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.

Cancer Institute of New Jersey Rutgers The State University of New Jersey New Brunswick New Jersey

Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway New Jersey

National Centre for Biomolecular Research Faculty of Science Masaryk University Brno Czech Republic

Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Hinxton Cambridge United Kingdom

Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway New Jersey

Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla California

Zobrazit více v PubMed

Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, Wang J, Cong Q, Kinch LN, Schaeffer RD, Millán C, Park H, Adams C, Glassman CR, DeGiovanni A, Pereira JH, Rodrigues AV, van Dijk AA, Ebrecht AC, … Baker D (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373(6557), 871–876. 10.1126/science.abj8754 PubMed DOI PMC

Bordin N, Dallago C, Heinzinger M, Kim S, Littmann M, Rauer C, Steinegger M, Rost B, & Orengo C (2023). Novel machine learning approaches revolutionize protein knowledge. Trends in Biochemical Sciences, 48(4), 345–359. 10.1016/j.tibs.2022.11.001 PubMed DOI PMC

Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan S, Ghosh S, Goodsell DS, Green RK, Guranovic V, … Zardecki C (2023). RCSB Protein Data Bank (RCSB.org): Delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Research, 51(D1), D488–D508. 10.1093/nar/gkac1077 PubMed DOI PMC

Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan SJ, Ghosh S, Goodsell DS, Green RK, Guranovic V, … Zardecki C (2022). RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D. Protein Science, 31(12), e4482. 10.1002/pro.4482 PubMed DOI PMC

Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan SJ, Goodsell DS, Ghosh S, Kramer Green R, Guranovic V, Henry J, Hudson BP, Lawson CL, … Zardecki C (2022). RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Protein Science, 31(1), 187–208. 10.1002/pro.4213 PubMed DOI PMC

Crystallography: Protein Data Bank. (1971). Nature New Biology, 233(42), 223–223. 10.1038/newbio233223b0 PubMed DOI

Danneskiold-Samsøe NB, Kavi D, Jude KM, Nissen SB, Wat LW, Coassolo L, Zhao M, Santana-Oikawa GA, Broido BB, Garcia KC, & Svensson KJ (2023). Rapid and accurate deorphanization of ligand-receptor pairs using AlphaFold. bioRxiv, 2023.03.16.531341. 10.1101/2023.03.16.531341 DOI

Evans JD, Bon V, Senkovska I, & Kaskel S (2021). A universal standard archive file for adsorption data. Langmuir, 37(14), 4222–4226. 10.1021/acs.langmuir.1c00122 PubMed DOI

Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, … Hassabis D (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. 10.1038/s41586-021-03819-2 PubMed DOI PMC

Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, Dos Santos Costa A, Fazel-Zarandi M, Sercu T, Candido S, & Rives A (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637), 1123–1130. 10.1126/science.ade2574 PubMed DOI

Meng EC, Goddard TD, Pettersen EF, Couch GS, Pearson ZJ, Morris JH, & Ferrin TE (2023). UCSF ChimeraX: Tools for structure building and analysis. Protein Science, 32(11), e4792. 10.1002/pro.4792 PubMed DOI PMC

Mosalaganti S, Obarska-Kosinska A, Siggel M, Taniguchi R, Turoňová B, Zimmerli CE, Buczak K, Schmidt FH, Margiotta E, Mackmull M-T, Hagen WJH, Hummer G, Kosinski J, & Beck M (2022). AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science, 376(6598), eabm9506. 10.1126/science.abm9506 PubMed DOI

Pettersen EF, Goddard TD, Huang CC, Meng EC, Couch GS, Croll TI, Morris JH, & Ferrin TE (2021). UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Science, 30(1), 70–82. 10.1002/pro.3943 PubMed DOI PMC

Procter JB, Carstairs GM, Soares B, Mourão K, Ofoegbu TC, Barton D, Lui L, Menard A, Sherstnev N, Roldan-Martinez D, Duce S, Martin DMA, & Barton GJ (2021). Alignment of biological sequences with Jalview. Methods in Molecular Biology, 2231, 203–224. 10.1007/978-1-0716-1036-7_13 PubMed DOI PMC

