Quantitative Comparison of Conformational Ensembles

Wolfe, Kevin; Chirikjian, Gregory S.

doi:10.3390/e14020213

Cited by 11 publications

(12 citation statements)

References 38 publications

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“…Structure fitting is necessary to remove the bias of discriminability against whole molecule rotation and translation, as that is not the goal of this comparison. We expect the least‐square algorithm to be adequate for structure fitting because, as noted earlier, the structural differences between the apo and bound states are small. Note also that during ensemble comparison we consider only heavy atoms, and the spatial distributions of the hydrogen atoms are neglected.…”

Section: Resultsmentioning

confidence: 90%

Discerning intersecting fusion‐activation pathways in the Nipah virus using machine learning

2014

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The fusion of Nipah with host cells is facilitated by two of their glycoproteins, the G and the F proteins. The binding of cellular ephrins to the G head domain causes the G stalk domain to interact differently with F, which activates F to mediate virus-host fusion. To gain insight into how the ephrin-binding signal transduces from the head to the stalk domain of G, we examine quantitatively the differences between the conformational ensembles of the G head domain in its ephrin-bound and unbound states. We consider the human ephrins B2 and B3, and a double mutant of B2, all of which trigger fusion. The ensembles are generated using molecular dynamics, and the differences between them are quantified using a new machine learning method. We find that the portion of the G head domain whose conformational density is altered equivalently by the three ephrins is large, and comprises ∼25% of the residues in the G head domain. This subspace also includes the residues that are known to be important to F activation, which suggests that it contains at least one common signaling pathway. The spatial distribution of the residues constituting this subspace supports the model of signal transduction in which the signal transduces via the G head dimer interface. This study also adds to the growing list of examples where signaling does not depend solely on backbone deviations. In general, this study provides an approach to filter out conserved patterns in protein dynamics.

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Section: Resultsmentioning

confidence: 90%

Discerning intersecting fusion‐activation pathways in the Nipah virus using machine learning

2014

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“…These observations can be related to the average nature of the RMSD measure, which does not capture local structural similarity/dissimilarity between two structures. [54] Therefore, it seems that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with the receptor.…”

Section: Sqm Analysismentioning

confidence: 99%

Using the Semiempirical Quantum Mechanics in Improving the Molecular Docking: A Case Study with CDK2

Bagheri

Behnejad

Firouzi

et al. 2020

Molecular Informatics

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In this study, we use some modified semiempirical quantum mechanics (SQM) methods for improving the molecular docking process. To this end, the three popular SQM Hamiltonians, PM6, PM6‐D3H4X, and PM7 are employed for geometry optimization of some binding modes of ligands docked into the human cyclin‐dependent kinase 2 (CDK2) by two widely used docking tools, AutoDock and AutoDock Vina. The results were analyzed with two different evaluation metrics: the symmetry‐corrected heavy‐atom RMSD and the fraction of recovered ligand‐protein contacts. It is shown that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with a protein. It was also found that AutoDock is more successful than AutoDock Vina in producing the correct ligand poses (RMSD≤2.0 Å) and ranking of the poses. It is also demonstrated that the ligand optimization at the SQM level improves the docking results and the SQM structures have a significantly better fit to the observed crystal structures. Finally, the SQM optimizations reduce the number of close contacts in the docking poses and successfully remove most of the clash or bad contacts between ligand and protein.

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“…We further quantify the differences in bulk and adsorbed states using Jensen-Shannon (JS) distance between the φ-ψ distributions (see Fig. S8) [102][103][104][105]. JS distance is an information theory based metric that is used to measure the distance between distributions of a random variable.…”

Section: Conformations Of Amino Acids In Graphene Adsorbed Statementioning

confidence: 99%