Search Results for "structural"

Protenix is an open-source, trainable PyTorch reimplementation of AlphaFold 3, developed by ByteDance with the goal of democratizing high-accuracy protein structure prediction for computational biology and drug-discovery research. Protenix provides a complete pipeline for turning protein sequences (with optional MSA / sequence alignment) or structural inputs (e.g. PDB/CIF) into full 3D atomic-level structure predictions. It supports both “full” models and lightweight variants such as “Protenix-Mini,” offering a trade-off between speed/compute cost and predictive accuracy — making structure prediction accessible even in resource-constrained environments. The project also includes support for constraints (e.g., specifying residue- or atom-level contact constraints, or pocket constraints) to guide predictions toward biologically or experimentally relevant conformations, which enhances its utility for tasks like modeling complexes, ligands, or antibody–antigen interactions.

Understanding the anatomy of complex organs such as the human brain can be a challenge for students, so there is a definite advantage given to students who can experience the structures in a spatial way through real or virtual experiences. Since 2016, the Marquette Visualization Lab has been utilizing a dataset of 3 and 7 Tesla MRI scans from the Structural Informatics Group at University of Washington to visualize the structures of the brain in their Cave, utilizing the Unity game engine and 10 stereoscopic 3D projectors. Most MRI scans are presented as a volumetric rendering, but this dataset is unique because it was fully labelled and parcellated, allowing lab users to not only view the structures of the brain, but also virtually dissect it in a large scale, collaborative virtual reality environment. ...