| Chapter title |
YASARA: A Tool to Obtain Structural Guidance in Biocatalytic Investigations
|
|---|---|
| Chapter number | 4 |
| Book title |
Protein Engineering
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7366-8_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7364-4, 978-1-4939-7366-8
|
| Authors |
Henrik Land, Maria Svedendahl Humble, Land, Henrik, Humble, Maria Svedendahl |
| Abstract |
In biocatalysis, structural knowledge regarding an enzyme and its substrate interactions complements and guides experimental investigations. Structural knowledge regarding an enzyme or a biocatalytic reaction system can be generated through computational techniques, such as homology- or molecular modeling. For this type of computational work, a computer program developed for molecular modeling of proteins is required. Here, we describe the use of the program YASARA Structure. Protocols for two specific biocatalytic applications, including both homology modeling and molecular modeling such as energy minimization, molecular docking simulations and molecular dynamics simulations, are shown. The applications are chosen to give realistic examples showing how structural knowledge through homology and molecular modeling is used to guide biocatalytic investigations and protein engineering studies. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 228 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 39 | 17% |
| Student > Master | 31 | 14% |
| Student > Ph. D. Student | 24 | 11% |
| Researcher | 17 | 7% |
| Lecturer | 8 | 4% |
| Other | 28 | 12% |
| Unknown | 81 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 57 | 25% |
| Agricultural and Biological Sciences | 25 | 11% |
| Chemistry | 18 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 12 | 5% |
| Medicine and Dentistry | 6 | 3% |
| Other | 19 | 8% |
| Unknown | 91 | 40% |