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We designed a Support Vector Regression (SVR) model that selects correct binding site geometries in a large ensemble of multiple receptor conformations.
We also propose that the NMA encoded multiple receptor conformations (MRC) of BACE1 elucidate the pharmacophoric feature necessary to inhibit the enzyme by a polyphenol, myricetin.
Multiple Receptor Conformations (MRC) is a straightforward and intuitive way to discretely mimic target plasticity [19].
On average, the MRC-score AUAC fell in the 70th percentile or better, suggesting that multiple receptor conformations enhanced the ability of this protocol to separate binders from non-binders with respect to SRC-VLS.
Therefore if we consider the explicit flexibility using multiple receptor conformations, there are also a number of approaches.
In addition, Kiselyov et al. proposed a mathematical model that describes the kinetic properties of insulin and insulin-like growth factor 1 receptor binding and activation, by considering the combinatorial complexity arising from multivalent binding to multiple receptor conformations.
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Here we show that using multiple fixed receptor conformations, either experimentally determined by crystallography or NMR, or computationally generated, is a practical shortcut that may improve docking calculations.
Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders.
However, the optimal number of receptor conformations is yet to be determined.
The models were subjected to molecular simulations in solvated lipid bilayers to sample different receptor conformations.
Additional analysis was also performed for homology models with up to 20 receptor conformations considered.
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