Chapter title |
Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions
|
---|---|
Chapter number | 5 |
Book title |
Modeling Peptide-Protein Interactions
|
Published in |
Methods in molecular biology, February 2017
|
DOI | 10.1007/978-1-4939-6798-8_5 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6796-4, 978-1-4939-6798-8
|
Authors |
Christina Schindler, Martin Zacharias |
Editors |
Ora Schueler-Furman, Nir London |
Abstract |
Peptide-protein interactions are abundant in the cell and form an important part of the interactome. Large-scale modeling of peptide-protein complexes requires a fully blind approach; i.e., simultaneously predicting the peptide-binding site and the peptide conformation to high accuracy. Here, we present one of the first fully blind peptide-protein docking protocols, pepATTRACT. It combines a coarse-grained ensemble docking search of the entire protein surface with two stages of atomistic flexible refinement. pepATTRACT yields high-quality predictions for 70 % of the cases when tested on a large benchmark of peptide-protein complexes. This performance in fully blind mode is similar to state-of-the-art local docking approaches that use information on the location of the binding site. Limiting the search to the peptide-binding region, the resulting pepATTRACT-local approach further improves the performance. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html . Here, we explain how to set up a docking run with the pepATTRACT web interface and demonstrate its usage by an application on binding of disordered regions from tumor suppressor p53 to a partner protein. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 10 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Professor | 2 | 20% |
Other | 2 | 20% |
Researcher | 2 | 20% |
Professor > Associate Professor | 1 | 10% |
Unknown | 3 | 30% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 2 | 20% |
Agricultural and Biological Sciences | 2 | 20% |
Chemistry | 1 | 10% |
Unknown | 5 | 50% |