High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock

By Nawsad Alam, O. Goldstein, Bing Xia, Kathryn A. Porter, Dima Kozakov, O. Schueler‐Furman. 2017

144 citations recorded by the source corpus.

Peptide-protein interactions contribute a significant fraction of the protein-protein interactome. Accurate modeling of these interactions is challenging due to the vast conformational space associated with interactions of highly flexible peptides with large receptor surfaces. To address this challenge we developed a fragment based high-resolution peptide-protein docking protocol. By streamlining the Rosetta fragment picker for accurate peptide fragment ensemble generation, the PIPER docking algorithm for exhaustive fragment-receptor rigid-body docking and Rosetta FlexPepDock for flexible full-atom refinement of PIPER docked models, we successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy. Validation on a representative set of solved peptide-protein complex structures demonstrates the accuracy and robustness of our approach, and opens up the way to high-resolution modeling of many more peptide-protein interactions and to the detailed study of peptide-protein association in general. PIPER-FlexPepDock is freely available to the academic community as a server at http://piperfpd.furmanlab.cs.huji.ac.il.

Subfields: Materials Chemistry, Molecular Biology, Oncology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. O. Schueler‐Furman
2. Isak Johansson-Åkhe
3. Daisuke Kihara
4. Sean T. Flannery
5. Xiao Wang
6. Björn Wallner
7. Yang Zhang
8. Arne Elofsson

In Molecular Biology

1. Björn Wallner
2. Isak Johansson-Åkhe
3. O. Schueler‐Furman
4. Daisuke Kihara
5. Sean T. Flannery
6. Xiao Wang
7. Yang Zhang
8. Liam J. McGuffin

Citations in the corpus

• Improving peptide-protein docking with AlphaFold-Multimer using forced sampling (2021, methodology): “The PIPER-FlexPepDock protocol generates several possible peptide conformations which are docked on the surface of the receptor protein through PIPER FFT-based docking (Kozakov et al., 2006).”
• Harnessing protein folding neural networks for peptide–protein docking (2021, methodology): “In contrast to PFPD, using AF2 for peptide docking includes modeling of both the peptide and the receptor.”
• Protein Docking Model Evaluation by Graph Neural Networks (2020, background): “, 2012; Ritchie and Grudinin, 2016), peptide–protein docking (Kurcinski et al., 2015; Alam et al., 2017; Kurcinski et al., 2020), docking with disordered proteins (Peterson et al.”