Publications by authors named "David S Goodsell"

129 Publications

Art as a tool for science.

Authors:
David S Goodsell

Nat Struct Mol Biol 2021 05;28(5):402-403

Department of Integrative Structural and Computational Biology, the Scripps Research Institute, La Jolla, CA, USA.

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http://dx.doi.org/10.1038/s41594-021-00587-5DOI Listing
May 2021

Seeing the PDB.

J Biol Chem 2021 Jan-Jun;296:100742. Epub 2021 May 4.

Department of Integrative and Computational Biology, The Scripps Research Institute, La Jolla, California, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, New Jersey, USA. Electronic address:

Ever since the first structures of proteins were determined in the 1960s, structural biologists have required methods to visualize biomolecular structures, both as an essential tool for their research and also to promote 3D comprehension of structural results by a wide audience of researchers, students, and the general public. In this review to celebrate the 50th anniversary of the Protein Data Bank, we present our own experiences in developing and applying methods of visualization and analysis to the ever-expanding archive of protein and nucleic acid structures in the worldwide Protein Data Bank. Across that timespan, Jane and David Richardson have concentrated on the organization inside and between the macromolecules, with ribbons to show the overall backbone "fold" and contact dots to show how the all-atom details fit together locally. David Goodsell has explored surface-based representations to present and explore biological subjects that range from molecules to cells. This review concludes with some ideas about the current challenges being addressed by the field of biomolecular visualization.
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http://dx.doi.org/10.1016/j.jbc.2021.100742DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8167287PMC
August 2021

Molecular storytelling for online structural biology outreach and education.

Struct Dyn 2021 Mar 5;8(2):020401. Epub 2021 Mar 5.

Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, USA.

Knowledge about the structure and function of biomolecules continues to grow exponentially, enabling us to "see" structural snapshots of biomolecular interactions and functional assemblies. At PDB-101, the educational portal of the RCSB Protein Data Bank, we have taken a storytelling approach to make this body of knowledge accessible and comprehensible to a wide community of students, educators, and the general public. For over 20 years, the Molecule of the Month series has utilized a traditional illustrated storytelling approach that is regularly adapted for classroom instruction. Similar visual and interactive storytelling approaches are used to present topical subjects at PDB-101 and full curricular materials and case studies for building a detailed narrative around topics of particular interest. This emphasis on storytelling led to the Video Challenge for High School students, now in its 8th year. In this Article, we will present some of the lessons we have learned for teaching and communicating structural biology using the PDB archive of biomolecular structures.
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http://dx.doi.org/10.1063/4.0000077DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7936881PMC
March 2021

Moltemplate: A Tool for Coarse-Grained Modeling of Complex Biological Matter and Soft Condensed Matter Physics.

J Mol Biol 2021 05 2;433(11):166841. Epub 2021 Feb 2.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA; RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ, USA. Electronic address:

Coarse-grained models have long been considered indispensable tools in the investigation of biomolecular dynamics and assembly. However, the process of simulating such models is arduous because unconventional force fields and particle attributes are often needed, and some systems are not in thermal equilibrium. Although modern molecular dynamics programs are highly adaptable, software designed for preparing all-atom simulations typically makes restrictive assumptions about the nature of the particles and the forces acting on them. Consequently, the use of coarse-grained models has remained challenging. Moltemplate is a file format for storing coarse-grained molecular models and the forces that act on them, as well as a program that converts moltemplate files into input files for LAMMPS, a popular molecular dynamics engine. Moltemplate has broad scope and an emphasis on generality. It accommodates new kinds of forces as they are developed for LAMMPS, making moltemplate a popular tool with thousands of users in computational chemistry, materials science, and structural biology. To demonstrate its wide functionality, we provide examples of using moltemplate to prepare simulations of fluids using many-body forces, coarse-grained organic semiconductors, and the motor-driven supercoiling and condensation of an entire bacterial chromosome.
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http://dx.doi.org/10.1016/j.jmb.2021.166841DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119336PMC
May 2021

Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic.

bioRxiv 2020 Dec 7. Epub 2020 Dec 7.

Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.
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http://dx.doi.org/10.1101/2020.12.01.406637DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7724657PMC
December 2020

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences.

