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Thermal Modeling of Solar Drying Systems
Published in Om Prakash, Anil Kumar, Solar Drying Systems, 2020
The root mean square deviation (RMSD) is also known as the root mean square error. It measures the difference between predicted and experimental values. It is expressed mathematically as: RMSE/RMSD=∑pii2N Where pii=Xpr,i−Xex,iXex,i = ith experimental values
Linear Algebra in Biomolecular Modeling
Published in Leslie Hogben, Richard Brualdi, Anne Greenbaum, Roy Mathias, Handbook of Linear Algebra, 2006
Let X and Y be two n × 3 coordinate matrices for two lists of atoms in proteins A and B, respectively, where xi = (xi,1, xi,2, xi3)T is the coordinate vector of the i th atom selected from protein A to be compared with yi = (yi,1, yi,2, yi,3)T, the coordinate vector of the i th atom selected from protein B. Assume that X and Y have been translated so that their centers of geometry are located at the same position, say, at the origin. Then, the structural difference between the two proteins can be measured by using the root-mean-square deviation (RMSD) of the structures, RMSD(X,Y)=minQ‖X−YQ‖F/n, where Q is a 3 × 3 rotation matrix and QQT = I, and ║·║F is the matrix Frobenius norm.
A Study of Crime in India Through Statistical Analysis
Published in Durgesh Kumar Mishra, Nilanjan Dey, Bharat Singh Deora, Amit Joshi, ICT for Competitive Strategies, 2020
B. Mittal, A. K. Verma, S. Bagai
The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) (or sometimes root-mean-squared error) is used to measure the differences between the values predicted by the model and the values observed. RMSE is always greater or equal to zero, and a value of 0 indicates a perfect fit to the data. The lower the RMSE, the better the model.
Molecular dynamics simulation study on the inhibitory mechanism of RIPK1 by 4,5-dihydropyrazole derivatives
Published in Molecular Physics, 2023
Yurou Zhang, Song Wang, Aimin Ren, Shanshan Guan, E Jingwen, Zhijian Luo, Zhijie Yang, Xinyue Zhang, Juan Du, Hao Zhang
The docking results showed that all five inhibitors bound to RIPK1 in a similar posture and were stably present in the hydrophobic pocket formed by residues Leu70, Val75, Val76, Leu78, Leu129, Ala155, Leu159, Asp156, and Phe162. All inhibitors formed a hydrogen bond with the Asp156 of RIPK1 (Figure S1). The docking results were used as the initial conformation for molecular dynamics simulations of the five complexes. After the five systems were respectively conducted molecular dynamics simulations for 200 ns, the root-mean-square deviation (RMSD) of the protein Cα backbone atoms was computed to ensure the stability of the MD simulations. The variation of RMSD for all systems against simulation time was shown in Figure 3, from which it can be seen that after a very short period of rapid increase at the beginning, the RMSD of all systems tended to stable after 50 ns, with fluctuations of less than 0.1 nm. In addition, the rotational radius (Rg) and solvent-accessible surface area (SASA) can also confirm that the simulation reaches stability (Figure S2).
The use of C1 symmetry imidazole-carboxylate building block and auxiliary acetate co-ligand for assembly of a 2D wave-like zinc(II) coordination polymer: experimental and theoretical study
Published in Journal of Coordination Chemistry, 2020
Dana Bejan, Lucian Gabriel Bahrin, Corneliu Cojocaru, Alexandru Florentin Trandabat, Narcisa Laura Marangoci, Alexandru Rotaru, Sergiu Shova
In order to better understand the comparison between the obtained theoretical and experimental structural values, the DFT optimized structures were compared with X-ray crystal data in terms of root-mean-square-deviation (RMSD). This estimator (RMSD) is a measure of resemblance between observed crystal structure and predicted geometry of a molecule in terms of atomic positions. The smaller the RMSD value, the better is the match between the experimental (X-ray) structure and predicted (DFT) geometry. The explicit mathematical relationship employed to compute the RMSD estimator is given the in Supplementary Information (Equation S1). Comparison of the single-crystal structures and the optimized molecular geometries calculated by DFT method is shown in Figure 4a (HL) and Figure 4b ([ZnLAc]).
The performance of Dunning, Jensen, and Karlsruhe basis sets on computing relative energies and geometries
Published in Soft Materials, 2020
Karl N. Kirschner, Dirk Reith, Wolfgang Heiden
All QM calculations were done using Psi4 (v. 1.1a2.dev170). [39] Root-mean-squared deviations (RMSD) were computed using pytraj, a Python package that uses the cpptraj program.[40] Python3 [41,42] was used to execute pytraj, perform statistical analysis and generate plots using Matplotlib (v. 1.3.1) [43] and Seaborn (v. 0.7.1). [44] A total of 860 full optimizations and 80 single-point calculations were used in the analysis. All averages presented were computed using absolute values (i.e. ) and exclude the global minima (i.e. 0.000 kcalmol) for each molecular system. Data normalization was done using max-min feature scaling implemented in the Python scikit-learn library.[45]