Chapter title |
A Structure-Based Design Protocol for Optimizing Combinatorial Protein Libraries.
|
---|---|
Chapter number | 7 |
Book title |
Computational Design of Ligand Binding Proteins
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3569-7_7 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3567-3, 978-1-4939-3569-7
|
Authors |
Mark W. Lunt, Christopher D. Snow |
Editors |
Barry L. Stoddard |
Abstract |
Protein variant libraries created via site-directed mutagenesis are a powerful approach to engineer improved proteins for numerous applications such as altering enzyme substrate specificity. Conventional libraries commonly use a brute force approach: saturation mutagenesis via degenerate codons that encode all 20 natural amino acids. In contrast, this chapter describes a protocol for designing "smarter" degenerate codon libraries via direct combinatorial optimization in "library space."Several case studies illustrate how it is possible to design degenerate codon libraries that are highly enriched for favorable, low-energy sequences as assessed using a standard all-atom scoring function. There is much to gain for experimental protein engineering laboratories willing to think beyond site saturation mutagenesis. In the common case that the exact experimental screening budget is not fixed, it is particularly helpful to perform a Pareto analysis to inspect favorable libraries at a range of possible library sizes. |
X Demographics
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Other | 2 | 22% |
Researcher | 2 | 22% |
Student > Bachelor | 1 | 11% |
Student > Ph. D. Student | 1 | 11% |
Professor | 1 | 11% |
Other | 0 | 0% |
Unknown | 2 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 2 | 22% |
Agricultural and Biological Sciences | 2 | 22% |
Linguistics | 1 | 11% |
Neuroscience | 1 | 11% |
Engineering | 1 | 11% |
Other | 0 | 0% |
Unknown | 2 | 22% |