Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures.
David SehnalSebastian BittrichMandar S DeshpandeRadka SvobodováKarel BerkaVáclav BazgierSameer VelankarStephen K BurleyJaroslav KočaAlexander S RosePublished in: Nucleic acids research (2021)
Large biomolecular structures are being determined experimentally on a daily basis using established techniques such as crystallography and electron microscopy. In addition, emerging integrative or hybrid methods (I/HM) are producing structural models of huge macromolecular machines and assemblies, sometimes containing 100s of millions of non-hydrogen atoms. The performance requirements for visualization and analysis tools delivering these data are increasing rapidly. Significant progress in developing online, web-native three-dimensional (3D) visualization tools was previously accomplished with the introduction of the LiteMol suite and NGL Viewers. Thereafter, Mol* development was jointly initiated by PDBe and RCSB PDB to combine and build on the strengths of LiteMol (developed by PDBe) and NGL (developed by RCSB PDB). The web-native Mol* Viewer enables 3D visualization and streaming of macromolecular coordinate and experimental data, together with capabilities for displaying structure quality, functional, or biological context annotations. High-performance graphics and data management allows users to simultaneously visualise up to hundreds of (superimposed) protein structures, stream molecular dynamics simulation trajectories, render cell-level models, or display huge I/HM structures. It is the primary 3D structure viewer used by PDBe and RCSB PDB. It can be easily integrated into third-party services. Mol* Viewer is open source and freely available at https://molstar.org/.
Keyphrases
- electron microscopy
- molecular dynamics simulations
- high resolution
- electronic health record
- big data
- healthcare
- primary care
- depressive symptoms
- physical activity
- molecular docking
- stem cells
- health information
- machine learning
- quality improvement
- mesenchymal stem cells
- mass spectrometry
- deep learning
- amino acid
- artificial intelligence
- protein protein