Rosignoli S, & Paiardini A (2022). Boosting the full potential of PyMOL with structural biology plugins.Biomolecules,12(12),1764.10.3390/biom12121764 PubMed DOI PMC

Rossi Sebastiano M, Ermondi G, Hadano S, & Caron G (2022). AI-based protein structure databases have the potential to accelerate rare diseases research: AlphaFoldDB and the case of IAHSP/Alsin. Drug Discovery Today, 27(6), 1652–1660. 10.1016/j.drudis.2021.12.018 PubMed DOI

Schwede T, Sali A, Honig B, Levitt M, Berman HM, Jones D, Brenner SE, Burley SK, Das R, Dokholyan NV, Dunbrack RL, Fidelis K, Fiser A, Godzik A, Huang YJ, Humblet C, Jacobson MP, Joachimiak A, Krystek SR, … Wilson IA (2009). Outcome of a workshop on applications of protein models in biomedical research. Structure, 17(2), 151–159. 10.1016/j.str.2008.12.014 PubMed DOI PMC

Sehnal D, Bittrich S, Deshpande M, Svobodová R, Berka K, Bazgier V, Velankar S, Burley SK, Koča J, & Rose AS (2021). Mol* Viewer: Modern web app for 3D visualization and analysis of large biomolecular structures. Nucleic Acids Research, 49(W1), W431–W437. 10.1093/nar/gkab314 PubMed DOI PMC

Sehnal D, Bittrich S, Velankar S, Koča J, Svobodová R, Burley SK, & Rose AS (2020). BinaryCIF and CIFTools-Lightweight, efficient and extensible macromolecular data management. PLoS Computational Biology, 16(10), e1008247. 10.1371/journal.pcbi.1008247 PubMed DOI PMC

Varadi M, Anyango S, Appasamy SD, Armstrong D, Bage M, Berrisford J, Choudhary P, Bertoni D, Deshpande M, Leines GD, Ellaway J, Evans G, Gaborova R, Gupta D, Gutmanas A, Harrus D, Kleywegt GJ, Bueno WM, Nadzirin N, … Velankar S (2022). PDBe and PDBe-KB: Providing high-quality, up-to-date and integrated resources of macromolecular structures to support basic and applied research and education. Protein Science, 31(10), e4439. 10.1002/pro.4439 PubMed DOI PMC

Varadi M, Bertoni D, Magana P, Paramval U, Pidruchna I, Radhakrishnan M, Tsenkov M, Nair S, Mirdita M, Yeo J, Kovalevskiy O, Tunyasuvunakool K, Laydon A, Žídek A, Tomlinson H, Hariharan D, Abrahamson J, Green T, Jumper J, … Velankar S (2024). AlphaFold Protein Structure Database in 2024: Providing structure coverage for over 214 million protein sequences. Nucleic Acids Research, 52(D1), D368–D375. 10.1093/nar/gkad1011 PubMed DOI PMC

Varadi M, Bordin N, Orengo C, & Velankar S (2023). The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors. Current Opinion in Structural Biology, 79, 102543. 10.1016/j.sbi.2023.102543 PubMed DOI

Velankar S, Burley SK, Kurisu G, Hoch JC, & Markley JL (2021). The Protein Data Bank archive. Methods in Molecular Biology, 2305, 3–21. 10.1007/978-1-0716-1406-8_1 PubMed DOI

wwPDB consortium. (2019). Protein Data Bank: The single global archive for 3D macromolecular structure data. Nucleic Acids Research, 47(D1), D520–D528. 10.1093/nar/gky949 PubMed DOI PMC

Visualizing and analyzing 3D biomolecular structures using Mol* at RCSB.org: Influenza A H5N1 virus proteome case study

Mesoscale explorer: Visual exploration of large-scale molecular models

Mesoscale Explorer - Visual Exploration of Large-Scale Molecular Models