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Protein Engineering

Overview of attention for book
Cover of 'Protein Engineering'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Protein Engineering: Past, Present, and Future
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    Chapter 2 Rational and Semirational Protein Design
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    Chapter 3 Computational Analysis of Protein Tunnels and Channels
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    Chapter 4 YASARA: A Tool to Obtain Structural Guidance in Biocatalytic Investigations
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    Chapter 5 A Computational Library Design Protocol for Rapid Improvement of Protein Stability: FRESCO
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    Chapter 6 Directed Evolution of Proteins Based on Mutational Scanning
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    Chapter 7 A Brief Guide to the High-Throughput Expression of Directed Evolution Libraries
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    Chapter 8 Library Growth and Protein Expression: Optimal and Reproducible Microtiter Plate Expression of Recombinant Enzymes in E. coli Using MTP Shakers
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    Chapter 9 Normalized Screening of Protein Engineering Libraries by Split-GFP Crude Cell Extract Quantification
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    Chapter 10 Functional Analysis of Membrane Proteins Produced by Cell-Free Translation
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    Chapter 11 Practical Considerations Regarding the Choice of the Best High-Throughput Assay
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    Chapter 12 High-Throughput Screening Assays for Lipolytic Enzymes
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    Chapter 13 Continuous High-Throughput Colorimetric Assays for α -Transaminases
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    Chapter 14 Colorimetric High-Throughput Screening Assays for the Directed Evolution of Fungal Laccases
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    Chapter 15 Directed Coevolution of Two Cellulosic Enzymes in Escherichia coli Based on Their Synergistic Reactions
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    Chapter 16 Program-Guided Design of High-Throughput Enzyme Screening Experiments and Automated Data Analysis/Evaluation
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    Chapter 17 Solid-Phase Agar Plate Assay for Screening Amine Transaminases
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    Chapter 18 Ultrahigh-Throughput Screening of Single-Cell Lysates for Directed Evolution and Functional Metagenomics
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    Chapter 19 Isolation of pH-Sensitive Antibody Fragments by Fluorescence-Activated Cell Sorting and Yeast Surface Display
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    Chapter 20 Library Generation and Auxotrophic Selection Assays in Escherichia coli and Thermus thermophilus
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    Chapter 21 Erratum to: Functional Analysis of Membrane Proteins Produced by Cell-Free Translation
Attention for Chapter 6: Directed Evolution of Proteins Based on Mutational Scanning
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)

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Chapter title
Directed Evolution of Proteins Based on Mutational Scanning
Chapter number 6
Book title
Protein Engineering
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7366-8_6
Pubmed ID
Book ISBNs
978-1-4939-7364-4, 978-1-4939-7366-8
Authors

Carlos G. Acevedo-Rocha, Matteo Ferla, Manfred T. Reetz, Acevedo-Rocha, Carlos G., Ferla, Matteo, Reetz, Manfred T.

Abstract

Directed evolution has emerged as one of the most effective protein engineering methods in basic research as well as in applications in synthetic organic chemistry and biotechnology. The successful engineering of protein activity, allostery, binding affinity, expression, folding, fluorescence, solubility, substrate scope, selectivity (enantio-, stereo-, and regioselectivity), and/or stability (temperature, organic solvents, pH) is just limited by the throughput of the genetic selection, display, or screening system that is available for a given protein. Sometimes it is possible to analyze millions of protein variants from combinatorial libraries per day. In other cases, however, only a few hundred variants can be screened in a single day, and thus the creation of smaller yet smarter libraries is needed. Different strategies have been developed to create these libraries. One approach is to perform mutational scanning or to construct "mutability landscapes" in order to understand sequence-function relationships that can guide the actual directed evolution process. Herein we provide a protocol for economically constructing scanning mutagenesis libraries using a cytochrome P450 enzyme in a high-throughput manner. The goal is to engineer activity, regioselectivity, and stereoselectivity in the oxidative hydroxylation of a steroid, a challenging reaction in synthetic organic chemistry. Libraries based on mutability landscapes can be used to engineer any fitness trait of interest. The protocol is also useful for constructing gene libraries for deep mutational scanning experiments.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 88 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 17%
Student > Ph. D. Student 8 9%
Student > Bachelor 5 6%
Student > Master 5 6%
Professor > Associate Professor 2 2%
Other 3 3%
Unknown 50 57%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 18%
Agricultural and Biological Sciences 10 11%
Chemistry 2 2%
Medicine and Dentistry 2 2%
Immunology and Microbiology 1 1%
Other 3 3%
Unknown 54 61%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 December 2017.
All research outputs
#8,168,519
of 15,132,268 outputs
Outputs from Methods in molecular biology
#2,054
of 8,925 outputs
Outputs of similar age
#138,073
of 320,916 outputs
Outputs of similar age from Methods in molecular biology
#1
of 1 outputs
Altmetric has tracked 15,132,268 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,925 research outputs from this source. They receive a mean Attention Score of 2.6. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 320,916 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them