Search

The advent of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), coupled with computational modeling, has enabled the creation of integrative 3D models of viruses, bacteria, and cellular organelles. These models, composed of thousands of macromolecules and billions of atoms, have historically posed significant challenges for manipulation and visualization without specialized molecular graphics tools and hardware. With the recent advancements in GPU rendering power and web browser capabilities, it is now feasible to render interactively large molecular scenes directly on the web. In this work, we introduce Mesoscale Explorer, a web application built using the Mol* framework, dedicated to the visualization of large-scale molecular models ranging from viruses to cell organelles. Mesoscale Explorer provides unprecedented access and insight into the molecular fabric of life, enhancing perception, streamlining exploration, and simplifying visualization of diverse data types, showcasing the intricate details of these models with unparalleled clarity., (© 2024 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

The advent of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), coupled with computational modeling, has enabled the creation of integrative 3D models of viruses, bacteria, and cellular organelles. These models, composed of thousands of macromolecules and billions of atoms, have historically posed significant challenges for manipulation and visualization without specialized molecular graphics tools and hardware. With the recent advancements in GPU rendering power and web browser capabilities, it is now feasible to render interactively large molecular scenes directly on the web. In this work, we introduce Mesoscale Explorer , a web application built using the Mol * framework, dedicated to the visualization of large-scale molecular models ranging from viruses to cell organelles. Mesoscale Explorer provides unprecedented access and insight into the molecular fabric of life, enhancing perception, streamlining exploration, and simplifying visualization of diverse data types, showcasing the intricate details of these models with unparalleled clarity.

With the ever-expanding toolkit of molecular viewers, the ability to visualize macromolecular structures has never been more accessible. Yet, the idiosyncratic technical intricacies across tools and the integration complexities associated with handling structure annotation data present significant barriers to seamless interoperability and steep learning curves for many users. The necessity for reproducible data visualizations is at the forefront of the current challenges. Recently, we introduced MolViewSpec (homepage: https://molstar.org/mol-view-spec/, GitHub project: https://github.com/molstar/mol-view-spec), a specification approach that defines molecular visualizations, decoupling them from the varying implementation details of different molecular viewers. Through the protocols presented herein, we demonstrate how to use MolViewSpec and its 3D view-building Python library for creating sophisticated, customized 3D views covering all standard molecular visualizations. MolViewSpec supports representations like cartoon and ball-and-stick with coloring, labeling, and applying complex transformations such as superposition to any macromolecular structure file in mmCIF, BinaryCIF, and PDB formats. These examples showcase progress towards reusability and interoperability of molecular 3D visualization in an era when handling molecular structures at scale is a timely and pressing matter in structural bioinformatics as well as research and education across the life sciences. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating a MolViewSpec view using the MolViewSpec Python package Basic Protocol 2: Creating a MolViewSpec view with reference to MolViewSpec annotation files Basic Protocol 3: Creating a MolViewSpec view with labels and other advanced features Support Protocol 1: Computing rotation and translation vectors Support Protocol 2: Creating a MolViewSpec annotation file., (© 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.)

ChannelsDB 2.0 is an updated database providing structural information about the position, geometry and physicochemical properties of protein channels-tunnels and pores-within deposited biomacromolecular structures from PDB and AlphaFoldDB databases. The newly deposited information originated from several sources. Firstly, we included data calculated using a popular CAVER tool to complement the data obtained using original MOLE tool for detection and analysis of protein tunnels and pores. Secondly, we added tunnels starting from cofactors within the AlphaFill database to enlarge the scope of the database to protein models based on Uniprot. This has enlarged available channel annotations ∼4.6 times as of 1 September 2023. The database stores information about geometrical features, e.g. length and radius, and physico-chemical properties based on channel-lining amino acids. The stored data are interlinked with the available UniProt mutation annotation data. ChannelsDB 2.0 provides an excellent resource for deep analysis of the role of biomacromolecular tunnels and pores. The database is available free of charge: https://channelsdb2.biodata.ceitec.cz., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Channels, tunnels, and pores serve as pathways for the transport of molecules and ions through protein structures, thus participating to their functions. MOLEonline ( https://mole.upol.cz ) is an interactive web-based tool with enhanced capabilities for detecting and characterizing channels, tunnels, and pores within protein structures. MOLEonline has two distinct calculation modes for analysis of channel and tunnels or transmembrane pores. This application gives researchers rich analytical insights into channel detection, structural characterization, and physicochemical properties. ChannelsDB 2.0 ( https://channelsdb2.biodata.ceitec.cz/ ) is a comprehensive database that offers information on the location, geometry, and physicochemical characteristics of tunnels and pores within macromolecular structures deposited in Protein Data Bank and AlphaFill databases. These tunnels are sourced from manual deposition from literature and automatic detection using software tools MOLE and CAVER. MOLEonline and ChannelsDB visualization is powered by the LiteMol Viewer and Mol* viewer, ensuring a user-friendly workspace. This chapter provides an overview of user applications and usage., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Summary: PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types, and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics., Availability and Implementation: PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images., (© The Author(s) 2023. Published by Oxford University Press.)

Segmentation helps interpret imaging data in a biological context. With the development of powerful tools for automated segmentation, public repositories for imaging data have added support for sharing and visualizing segmentations, creating the need for interactive web-based visualization of 3D volume segmentations. To address the ongoing challenge of integrating and visualizing multimodal data, we developed Mol* Volumes and Segmentations (Mol*VS), which enables the interactive, web-based visualization of cellular imaging data supported by macromolecular data and biological annotations. Mol*VS is fully integrated into Mol* Viewer, which is already used for visualization by several public repositories. All EMDB and EMPIAR entries with segmentation datasets are accessible via Mol*VS, which supports the visualization of data from a wide range of electron and light microscopy experiments. Additionally, users can run a local instance of Mol*VS to visualize and share custom datasets in generic or application-specific formats including volumes in .ccp4, .mrc, and .map, and segmentations in EMDB-SFF .hff, Amira .am, iMod .mod, and Segger .seg. Mol*VS is open source and freely available at https://molstarvolseg.ncbr.muni.cz/., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The archiving and dissemination of protein and nucleic acid structures as well as their structural, functional and biophysical annotations is an essential task that enables the broader scientific community to conduct impactful research in multiple fields of the life sciences. The Protein Data Bank in Europe (PDBe; pdbe.org) team develops and maintains several databases and web services to address this fundamental need. From data archiving as a member of the Worldwide PDB consortium (wwPDB; wwpdb.org), to the PDBe Knowledge Base (PDBe-KB; pdbekb.org), we provide data, data-access mechanisms, and visualizations that facilitate basic and applied research and education across the life sciences. Here, we provide an overview of the structural data and annotations that we integrate and make freely available. We describe the web services and data visualization tools we offer, and provide information on how to effectively use or even further develop them. Finally, we discuss the direction of our data services, and how we aim to tackle new challenges that arise from the recent, unprecedented advances in the field of structure determination and protein structure modeling., (© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)