Acetylcholinesterase, with an active site located at the bottom of a narrow and deep gorge, provides a striking example of enzymes with buried active sites. Recent molecular dynamics simulations ...showed that reorientation of five aromatic rings leads to rapid opening and closing of the gate to the active site. In the present study the molecular dynamics trajectory is used to quantitatively analyze the effect of the gate on the substrate binding rate constant. For a 2.4- angstrom probe modeling acetylcholine, the gate is open only 2.4% of the time, but the quantitative analysis reveals that the substrate binding rate is slowed by merely a factor of 2. We rationalize this result by noting that the substrate, by virtue of Brownian motion, will make repeated attempts to enter the gate each time it is near the gate. If the gate is rapidly switching between the open and closed states, one of these attempts will coincide with an open state, and then the substrate succeeds in entering the gate. However, there is a limit on the extent to which rapid gating dynamics can compensate for the small equilibrium probability of the open state. Thus the gate is effective in reducing the binding rate for a ligand 0.4 angstrom bulkier by three orders of magnitude. This relationship suggests a mechanism for achieving enzyme specificity without sacrificing efficiency.
We have studied the dynamic properties of acetylcholinesterase dimer from Torpedo californica liganded with tacrine (AChE−THA) in solution using molecular dynamics. The simulation reveals ...fluctuations in the width of the primary channel to the active site that are large enough to admit substrates. Alternative entries to the active site through the side walls of the gorge have been detected in a number of structures. This suggests that transport of solvent molecules participating in catalysis can occur across the porous wall, contributing to the efficiency of the enzyme.
Brownian dynamics simulations of the encounter kinetics between the active site of the wild-type and Glu199 mutant of Torpedo californica acetylcholinesterase (TcAChE) with a charged substrate were ...performed. In addition, ab initio quantum chemical calculations using the 3-21G basis set were undertaken to probe the energetics of the transformation of the Michaelis complex into a covalently bound tetrahedral intermediate using various models of the wild-type and Glu199Gln mutant active sites. The quantum calculations predicted about a factor of 32 reduction in the rate of formation of the tetrahedral intermediate upon the Glu199Gln mutation and showed that the Glu199 residue located in the proximity of the enzyme active triad boosts AChE's activity in a dual fashion: (1) by increasing the encounter rate due to the favorable modification of the electric field inside the enzyme reaction gorge and (2) by stabilization of the transition state for the first chemical step of catalysis. Our calculations also demonstrate the critical role of the oxyanion hole in stabilization of the tetrahedral intermediate and suggests that a charge relay mechanism may operate in the Glu199Gln mutant AChE as opposed to a general base mechanism as in the wild-type enzyme.
A recent experimental study of human acetylcholinesterase has shown that the mutation of surface acidic residues has little effect on the rate constant for hydrolysis of acetylthiocholine. It was ...concluded, on this basis, that the reaction is not diffusion controlled and that electrostatic steering plays only a minor role in determining the rate. Here we examine this issue through Brownian dynamics simulations on Torpedo californica acetylcholinesterase in which the surface acidic residues homologous with those mutated in the human enzyme are artificially neutralized. The computed effects of the mutations on the rate constants reproduce quite well the modest effects of the mutations upon the measured encounter rates. Nonetheless, the electrostatic field of the enzyme is found to increase the rate constants by about an order of magnitude in both the wild type and the mutants. We therefore conclude that the mutation experiments do not disprove that electrostatic steering substantially affects the catalytic rate of acetylcholinesterase.
Conformational flexibility is a major determinant of the properties of macrocycles and other drugs in beyond rule of 5 (bRo5) space. Prediction of conformations is essential for design of drugs in ...this space, and we have evaluated three tools for conformational sampling of a set of 10 bRo5 drugs and clinical candidates in polar and apolar environments. The distance-geometry based OMEGA was found to yield ensembles spanning larger structure and property spaces than the ensembles obtained by MOE-LowModeMD (MOE) and MacroModel (MC). Both MC and OMEGA but not MOE generated different ensembles for polar and apolar environments. All three conformational search methods generated conformers similar to the crystal structure conformers for 9 of the 10 compounds, with OMEGA performing somewhat better than MOE and MC. MOE and OMEGA found all six conformers of roxithromycin that were identified by NMR in aqueous solutions, whereas only OMEGA sampled the three conformers observed in chloroform. We suggest that characterization of conformers using molecular descriptors, e.g., the radius of gyration and polar surface area, is preferred to energy- or root-mean-square deviation-based methods for selection of biologically relevant conformers in drug discovery in bRo5 space.
