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RMSD—another tutorial on how to calculate RMSD with example code; Secondary Structure Matching (SSM) — a tool for protein structure comparison. Uses RMSD. GDT, LCS and LGA — different structure comparison measures. Description and services. SuperPose — a protein superposition server. Uses RMSD. superpose — structural alignment based ...
The Kabsch algorithm, also known as the Kabsch-Umeyama algorithm, [1] named after Wolfgang Kabsch and Shinji Umeyama, is a method for calculating the optimal rotation matrix that minimizes the RMSD (root mean squared deviation) between two paired sets of points.
In experimental psychology, the RMSD is used to assess how well mathematical or computational models of behavior explain the empirically observed behavior. In GIS, the RMSD is one measure used to assess the accuracy of spatial analysis and remote sensing. In hydrogeology, RMSD and NRMSD are used to evaluate the calibration of a groundwater ...
The RMSD of two aligned structures indicates their divergence from one another. Structural alignment can be complicated by the existence of multiple protein domains within one or more of the input structures, because changes in relative orientation of the domains between two structures to be aligned can artificially inflate the RMSD.
The TM-score is intended as a more accurate measure of the global similarity of full-length protein structures than the often used RMSD measure. The TM-score indicates the similarity between two structures by a score between ( 0 , 1 ] {\displaystyle (0,1]} , where 1 indicates a perfect match between two structures (thus the higher the better ...
The Robotics Toolbox for Python is a reimplementation of the Robotics Toolbox for MATLAB for Python 3. [ 7 ] [ 8 ] Its functionality is a superset of the Robotics Toolbox for MATLAB, the programming model is similar, and it supports additional methods to define a serial link manipulator including URDF and elementary transform sequences.
Marching Square Matlab algorithm – An easy to understand open-source marching square algorithm. implementation in Java; Marching Squares code in Java. Given a 2D data set and thresholds, returns GeneralPath[] for easy plotting. Meandering Triangles explanation and sample Python implementation.
Once one has an algorithm for estimating the Gaussian function parameters, it is also important to know how precise those estimates are. Any least squares estimation algorithm can provide numerical estimates for the variance of each parameter (i.e., the variance of the estimated height, position, and width of the function).