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Articles by Randy Read
AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network
Abstract Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, key challenges persist in modeling side-chain packing, condition-dependent conformational changes and biomolecular interactions, largely because of limited high-quality training data.
Xtricorder: A likelihood-enhanced self-rotation function and application to a machine-learning enhanced Matthews prediction of asymmetric unit copy number
Abstract Abstract Analysis of crystallographic diffraction data before phasing gives the crystallographer a "first look" at the nature of the problem and the context in which the structure determination will be performed. We here report the development of Xtricorder, an application that targets analysis of crystallographic data specifically for likelihood-based phasing.
CryoET of β-amyloid and tau within postmortem Alzheimer’s disease brain - Nature
Abstract A defining pathological feature of most neurodegenerative diseases is the assembly of proteins into amyloid that form disease-specific structures1. In Alzheimer’s disease, this is characterized by the deposition of β-amyloid and tau with disease-specific conformations. The in situ structure of amyloid in the human brain is unknown.
Likelihood-based interactive local docking into cryo-EM maps in ChimeraX
Abstract The interpretation of cryo-EM maps often includes the docking of known or predicted structures of the components, which is particularly useful when the map resolution is worse than 4 Angstrom. Although it can be effective to search the entire map to find the best placement of a component, the process can be slow when the maps are large. However, frequently there is a well-founded hypothesis about where particular components are located.
Welcoming five new Co‐editors
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AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination - Nature Methods
Abstract Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps.
AlphaFold and the future of structural biology
This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.
Improved AlphaFold modeling with implicit experimental information - Nature Methods
Abstract Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy.
Improvement of Cryo-Em Maps by Density Modification
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Improvement of cryo-EM maps by density modification
Abstract A density-modification procedure for improving maps from single-particle electron cryogenic microscopy (cryo-EM) is presented. The theoretical basis of the method is identical to that of maximum-likelihood density modification, previously used to improve maps from macromolecular X-ray crystallography.
The Continuing Evolution of Telematics Technology - Fleet Management Weekly
First in a Series on the Benefits and How to Select, Implement and Optimize By Randy Read, President, EnVue Telematics Editor’s note: Randy Read, with his partner Susan Johnson, founded EnVue Telematics in 2011. Randy has been involved in the commercial fleet industry for more than 25 years, the last 12 working in the rapidly growing telematics technology business.
Structural basis for the specificity of renin-mediated angiotensinogen cleavage
Yahui Yan1, Aiwu Zhou2, Robin W. Carrell3 and Randy J.
Soon, Our Robot Coffee Baristas Will Only Brew Certain Brands
It’s historically operated on a razor blade model: Its Keurig business makes real money not by selling machine brewers but by selling K-Cups. Now cheaper competitors have moved in. They sell inexpensive one-off cups and reusable, extensible cups—threatening the company’s business on both sides. K-Cups and Keurig (the best-known brand used to brew them) are both manufactured by Green Mountain Coffee.
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