Enhancing the Efficiency of Directed Evolution in Focused Enzyme Libraries by the Adaptive Substituent Reordering Algorithm

Xiao, Feng; Sanchis, Joaquı́n; Reetz, Manfred T.; Rabitz, Herschel

doi:10.1002/chem.201103811

Cited by 49 publications

(69 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the present stage, such techniques are mostly used to restrict mutagenesis to relevant protein regions [72][73][74][75]77,78] or to guide directed evolution "on-the-fly" [199]. A second filtering, for example, could then be performed with fast molecular modeling techniques such as FoldX or Rosetta.…”

Section: Fig 3 Proposed Computer-aided Directed Evolution Workflowmentioning

confidence: 99%

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Monza

Acebes

Lucas

et al. 2017

Directed Enzyme Evolution: Advances and Applications

View full text Add to dashboard Cite

Directed evolution creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship The final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-319-50413-1_10/fulltext.html of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of art of molecular modeling, discussing their strengths, limitations and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques, and a synergic effort between simulations and experimental validation. IntroductionBiotechnology needs catalysts that can work under harsh conditions, catalyze a broad range of substrates, generate maximum amount of product, and tolerate changes in the environment. Enzymes, which are biodegradable and reusable catalysts [1], in addition to remarkable reaction rates, can work in environmentally friendly pH and temperature ranges, and display control over stereochemistry and regioselectivity which makes them ideal for many applications [2,3]. When thinking about enzymes, people normally associate them to expressions such as "perfect catalysts" or "outstanding reaction rate". In fact, there are examples of enzymes that catalyze reactions at extremely high rates such as triose phosphate isomerase, superoxide dismutase or carbonic anhydrase [4]. These are often limited only by the rate of ligand diffusion into the active site (diffusion-controlled rate). Nevertheless, an extensive analysis by Bar-Even et al., of nearly 2000 enzymes, showed that the median maximal turnover rate value over all measured enzymes is about 10 s −1 nowhere close to the values of 10 5 or 10 6 normally associated with catalysts [5,6].So, it would appear that natural enzymes are "just good enough" for the function they must perform in a given organism [7]. One might conclude that if they had evolved to their optimum performance then trying to improve them (from a kinetic point of view) would be attempting the impossible. On the contrary, as seen by the distribution of reaction rates, k cat , most enzymes function at a lower rate than the diffusion-limit and thus, there is space to further increase their kinetic properties to meet industrial needs. Additionally, we need enzymes capable of catalyzing reactions for which no known enzymes exist, to work with different substrates and for particular conditions that are industrially...

show abstract

Section: Fig 3 Proposed Computer-aided Directed Evolution Workflowmentioning

confidence: 99%

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Monza

Acebes

Lucas

et al. 2017

Directed Enzyme Evolution: Advances and Applications

View full text Add to dashboard Cite

show abstract

“…A substituent reordering algorithm was designed especially for optimization of protein properties in focused libraries [16]; also see Section 2.4.4 for like applications to molecular discovery. The algorithm was evaluated in a proof-of-principle study to optimize the enatioselectivity (E-value) of the expoxide hydrolase from Aspergillus niger.…”

Section: Application Of Optievo Theory To Directed Evolutionmentioning

confidence: 99%

“…Building on this foundation, OptiEvo lends itself to a general substituent reordering strategy for optimizing target properties of complex biosystems with minimal sampling effort and without requiring explicit information about the structure-property relationships [16,24]. In cases where gene mutations occur at many sites in the directed evolution experiments, the order of ∼ 10 3 − 10 4 samples may be required for reliable prediction.…”

Section: Perspectivementioning

confidence: 99%

Fundamental Principles of Control Landscapes with Applications to Quantum Mechanics, Chemistry and Evolution

Rabitz

et al. 2014

Recent Advances in the Theory and Application of Fitness Landscapes

Self Cite

View full text Add to dashboard Cite

Abstract. The concept of a landscape or response surface naturally arises in applications widely ranging over the sciences, engineering and other disciplines. A landscape is the desired output as a function of a set of input variables, often of very high dimension. The relationship between the features of a landscape and the input variables is usually unknown a priori and often thought to be highly complex due to the anticipated intricate interactions involved. This chapter reviews recent developments in the analysis of landscape topology with the input variables considered as controls. Taking a control perspective allows for the specification of particular assumptions whose satisfaction permits a general analysis of the landscape topology. Satisfaction of these conditions leads to the conclusion that control landscapes should be devoid of suboptimal critical point traps, thereby permitting ready excursions without hindrance to the highest values of the landscape. These principles are set out in a general framework and then specifically illustrated for applications

show abstract

“…Focused mutagenesis is mainly used in semi-rational protein design experiments [14] to improve localized properties such as activity [9,[19][20][21], selectivity [22][23][24] and, less-frequently, thermal resistance [15,25,26]. The prerequisite is a crystal structure or a homology model to identify the 'beneficial' residues that are subsequently randomized [27].…”

Section: State Of the Art And Challengesmentioning

confidence: 99%

To get what we aim for – progress in diversity generation methods

2013

View full text Add to dashboard Cite

Protein re‐engineering by directed evolution has become a standard approach for tailoring enzymes in many fields of science and industry. Advances in screening formats and screening systems are fueling progress and enabling novel directed evolution strategies, despite the fact that the quality of mutant libraries can still be improved significantly. Diversity generation strategies in directed enzyme evolution comprise three options: (a) focused mutagenesis (selected residues are randomized); (b) random mutagenesis (mutations are randomly introduced over the whole gene); and (c) gene recombination (stretches of genes are mixed to chimeras in a random or rational manner). Either format has both advantages and limitations depending on the targeted enzyme and property. The quality of diverse mutant libraries plays a key role in finding improved mutants. In this review, we summarize methodological advancements and novel concepts (since 2009) in diversity generation for all three formats. Advancements are discussed with respect to the state of the art in diversity generation and high‐throughput screening capabilities, as well as robustness and simplicity in use. Furthermore, limitations and remaining challenges are emphasized ‘to get what we aim for’ through ‘optimal diversity’ generation.

show abstract

Enhancing the Efficiency of Directed Evolution in Focused Enzyme Libraries by the Adaptive Substituent Reordering Algorithm

Cited by 49 publications

References 67 publications

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Fundamental Principles of Control Landscapes with Applications to Quantum Mechanics, Chemistry and Evolution

To get what we aim for – progress in diversity generation methods

Contact Info

Product

Resources

About