

et al. 2021

Front. Bioeng. Biotechnol.

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To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO2 or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.

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“…Moreover, the successful development of methods of directed evolution [ 2 , 3 , 4 ], rational protein engineering based on bioinformatics analysis and molecular modeling provide a good chance to create more efficient variants of the wild-type enzyme [ 5 , 6 , 7 , 8 , 9 ]. The next step on the agenda is the development of methods for constructing de novo biocatalysts to carry out previously unknown reactions [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Prospects of Using Biocatalysis for the Synthesis and Modification of Polymers

Nikulin

Švedas

2021

Molecules

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Trends in the dynamically developing application of biocatalysis for the synthesis and modification of polymers over the past 5 years are considered, with an emphasis on the production of biodegradable, biocompatible and functional polymeric materials oriented to medical applications. The possibilities of using enzymes not only as catalysts for polymerization but also for the preparation of monomers for polymerization or oligomers for block copolymerization are considered. Special attention is paid to the prospects and existing limitations of biocatalytic production of new synthetic biopolymers based on natural compounds and monomers from biomass, which can lead to a huge variety of functional biomaterials. The existing experience and perspectives for the integration of bio- and chemocatalysis in this area are discussed.

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Enhancing ade novoenzyme activity by computationally-focused ultra-low-throughput screening

Abstract: De novo enzymes capable of efficiently catalysis of a non-natural reaction are obtained through minimalist design plus computationally-focused variant library screening.

Cited by 34 publications

References 108 publications

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

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

Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

Prospects of Using Biocatalysis for the Synthesis and Modification of Polymers

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