Nucleic Acids Res 2021 01;49(D1):D437-D451

Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA.

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.
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http://dx.doi.org/10.1093/nar/gkaa1038DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779003PMC
January 2021

Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors.

Trends Pharmacol Sci 2020 12 2;41(12):1038-1049. Epub 2020 Nov 2.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA. Electronic address:

Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the designof new ones, expanding the toolbox for discovery and optimization of selectiveand effective covalent inhibitors. Commonly applied approaches are covalentdocking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes.
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http://dx.doi.org/10.1016/j.tips.2020.10.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7669701PMC
December 2020

Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models.

IEEE Trans Vis Comput Graph 2021 02 28;27(2):722-732. Epub 2021 Jan 28.

We present a new technique for the rapid modeling and construction of scientifically accurate mesoscale biological models. The resulting 3D models are based on a few 2D microscopy scans and the latest knowledge available about the biological entity, represented as a set of geometric relationships. Our new visual-programming technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we determine the statistical properties of various structural aspects, such as the outer membrane shape, the spatial properties, and the distribution characteristics of the macromolecular elements on the membrane. This information is utilized in the construction of the 3D model. Once all the imaging evidence is incorporated into the model, additional information can be incorporated by interactively defining the rules that spatially characterize the rest of the biological entity, such as mutual interactions among macromolecules, and their distances and orientations relative to other structures. These rules are defined through an intuitive 3D interactive visualization as a visual-programming feedback loop. We demonstrate the applicability of our approach on a use case of the modeling procedure of the SARS-CoV-2 virion ultrastructure. This atomistic model, which we present here, can steer biological research to new promising directions in our efforts to fight the spread of the virus.
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http://dx.doi.org/10.1109/TVCG.2020.3030415DOI Listing
February 2021

RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study.

Oncogene 2020 10 16;39(43):6623-6632. Epub 2020 Sep 16.

Research Collaboratory for Structural Bioinformatics Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.

Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.
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http://dx.doi.org/10.1038/s41388-020-01461-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581513PMC
October 2020

The AutoDock suite at 30.

Protein Sci 2021 01 12;30(1):31-43. Epub 2020 Sep 12.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.
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http://dx.doi.org/10.1002/pro.3934DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737764PMC
January 2021

Integrative illustration for coronavirus outreach.

PLoS Biol 2020 08 6;18(8):e3000815. Epub 2020 Aug 6.

RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America.

Two illustrations integrate current knowledge about severe acute respiratory syndrome (SARS) coronaviruses and their life cycle. They have been widely used in education and outreach through free distribution as part of a coronavirus-related resource at Protein Data Bank (PDB)-101, the education portal of the RCSB PDB. Scientific sources for creation of the illustrations and examples of dissemination and response are presented.
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http://dx.doi.org/10.1371/journal.pbio.3000815DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7433897PMC
August 2020

Insights from 20 years of the Molecule of the Month.

Biochem Mol Biol Educ 2020 07 17;48(4):350-355. Epub 2020 Jun 17.

Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.

For 20 years, Molecule of the Month articles have highlighted the functional stories of 3D structures found in the Protein Data Bank (PDB). The PDB is the primary archive of atomic structures of biological molecules, currently providing open access to more than 150,000 structures studied by researchers around the world. The wealth of knowledge embodied in this resource is remarkable, with structures that allow exploration of nearly any biomolecular topic, including the basic science of genetic mechanisms, mechanisms of photosynthesis and bioenergetics, and central biomedical topics like cancer therapy and the fight against infectious disease. The central motivation behind the Molecule of the Month is to provide a user-friendly introduction to this rich body of data, charting a path for users to get started with finding and exploring the many available structures. The Molecule of the Month and related materials are updated regularly at the education portal PDB-101 (http://pdb101.rcsb.org/), offering an ongoing resource for molecular biology educators and students around the world.
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http://dx.doi.org/10.1002/bmb.21360DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7496199PMC
July 2020

Art and Science of the Cellular Mesoscale.

Trends Biochem Sci 2020 06 21;45(6):472-483. Epub 2020 Mar 21.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.

