Chapter title |
An Evolution-Based Approach to De Novo Protein Design.
|
---|---|
Chapter number | 12 |
Book title |
Computational Protein Design
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6637-0_12 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6635-6, 978-1-4939-6637-0
|
Authors |
Jeffrey R. Brender, David Shultis, Naureen Aslam Khattak, Yang Zhang, Brender, Jeffrey R., Shultis, David, Khattak, Naureen Aslam, Zhang, Yang |
Editors |
Ilan Samish |
Abstract |
EvoDesign is a computational algorithm that allows the rapid creation of new protein sequences that are compatible with specific protein structures. As such, it can be used to optimize protein stability, to resculpt the protein surface to eliminate undesired protein-protein interactions, and to optimize protein-protein binding. A major distinguishing feature of EvoDesign in comparison to other protein design programs is the use of evolutionary information in the design process to guide the sequence search toward native-like sequences known to adopt structurally similar folds as the target. The observed frequencies of amino acids in specific positions in the structure in the form of structural profiles collected from proteins with similar folds and complexes with similar interfaces can implicitly capture many subtle effects that are essential for correct folding and protein-binding interactions. As a result of the inclusion of evolutionary information, the sequences designed by EvoDesign have native-like folding and binding properties not seen by other physics-based design methods. In this chapter, we describe how EvoDesign can be used to redesign proteins with a focus on the computational and experimental procedures that can be used to validate the designs. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 28 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 5 | 18% |
Student > Ph. D. Student | 5 | 18% |
Student > Bachelor | 4 | 14% |
Student > Postgraduate | 3 | 11% |
Professor | 2 | 7% |
Other | 3 | 11% |
Unknown | 6 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 13 | 46% |
Agricultural and Biological Sciences | 4 | 14% |
Chemistry | 2 | 7% |
Mathematics | 1 | 4% |
Engineering | 1 | 4% |
Other | 0 | 0% |
Unknown | 7 | 25% |