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Abstract Computational modeling of protein–DNA complex structures has important implications in biomedical applications such as structure‐based, computer aided drug design. A key step in developing methods for accurate modeling of protein–DNA complexes is similarity assessment between models and their reference complex structures. Existing methods primarily rely on distance‐based metrics and generally do not consider important functional features of the complexes, such as interface hydrogen bonds that are critical to specific protein–DNA interactions. Here, we present a new scoring function, ComparePD, which takes interface hydrogen bond energy and strength into account besides the distance‐based metrics for accurate similarity measure of protein–DNA complexes. ComparePD was tested on two datasets of computational models of protein–DNA complexes generated using docking (classified as easy, intermediate, and difficult cases) and homology modeling methods. The results were compared with PDDockQ, a modified version of DockQ tailored for protein–DNA complexes, as well as the metrics employed by the community‐wide experiment CAPRI (Critical Assessment of PRedicted Interactions). We demonstrated that ComparePD provides an improved similarity measure over PDDockQ and the CAPRI classification method by considering both conformational similarity and functional importance of the complex interface. ComparePD identified more meaningful models as compared to PDDockQ for all the cases having different top models between ComparePD and PDDockQ except for one intermediate docking case.
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Assessing the binding properties of CASP14 targets and models
Abstract An important question is how well the models submitted to CASP retain the properties of target structures. We investigate several properties related to binding. First we explore the binding of small molecules as probes, and count the number of interactions between each residue and such probes, resulting in a binding fingerprint. The similarity between two fingerprints, one for the X‐ray structure and the other for a model, is determined by calculating their correlation coefficient. The fingerprint similarity weakly correlates with global measures of accuracy, and GDT_TS higher than 80 is a necessary but not sufficient condition for the conservation of surface binding properties. The advantage of this approach is that it can be carried out without information on potential ligands and their binding sites. The latter information was available for a few targets, and we explored whether the CASP14 models can be used to predict binding sites and to dock small ligands. Finally, we tested the ability of models to reproduce protein–protein interactions by docking both the X‐ray structures and the models to their interaction partners in complexes. The analysis showed that in CASP14 the quality of individual domain models is approaching that offered by X‐ray crystallography, and hence such models can be successfully used for the identification of binding and regulatory sites, as well as for assembling obligatory protein–protein complexes. Success of ligand docking, however, often depends on fine details of the binding interface, and thus may require accounting for conformational changes by simulation methods.
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- Award ID(s):
- 1759472
- PAR ID:
- 10364826
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Proteins: Structure, Function, and Bioinformatics
- Volume:
- 89
- Issue:
- 12
- ISSN:
- 0887-3585
- Page Range / eLocation ID:
- p. 1922-1939
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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