Experimental information from microscopy, structural biology, and bioinformatics may be integrated to build structural models of entire cells with molecular detail. This integrative modeling is challenging in several ways: the intrinsic complexity of biology results in models with many closely packed and heterogeneous components; the wealth of available experimental data is scattered among multiple resources and must be gathered, reconciled, and curated; and computational infrastructure is only now gaining the capability of modeling and visualizing systems of this complexity. We present recent efforts to address these challenges, both with artistic approaches to depicting the cellular mesoscale, and development and application of methods to build quantitative models.
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http://dx.doi.org/10.1016/j.tibs.2020.02.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7230070PMC
June 2020

RCSB Protein Data Bank: Enabling biomedical research and drug discovery.

Protein Sci 2020 01 29;29(1):52-65. Epub 2019 Nov 29.

Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.

Analyses of publicly available structural data reveal interesting insights into the impact of the three-dimensional (3D) structures of protein targets important for discovery of new drugs (e.g., G-protein-coupled receptors, voltage-gated ion channels, ligand-gated ion channels, transporters, and E3 ubiquitin ligases). The Protein Data Bank (PDB) archive currently holds > 155,000 atomic-level 3D structures of biomolecules experimentally determined using crystallography, nuclear magnetic resonance spectroscopy, and electron microscopy. The PDB was established in 1971 as the first open-access, digital-data resource in biology, and is now managed by the Worldwide PDB partnership (wwPDB; wwPDB.org). US PDB operations are the responsibility of the Research Collaboratory for Structural Bioinformatics PDB (RCSB PDB). The RCSB PDB serves millions of RCSB.org users worldwide by delivering PDB data integrated with ∼40 external biodata resources, providing rich structural views of fundamental biology, biomedicine, and energy sciences. Recently published work showed that the PDB archival holdings facilitated discovery of ∼90% of the 210 new drugs approved by the US Food and Drug Administration 2010-2016. We review user-driven development of RCSB PDB services, examine growth of the PDB archive in terms of size and complexity, and present examples and opportunities for structure-guided drug discovery for challenging targets (e.g., integral membrane proteins).
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http://dx.doi.org/10.1002/pro.3730DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933845PMC
January 2020

Illustrate: Software for Biomolecular Illustration.

Structure 2019 11 10;27(11):1716-1720.e1. Epub 2019 Sep 10.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.

The small program Illustrate generates non-photorealistic images of biological molecules for use in dissemination, outreach, and education. The method has been used as part of the "Molecule of the Month," an ongoing educational column at the RCSB Protein Data Bank (http://rcsb.org). Insights from 20 years of application of the program are presented, and the program has been released both as open-source Fortran at GitHub and through an interactive web-based interface.
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http://dx.doi.org/10.1016/j.str.2019.08.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834899PMC
November 2019

Integrative modeling of the HIV-1 ribonucleoprotein complex.

PLoS Comput Biol 2019 06 13;15(6):e1007150. Epub 2019 Jun 13.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.

A coarse-grain computational method integrates biophysical and structural data to generate models of HIV-1 genomic RNA, nucleocapsid and integrase condensed into a mature ribonucleoprotein complex. Several hypotheses for the initial structure of the genomic RNA and oligomeric state of integrase are tested. In these models, integrase interaction captures features of the relative distribution of gRNA in the immature virion and increases the size of the RNP globule, and exclusion of nucleocapsid from regions with RNA secondary structure drives an asymmetric placement of the dimerized 5'UTR at the surface of the RNP globule.
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http://dx.doi.org/10.1371/journal.pcbi.1007150DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592547PMC
June 2019

Novel Intersubunit Interaction Critical for HIV-1 Core Assembly Defines a Potentially Targetable Inhibitor Binding Pocket.

mBio 2019 03 12;10(2). Epub 2019 Mar 12.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA

