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MM XRD: Free open-source: Java 3D applet or standalone program: Ovito: MM XRD EM MD: Free open-source: Python [13] [14] PyMOL: MM XRD SMI EM: Open-source [15] Python [16] [self-published source?] According to the author, almost 1/4 of all published images of 3D protein structures in the scientific literature were made via PyMOL. [citation ...
Interpretation of PAE values allows scientists to understand the level of confidence in the predicted structure of a protein: Lower PAE values between residue pairs from different domains indicate that the model predicts well-defined relative positions and orientations for those domains.
Reconstructing the 3D shape of the grains is nontrivial and three approaches are available to do so, respectively based on simple back-projection, forward projection, algebraic reconstruction technique and Monte Carlo method-based reconstruction. [10]
These scattering methods generally use monochromatic X-rays, which are restricted to a single wavelength with minor deviations. A broad spectrum of X-rays (that is, a blend of X-rays with different wavelengths) can also be used to carry out X-ray diffraction, a technique known as the Laue method.
X-ray diffraction computed tomography is an experimental technique that combines X-ray diffraction with the computed tomography data acquisition approach. X-ray diffraction (XRD) computed tomography (CT) was first introduced in 1987 by Harding et al. [1] using a laboratory diffractometer and a monochromatic X-ray pencil beam.
The method has also revealed the structure and function of many biological molecules, including vitamins, drugs, proteins and nucleic acids such as DNA. X-ray crystallography is still the primary method for characterizing the atomic structure of materials and in differentiating materials that appear similar in other experiments.
Graph-tool, a free Python module for manipulation and statistical analysis of graphs. NetworkX, an open source Python library for studying complex graphs. Tulip (software) is a free software in the domain of information visualisation capable of manipulating huge graphs (with more than 1.000.000 elements).
The Levenberg-Marquardt method finds a local optimum. Due to the curve having many interference fringes, it finds incorrect layer thicknesses unless the initial guess is extraordinarily good. The derivative-free simplex method also finds a local optimum. In order to find global optimum, global optimization algorithms such as simulated annealing ...