Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers th...
Methods in molecular biology (Clifton, N.J.)
35507266
Like an article narrative is deemed by an editor and referees to be worthy of being a version of record on acceptance as a publication, so must the underpinning data also be scrutinized before passing it as a version of record. Indeed without the und...
Journal of chemical information and modeling
36456533
We present a graph-convolutional neural network (GCNN)-based method for learning and prediction of statistical torsional profiles (STP) in small organic molecules based on the experimental X-ray structure data. A specialized GCNN torsion profile mode...
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...
Since chemistry, materials science, and crystallography deal with three-dimensional structures, they use mathematics such as geometry and symmetry. In recent years, the application of topology and mathematics to material design has yielded remarkable...
The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallogr...
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...
Acta crystallographica. Section A, Foundations and advances
38189437
Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This f...
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases w...