AI Medical Compendium Journal:
Acta crystallographica. Section D, Structural biology

Showing 1 to 10 of 10 articles

Deep-learning map segmentation for protein X-ray crystallographic structure determination.

Acta crystallographica. Section D, Structural biology
When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification...

Deep residual networks for crystallography trained on synthetic data.

Acta crystallographica. Section D, Structural biology
The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and tra...

The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions.

Acta crystallographica. Section D, Structural biology
Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Å for both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Å both the procedu...

AlphaFold and the future of structural biology.

Acta crystallographica. Section D, 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.

Accelerating crystal structure determination with iterative AlphaFold prediction.

Acta crystallographica. Section D, Structural biology
Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented th...

20 years of crystal hits: progress and promise in ultrahigh-throughput crystallization screening.

Acta crystallographica. Section D, Structural biology
Diffraction-based structural methods contribute a large fraction of the biomolecular structural models available, providing a critical understanding of macromolecular architecture. These methods require crystallization of the target molecule, which r...

Using deep-learning predictions of inter-residue distances for model validation.

Acta crystallographica. Section D, Structural biology
Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the proce...

Sequence-assignment validation in cryo-EM models with checkMySequence.

Acta crystallographica. Section D, Structural biology
The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models w...

Predicting protein model correctness in Coot using machine learning.

Acta crystallographica. Section D, Structural biology
Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that ...

Sequence assignment for low-resolution modelling of protein crystal structures.

Acta crystallographica. Section D, Structural biology
The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for...