HIV-1 capsid protein (CA) plays critical roles in both early and late stages of the viral replication cycle. Mutagenesis and structural experiments have revealed that capsid core stability significantly affects uncoating and initiation of reverse transcription in host cells. This has led to efforts in developing antivirals targeting CA and its assembly, although none of the currently identified compounds are used in the clinic for treatment of HIV infection. A specific interaction that is primarily present in pentameric interfaces in the HIV-1 capsid core was identified and is reported to be important for CA assembly. This is shown by multidisciplinary characterization of CA site-directed mutants using biochemical analysis of virus-like particle formation, transmission electron microscopy of assembly, crystallographic studies, and molecular dynamic simulations. The data are consistent with a model where a hydrogen bond between CA residues E28 and K30' from neighboring N-terminal domains (CAs) is important for CA pentamer interactions during core assembly. This pentamer-preferred interaction forms part of an -terminal omain nterface (NDI) pocket that is amenable to antiviral targeting. Precise assembly and disassembly of the HIV-1 capsid core are key to the success of viral replication. The forces that govern capsid core formation and dissociation involve intricate interactions between pentamers and hexamers formed by HIV-1 CA. We identified one particular interaction between E28 of one CA and K30' of the adjacent CA that appears more frequently in pentamers than in hexamers and that is important for capsid assembly. Targeting the corresponding site could lead to the development of antivirals which disrupt this interaction and affect capsid assembly.
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http://dx.doi.org/10.1128/mBio.02858-18DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6414707PMC
March 2019

CellPAINT: Interactive Illustration of Dynamic Mesoscale Cellular Environments.

IEEE Comput Graph Appl 2018 Nov-Dec;38(6):51-66

CellPAINT allows nonexpert users to create interactive mesoscale illustrations that integrate a variety of biological data. Like popular digital painting software, scenes are created using a palette of molecular "brushes." The current release allows creation of animated scenes with an HIV virion, blood plasma, and a simplified T-cell.
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http://dx.doi.org/10.1109/MCG.2018.2877076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456043PMC
July 2019

RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy.

Nucleic Acids Res 2019 01;47(D1):D464-D474

Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, rcsb.org), the US data center for the global PDB archive, serves thousands of Data Depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without usage restrictions to more than 1 million rcsb.org Users worldwide and 600 000 pdb101.rcsb.org education-focused Users around the globe. PDB Data Depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy and 3D electron microscopy. PDB Data Consumers include researchers, educators and students studying Fundamental Biology, Biomedicine, Biotechnology and Energy. Recent reorganization of RCSB PDB activities into four integrated, interdependent services is described in detail, together with tools and resources added over the past 2 years to RCSB PDB web portals in support of a 'Structural View of Biology.'
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http://dx.doi.org/10.1093/nar/gky1004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324064PMC
January 2019

Labels on Levels: Labeling of Multi-Scale Multi-Instance and Crowded 3D Biological Environments.

IEEE Trans Vis Comput Graph 2019 Jan 9;25(1):977-986. Epub 2018 Dec 9.

Labeling is intrinsically important for exploring and understanding complex environments and models in a variety of domains. We present a method for interactive labeling of crowded 3D scenes containing very many instances of objects spanning multiple scales in size. In contrast to previous labeling methods, we target cases where many instances of dozens of types are present and where the hierarchical structure of the objects in the scene presents an opportunity to choose the most suitable level for each placed label. Our solution builds on and goes beyond labeling techniques in medical 3D visualization, cartography, and biological illustrations from books and prints. In contrast to these techniques, the main characteristics of our new technique are: 1) a novel way of labeling objects as part of a bigger structure when appropriate, 2) visual clutter reduction by labeling only representative instances for each type of an object, and a strategy of selecting those. The appropriate level of label is chosen by analyzing the scene's depth buffer and the scene objects' hierarchy tree. We address the topic of communicating the parent-children relationship between labels by employing visual hierarchy concepts adapted from graphic design. Selecting representative instances considers several criteria tailored to the character of the data and is combined with a greedy optimization approach. We demonstrate the usage of our method with models from mesoscale biology where these two characteristics-multi-scale and multi-instance-are abundant, along with the fact that these scenes are extraordinarily dense.
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http://dx.doi.org/10.1109/TVCG.2018.2864491DOI Listing
January 2019

From Atoms to Cells: Using Mesoscale Landscapes to Construct Visual Narratives.

J Mol Biol 2018 10 7;430(21):3954-3968. Epub 2018 Jun 7.

