Dockground: A comprehensive data resource for modeling of protein complexes

By Petras J. Kundrotas, Ivan V. Anishchenko, Taras Dauzhenka, Ian Kotthoff, Daniil Mnevets, Matthew M. Copeland, Ilya A. Vakser. 2018

83 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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. Dina Schneidman-Duhovny
2. Yumeng Yan
3. Huanyu Tao
4. Shengua Huang
5. Jiahua He
6. Jérôme Tubiana
7. Haim J. Wolfson
8. Patrick Bryant

In Molecular Biology

1. Dina Schneidman-Duhovny
2. Haim J. Wolfson
3. Huanyu Tao
4. Shengua Huang
5. Jiahua He
6. Gabriele Pozzati
7. Yumeng Yan
8. Arne Elofsson

Citations in the corpus

• Improved prediction of protein-protein interactions using AlphaFold2 (2021, methodology): “A set of heterodimeric complexes from Dockground benchmark 4 27 is used to develop the pipeline, focusing on the AF2 con�guration presented here.”
• ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction (2021, methodology): “Train and test sets constructed from the redundant Dockground template database [45].”
• The HDOCK server for integrated protein–protein docking (2020, background): “More docking examples can also be obtained from other public docking benchmarks 92 .”