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Similarly, changes in the vanadium hyperfine coupling constant with ligand binding have been calculated using model complexes and DFT methods.
Synthetic model complexes and molecular catalysts inspired by nature have unveiled the structural and functional basis of the active site with remarkable accuracy and this has led to the discovery of active synthetic catalysts.
This study provides insight into the vast differences in the catalytic efficiency between model complexes and NHases.
Our studies are compared with small model complexes and the effect of protein and environment on the kinetics and mechanism is explained.
This is >500-fold higher than second-order rate constants for comparable small molecule model complexes and within ∼100-fold of the efficiency of CAII, the fastest of the α-CA isozymes.
Additional experiments are necessary to determine the role and function of all components of the proposed model complexes and of the complicated interactions between them, including dsDNA, RNA Pol II, EWS, transcription factors, and other functional components.
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However, the worth of ANNs to model complex and nonlinear problems is desirable for many real-world problems.
Even though this may ease the implementation of practices, this makes the model complex and difficult to understand.
Artificial neural networks are widely used to model complex and non-linear relations between inputs and outputs or to determine patterns in data (Diamantopoulou 2012).
The objective of this work is to introduce the use of asymmetric peak functions to model complex and anisotropic soil property depth profiles.
With in silico simulations, one can model complex and highly diverse communities, but the models used to sample reads from isolate genomic data are limited in their ability to capture biases introduced by experimental protocol.
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