Center for BioMolecular Modeling, Milwaukee School of Engineering, Milwaukee, WI 53202, USA.

Modeling and visualization of the cellular mesoscale, bridging the nanometer scale of molecules to the micrometer scale of cells, is being studied by an integrative approach. Data from structural biology, proteomics, and microscopy are combined to simulate the molecular structure of living cells. These cellular landscapes are used as research tools for hypothesis generation and testing, and to present visual narratives of the cellular context of molecular biology for dissemination, education, and outreach.
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http://dx.doi.org/10.1016/j.jmb.2018.06.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186495PMC
October 2018

Molecular Illustration in Research and Education: Past, Present, and Future.

J Mol Biol 2018 10 9;430(21):3969-3981. Epub 2018 May 9.

Biomedical Communications, Department of Biology, University of Toronto, Mississauga, ON L5L 1C6, Canada.

Two-dimensional illustration is used extensively to study and disseminate the results of structural molecular biology. Molecular graphics methods have been and continue to be developed to address the growing needs of the structural biology community, and there are currently many effective, turn-key methods for displaying and exploring molecular structure. Building on decades of experience in design, best-practice resources are available to guide creation of illustrations that are effective for research and education communities.
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http://dx.doi.org/10.1016/j.jmb.2018.04.043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186494PMC
October 2018

Lattice Models of Bacterial Nucleoids.

J Phys Chem B 2018 05 25;122(21):5441-5447. Epub 2018 Jan 25.

Department of Integrative Structural and Computational Biology , The Scripps Research Institute , 10550 North Torrey Pines Road , La Jolla , California , United States.

Mesoscale molecular modeling is providing a new window into the inner workings of living cells. Modeling of genomes, however, remains a technical challenge, due to their large size and complexity. We describe a lattice method for rapid generation of bacterial nucleoid models that integrates experimental data from a variety of biophysical techniques and provides a starting point for simulation and hypothesis generation. The current method builds models of a circular bacterial genome with supercoiled plectonemes, packed within the small space of the bacterial cell. Lattice models are generated for Mycoplasma genitalium and Escherichia coli nucleoids, and used to simulate interaction data. The method is rapid enough to allow generation of multiple models when analyzing structure/function relationships, and we demonstrate use of the lattice models in creation of an all-atom representation of an entire cell.
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http://dx.doi.org/10.1021/acs.jpcb.7b11770DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5980677PMC
May 2018

A visual review of the human pathogen Streptococcus pneumoniae.

FEMS Microbiol Rev 2017 11;41(6):854-879

Aarhus Institute of Advanced Studies, Aarhus University, 8000 Aarhus, Denmark.

Being the principal causative agent of bacterial pneumonia, otitis media, meningitis and septicemia, the bacterium Streptococcus pneumoniae is a major global health problem. To highlight the molecular basis of this problem, we have portrayed essential biological processes of the pneumococcal life cycle in eight watercolor paintings. The paintings are done to a consistent nanometer scale based on currently available data from structural biology and proteomics. In this review article, the paintings are used to provide a visual review of protein synthesis, carbohydrate metabolism, cell wall synthesis, cell division, teichoic acid synthesis, virulence, transformation and pilus synthesis based on the available scientific literature within the field of pneumococcal biology. Visualization of the molecular details of these processes reveals several scientific questions about how molecular components of the pneumococcal cell are organized to allow biological function to take place. By the presentation of this visual review, we intend to stimulate scientific discussion, aid in the generation of scientific hypotheses and increase public awareness. A narrated video describing the biological processes in the context of a whole-cell illustration accompany this article.
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http://dx.doi.org/10.1093/femsre/fux037DOI Listing
November 2017

Instant Construction and Visualization of Crowded Biological Environments.

IEEE Trans Vis Comput Graph 2018 01 29;24(1):862-872. Epub 2017 Aug 29.

We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments distributed according to a Halton sequence. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV.
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http://dx.doi.org/10.1109/TVCG.2017.2744258DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5746312PMC
January 2018

The RCSB protein data bank: integrative view of protein, gene and 3D structural information.

Nucleic Acids Res 2017 01 27;45(D1):D271-D281. Epub 2016 Oct 27.

RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, http://rcsb.org), the US data center for the global PDB archive, makes PDB data freely available to all users, from structural biologists to computational biologists and beyond. New tools and resources have been added to the RCSB PDB web portal in support of a 'Structural View of Biology.' Recent developments have improved the User experience, including the high-speed NGL Viewer that provides 3D molecular visualization in any web browser, improved support for data file download and enhanced organization of website pages for query, reporting and individual structure exploration. Structure validation information is now visible for all archival entries. PDB data have been integrated with external biological resources, including chromosomal position within the human genome; protein modifications; and metabolic pathways. PDB-101 educational materials have been reorganized into a searchable website and expanded to include new features such as the Geis Digital Archive.
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http://dx.doi.org/10.1093/nar/gkw1000DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210513PMC
January 2017

Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives.

J Chem Inf Model 2016 08 25;56(8):1597-607. Epub 2016 Jul 25.

Integrative Structural and Computational Biology, The Scripps Research Institute , La Jolla, California 92037, United States.

We describe ADChemCast, a method for using results from virtual screening to create a richer representation of a target binding site, which may be used to improve ranking of compounds and characterize the determinants of ligand-receptor specificity. ADChemCast clusters docked conformations of ligands based on shared pairwise receptor-ligand interactions within chemically similar structural fragments, building a set of attributes characteristic of binders and nonbinders. Machine learning is then used to build rules from the most informational attributes for use in reranking of compounds. In this report, we use ADChemCast to improve the ranking of compounds in 11 diverse proteins from the Database of Useful Decoys-Enhanced (DUD-E) and demonstrate the utility of the method for characterizing relevant binding attributes in HIV reverse transcriptase.
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http://dx.doi.org/10.1021/acs.jcim.6b00248DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5023760PMC
August 2016

Winner of the 2016 Wellcome Image Awards.

Authors:
David S Goodsell

J Vis Commun Med 2016 Jan-Jun;39(1-2). Epub 2016 May 21.

a Ebola VirusImage courtesy of the Wellcome Trust.

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http://dx.doi.org/10.3109/17453054.2016.1171744DOI Listing
May 2016

Computational protein-ligand docking and virtual drug screening with the AutoDock suite.

Nat Protoc 2016 May 14;11(5):905-19. Epub 2016 Apr 14.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.

Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.
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http://dx.doi.org/10.1038/nprot.2016.051DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868550PMC
May 2016

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility.

PLoS Comput Biol 2015 Dec 2;11(12):e1004586. Epub 2015 Dec 2.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.

Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design. While modeling receptor flexibility is important for correctly predicting ligand binding, it still remains challenging. This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori. The challenges of this approach include the: 1) exponential growth of the search space, demanding more efficient search methods; and 2) increased number of false positives, calling for scoring functions tailored for flexible receptor docking. We present AutoDockFR-AutoDock for Flexible Receptors (ADFR), a new docking engine based on the AutoDock4 scoring function, which addresses the aforementioned challenges with a new Genetic Algorithm (GA) and customized scoring function. We validate ADFR using the Astex Diverse Set, demonstrating an increase in efficiency and reliability of its GA over the one implemented in AutoDock4. We demonstrate greatly increased success rates when cross-docking ligands into apo receptors that require side-chain conformational changes for ligand binding. These cross-docking experiments are based on two datasets: 1) SEQ17 -a receptor diversity set containing 17 pairs of apo-holo structures; and 2) CDK2 -a ligand diversity set composed of one CDK2 apo structure and 52 known bound inhibitors. We show that, when cross-docking ligands into the apo conformation of the receptors with up to 14 flexible side-chains, ADFR reports more correctly cross-docked ligands than AutoDock Vina on both datasets with solutions found for 70.6% vs. 35.3% systems on SEQ17, and 76.9% vs. 61.5% on CDK2. ADFR also outperforms AutoDock Vina in number of top ranking solutions on both datasets. Furthermore, we show that correctly docked CDK2 complexes re-create on average 79.8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the holo complexes. Finally, we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for AutoDockFR scales linearly when side-chain flexibility is added.
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http://dx.doi.org/10.1371/journal.pcbi.1004586DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4667975PMC
December 2015
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