•Crop classification based on time series of Sentinel-1 images using SAR polarimetry.•Multi-temporal descriptors of crops phenology derived from coherence matrices and H/α decomposition.•Fast ...object-oriented classification approach with the use of Random Forest classifier.•County-wide testing and mapping.•Use of administrative data as a source of reference data.
Crop classification is a crucial prerequisite for the collection of agricultural statistics, efficient crop management, biodiversity control, the design of agricultural policy, and food security. Crops are characterized by significant change during the growing season, and this information can be used to improve classification accuracy. However, capturing variation in vegetation cover requires a reliable source of valid data. Sentinel-1 radar images are a good candidate, as they supply information about Earth’s surface every six days, independent of weather and light conditions. In this paper, we present a method for crop classification based on radar polarimetry. We propose a set of multi-temporal indices derived from time series Sentinel-1 images that aim to characterize crop phenology. A big data, object-oriented classification technique is developed and tested on 16 crop types for the whole of Poland. Our analysis found that overall accuracy varied (regionally) from 86.36 to 89.13% in 2019, and from 85.95 to 89.81% in 2020. F1 scores for individual crops varied from 0.73 to 0.99, and the use of our multi-temporal phenological indices increased F1 scores by about 0.14 compared to calculations using only basic parameters. Results obtained for the whole country demonstrate the efficacy of the method and its resistance to environmental conditions.
Accurate conformations of a molecule are critical for reliable prediction of its properties, so good predictive models require good conformations. Here, we present a method for conformer sampling ...based on distance geometry, implemented in our conformation generator OMEGA, which we apply to both macrocycles and druglike molecules. We validate it in the usual fashion, reproducing conformations from the solid state, and compare its performance in detail to other methods. We find that OMEGA performs well on three key criteria: accuracy, speed, and ensemble size. To support our conclusions quantitatively, particularly on accuracy, we developed a workflow for method comparison that uses parameter estimation, inference from confidence intervals, classical null hypothesis significance testing, Bayesian estimation, and effect size. The workflow is designed to be robust to the highly skewed performance data often found when validating tools in computational chemistry and to provide reliable, easy to interpret results. In this workflow, we emphasize the importance of confidently distinguishing between methods, with particular reference to a priori estimation of sample size and statistical power (false negative or Type II error rate), a topic almost completely ignored hitherto in computational chemistry.
This study uses time-series Sentinel-1(S-1) synthetic aperture radar images to evaluate the impact of multi-temporal polarimetric processing on land-cover classification. Various polarimetric ...processing methods are applied to multi-temporal S-1 data set in order to obtain several inputs parameters for land-cover classification: e.g. time-series coherence matrices from dual-polarization data (shows coherence among polarizations in matrix for separated time points t
1
, t
2
, to t
n
); scatter zone time series; multi-temporal single and dual-polarization coherence matrices (reveal coherences among time points for one or two polarizations); and parameters from the H/α decomposition. Then, the classification potential of each polarimetric data set is compared to a reference classification, which was derived from time series of dual-polarization backscatter (σ
0
) images. We evaluate if polarimetric processing of dual-polarization images brings better classification results than alone classification of backscatter image. Finally, we evaluate the impact of segment size and the classifier on classification accuracy.
The classification based on polarimetric data sets is consistently better than that of backscatter time series. A maximum overall accuracy of 93.2% was achieved for the classification of four basic land-cover classes (urban, agriculture, forest, and water) using a composite data set made up time series of scatter zone derived from the H/α plane and scatter zone temporal stability maps. This accuracy was 4.5% better compared to our reference classification based on σ
0
time series. Similar trends were observed for more detailed land-cover classes. Classification accuracy is heavily influenced by segment size and can drop by about 15% for very small segments. The most suitable classifier proved to be the Support Vector Machine, which performed up to 12% better than the worst one. This study demonstrates the suitability of multi-temporal polarimetry processing for land--cover classification.
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