Dockground scoring benchmarks for protein docking. Issue 6 (5th February 2022)

• Record Type: Journal Article
• Authors: Kotthoff, Ian; Kundrotas, Petras J.; Vakser, Ilya A.
• Is Part Of: Proteins. Volume 90:Issue 6(2022)
• Page Start: 1259
• Page End: 1266
• Publication Date: 2022-02-05
• Subjects: modeling of protein complexes -- protein binding -- protein interactions -- structure datasets -- structure prediction; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.26306
• Languages: English
• ISSNs: 0887-3585

Abstract

Abstract: Protein docking protocols typically involve global docking scan, followed by re‐ranking of the scan predictions by more accurate scoring functions that are either computationally too expensive or algorithmically impossible to include in the global scan. Development and validation of scoring methodologies are often performed on scoring benchmark sets (docking decoys) which offer concise and nonredundant representation of the global docking scan output for a large and diverse set of protein–protein complexes. Two such protein–protein scoring benchmarks were built for the Dockground resource, which contains various datasets for the development and testing of protein docking methodologies. One set was generated based on the Dockground unbound docking benchmark 4, and the other based on protein models from the Dockground model–model benchmark 2. The docking decoys were designed to reflect the reality of the real‐case docking applications (e.g., correct docking predictions defined as near‐native rather than native structures), and to minimize applicability of approaches not directly related to the development of scoring functions (reducing clustering of predictions in the binding funnel and disparity in structural quality of the near‐native and nonnative matches). The sets were further characterized by the source organism and the function of the protein–protein complexes. The sets, freely available to the research community on the Dockground webpage, present a unique, user‐friendly resource for the developing and testing of protein–protein scoring approaches.