The scoring of poses in protein-protein docking: current capabilities and future directions

By Iain H. Moal, Mieczyslaw Torchala, Paul A. Bates, Juan Fernández-Recio. 2013

110 citations recorded by the source corpus.

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling. We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically. All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

Subfields: Computational Theory and Mathematics, 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 Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

DockQ: A Quality Measure for Protein-Protein Docking Models (2016, methodology): “Thus, the scoring part of CAPRI [8] and benchmarks of scoring functions for docking [11,10,12,13] only focuses on the ability to select good models according to the CAPRI classification, completely ignoring the potential useful information in the lower ranked models in assessing the ability to…”
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021, methodology): “Protein docking methods (Chen et al., 2003; Venkatraman et al., 2009; De Vries et al., 2010; Biesiada et al., 2011; Torchala et al., 2013; Schindler et al., 2017; Weng et al., 2019; Sunny and Jayaraj, 2021; Christoffer et al., 2021; Yan et al., 2020) typically run several steps: ﬁrst, they sample…”
Finding correct protein–protein docking models using ProQDock (2016, unknown): “…'CAPRI' set (Lensink and Wodak, 2014), which contains 19 013 models from 15 targets submitted to the CAPRI scoring experiment, and the 'MOAL' set (Moal et al., 2013) containing 56 015 models for 118 targets from the docking Benchmark 4.0 (Hwang et al., 2010) using SwarmDock (Torchala et al.,…”
SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation (2018, methodology): “functions [48], [46] which were subsequently implemented in the CCharPPI web server for characterising protein-protein interactions [49], as well as being used for ranking docked poses [45], [58], [6], [50].”
Protein Docking Model Evaluation by Graph Neural Networks (2020, background): “The accuracy of scoring decoys certainly determines the overall performance of protein docking, and thus, there is active development of scoring functions (Moal et al., 2013) for docking models.”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “All four energy functions, in most cases, outperform the Miyazawa-Jernigan MJ3h statistical potential (80), which has been recently shown to be one of the top-performing scoring functions in protein docking (81).”
Score_set: A CAPRI benchmark for scoring protein complexes (2014, methodology): “24,25 But so far developers needed to generate their own false examples (non-specific binding solutions), which they usually produce using their own or only a few other docking algorithms.”