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. 2020 Aug;88(8):1070-1081.
doi: 10.1002/prot.25875. Epub 2020 Feb 7.

How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking

Devlina Chakravarty  1 , G W McElfresh  1 , Petras J Kundrotas  1 , Ilya A Vakser  1   2
Affiliations

How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking

Devlina Chakravarty et al. Proteins. 2020 Aug.

Abstract

Comparative docking is based on experimentally determined structures of protein-protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template-based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface-based docking performed marginally better. The interface-based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2-fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure-based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.

Keywords: protein recognition; sequence homology; structure prediction; structure similarity; template detection.

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Figures

FIGURE 1
FIGURE 1. Success rates of structure alignment docking protocols.
Success rate is defined as a fraction of complexes for which at least one near-native model is in top N (N = 1, 10 and 100 docking predictions. Near-native models are defined either as (A) the ones with i-RMSD < 10 Å or (B) acceptable and better quality according to CAPRI criteria. Gray parts of the bars correspond to targets with only lower accuracy near-native models (5 Å < i-RMSD < 10 Å) in top N predictions. The full set consists of 223 two-chain protein complexes from the DOCKGROUND benchmark 4. The subset (common templates set) is 75 complexes for which the same templates were selected for top-1 model by both protocols.
FIGURE 2
FIGURE 2. Quality of top 1 full and interface-based models.
The quality categories: incorrect (I), acceptable (A), medium (M) and high (H) are according to CAPRI criteria. The number at the circular arrows is the number of bound/unbound model pairs, which belong to the same category in full and interface alignment. The numbers at straight arrows are the number of model pairs that change category in full and interface alignment protocols (arrows indicate transition from the full to the interface models).
FIGURE 3
FIGURE 3. Correlation of prediction accuracy when the same template is selected by different docking protocols.
All predictions are of acceptable or better quality according to CAPRI criteria.
FIGURE 4
FIGURE 4. Examples of top 1 docking predictions generated for targets with better quality of the full alignment-based model.
(A) E. coli complex of N utilization substance protein B and 30S ribosomal protein S10 (3d3cAJ), (B) yeast Importin beta-1 subunit bound to GTP-binding nuclear protein RAN from Canis lupus familiaris (2bkuBA), (C) human epidermal growth factor in complex with its receptor (1nqlBA), and (D) Evasin-1 protein from brown dock tick bound to human chemokine 3 (3fpuAB). Receptor is in green, ligand of the docking model is in dark blue, and ligand of the native structure is in cyan. In the right-hand panel A, interface of the native receptor is in blue and that of the ligand in red. Additional information, including analysis, is in Figures S1 – S4 and Table 2.
FIGURE 5
FIGURE 5. Examples of top 1 docking predictions generated for targets with better quality of the interface alignment-based model.
(A) Mouse complex of junctional adhesion molecule-like and Coxsackievirus and adenovirus receptor homolog (3mj7AB), (B) archaeal homolog of the human protein complex Rpp21-Rpp29 from Pyrococcus horikoshii (2zaeAB), (C) human complex of Endothelial PAS domain-containing protein 1 and Aryl hydrocarbon receptor nuclear translocator (3f1pAB), and (D) VipD protein from Legionella pneumophila bound to human Ras-related protein Rab-5C (4kyiAB). Receptor is in green, ligand of the docking model is in dark blue, and ligand of the native structure is in cyan. Additional information, including analysis, is in Figures S5 – S8 and Table 3.
FIGURE 6
FIGURE 6. Example of a target-template pair with structurally similar proteins and matching CATH and ECOD annotations, but different binding sites.
The target, Escherichia coli histidine-containing phosphotransfer (HPt) domain of anaerobic sensor protein ArcB bound to chemotaxis response regulator CheY (1bdjBA), is in cyan and dark blue. The top-1 template selected by both full and interface alignment protocols, yeast phosphorelay intermediate protein YPD1 bound to osmo-sensing histidine protein kinase SLN1-R1 (2r25AB), is in red and yellow.
FIGURE 7
FIGURE 7. Number of targets with near native prediction by different alignment protocols.
The near native prediction is defined as one of acceptable or better quality according to CAPRI criteria.
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