Impact of the Protein Data Bank on antineoplastic approvals

An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits

Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials.

Impact of structural biology and the protein data bank on us fda new drug approvals of low molecular weight antineoplastic agents 2019-2023

Open access to three-dimensional atomic-level biostructure information from the Protein Data Bank (PDB) facilitated discovery/development of 100% of the 34 new low molecular weight, protein-targeted, antineoplastic agents approved by the US FDA 2019-2023. Analyses of PDB holdings, the scientific literature, and related documents for each drug-target combination revealed that the impact of structural biologists and public-domain 3D biostructure data was broad and substantial, ranging from understanding target biology (100% of all drug targets), to identifying a given target as likely druggable (100% of all targets), to structure-guided drug discovery (>80% of all new small-molecule drugs, made up of 50% confirmed and >30% probable cases). In addition to aggregate impact assessments, illustrative case studies are presented for six first-in-class small-molecule anti-cancer drugs, including a selective inhibitor of nuclear export targeting Exportin 1 (selinexor, Xpovio), an ATP-competitive CSF-1R receptor tyrosine kinase inhibitor (pexidartinib,Turalia), a non-ATP-competitive inhibitor of the BCR-Abl fusion protein targeting the myristoyl binding pocket within the kinase catalytic domain of Abl (asciminib, Scemblix), a covalently-acting G12C KRAS inhibitor (sotorasib, Lumakras or Lumykras), an EZH2 methyltransferase inhibitor (tazemostat, Tazverik), and an agent targeting the basic-Helix-Loop-Helix transcription factor HIF-2α (belzutifan, Welireg).

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery

BACKGROUND

The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery.

OBJECTIVE

Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity.

METHODS

We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space.

RESULTS

The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces.

CONCLUSION

The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

Mutations in asparaginase II from E. coli and implications for inactivation and PEGylation

All clinically-used asparaginases convert L-asparagine (L-Asn) to l-aspartate (L-Asp) and l-glutamine (L-Gln) to L-glutamate (L-Glu), which has been useful in reducing bioavailable asparagine and glutamine in patients under treatment for acute lymphoblastic leukemia. The E. coli type 2 L-asparaginase (EcA2) can present different sequences among varying bacterial strains, which we hypothesized that might affect their biological function, stability and interchangeability. Here we report the analysis of two EcA2 provided by the public health system of a middle-income country. These enzymes were reported to have similar specific activity in vitro, whereas they differ in vivo. Protein sequencing by LC-MS-MS and peptide mapping by MALDI-ToF-MS of their tryptic digests revealed that Aginasa™ share similar sequence to EcA2 from E. coli strain BL21(DE3), while Leuginase™ has sequence equivalent to EcA2 from E. coli strain AS1.357. The two amino acid differences between Aginasa™ (64D and 252 T) and Leuginase™ (64 N and 252S) resulted in structural divergences in solution as accessed by small-angle X-ray scattering and molecular dynamics simulation trajectories. The conformational variability further results in dissimilar surface accessibility with major consequences for PEGylation, as well as different susceptibility to degradation by limited proteolysis. The present results reveal that the sequence variations between these two EcA2 variants results in conformational changes associated with differential conformational plasticity, potentially affecting physico-chemical and biological properties, including proteolytic and immunogenic silent inactivation.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

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

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.

RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D

Now in its 52nd year of continuous operations, the Protein Data Bank (PDB) is the premiere open-access global archive housing three-dimensional (3D) biomolecular structure data. It is jointly managed by the Worldwide Protein Data Bank (wwPDB) partnership. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is funded by the National Science Foundation, National Institutes of Health, and US Department of Energy and serves as the US data center for the wwPDB. RCSB PDB is also responsible for the security of PDB data in its role as wwPDB-designated Archive Keeper. Every year, RCSB PDB serves tens of thousands of depositors of 3D macromolecular structure data (coming from macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction). The RCSB PDB research-focused web portal (RCSB.org) makes PDB data available at no charge and without usage restrictions to many millions of PDB data consumers around the world. The RCSB PDB training, outreach, and education web portal (PDB101.RCSB.org) serves nearly 700 K educators, students, and members of the public worldwide. This invited Tools Issue contribution describes how RCSB PDB (i) is organized; (ii) works with wwPDB partners to process new depositions; (iii) serves as the wwPDB-designated Archive Keeper; (iv) enables exploration and 3D visualization of PDB data via RCSB.org; and (v) supports training, outreach, and education via PDB101.RCSB.org. New tools and features at RCSB.org are presented using examples drawn from high-resolution structural studies of proteins relevant to treatment of human cancers by targeting immune checkpoints.

Assessing PDB macromolecular crystal structure confidence at the individual amino acid residue level

Approximately 87% of the more than 190,000 atomic-level three-dimensional (3D) biostructures in the PDB were determined using macromolecular crystallography (MX). Agreement between 3D atomic coordinates and experimental data for >100 million individual amino acid residues occurring within ∼150,000 PDB MX structures was analyzed in detail. The real-space correlation coefficient (RSCC) calculated using the 3D atomic coordinates for each residue and experimental-data-derived electron density enables outlier detection of unreliable atomic coordinates (particularly important for poorly resolved side-chain atoms) and ready evaluation of local structure quality by PDB users. For human protein MX structures in PDB, comparisons of the per-residue RSCC metric with AlphaFold2-computed structure model confidence (pLDDT-predicted local distance difference test) document (1) that RSCC values and pLDDT scores are correlated (median correlation coefficient ∼0.41), and (2) that experimentally determined MX structures (3.5 Å resolution or better) are more reliable than AlphaFold2-computed structure models and should be used preferentially whenever possible.

Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.

Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first 6 months of the COVID-19 pandemic

Understanding the molecular evolution of the SARS-CoV-2 virus as it continues to spread in communities around the globe is important for mitigation and future pandemic preparedness. Three-dimensional structures of SARS-CoV-2 proteins and those of other coronavirusess archived in the Protein Data Bank were used to analyze viral proteome evolution during the first 6 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 isolates revealed how each one of 29 viral proteins have undergone amino acid changes. Catalytic residues in active sites and binding residues in protein-protein interfaces showed modest, but significant, numbers of substitutions, highlighting the mutational robustness of the viral proteome. 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 potential drug discovery targets and the four structural proteins that comprise the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and protein-protein and protein-nucleic acid 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.

Simplified quality assessment for small-molecule ligands in the Protein Data Bank

More than 70% of the experimentally determined macromolecular structures in the Protein Data Bank (PDB) contain small-molecule ligands. Quality indicators of ∼643,000 ligands present in ∼106,000 PDB X-ray crystal structures have been analyzed. Ligand quality varies greatly with regard to goodness of fit between ligand structure and experimental data, deviations in bond lengths and angles from known chemical structures, and inappropriate interatomic clashes between the ligand and its surroundings. Based on principal component analysis, correlated quality indicators of ligand structure have been aggregated into two largely orthogonal composite indicators measuring goodness of fit to experimental data and deviation from ideal chemical structure. Ranking of the composite quality indicators across the PDB archive enabled construction of uniformly distributed composite ranking score. This score is implemented at RCSB.org to compare chemically identical ligands in distinct PDB structures with easy-to-interpret two-dimensional ligand quality plots, allowing PDB users to quickly assess ligand structure quality and select the best exemplars.

RCSB Protein Data Bank resources for structure-facilitated design of mRNA vaccines for existing and emerging viral pathogens

Structural biologists provide direct insights into the molecular bases of human health and disease. The open-access Protein Data Bank (PDB) stores and delivers three-dimensional (3D) biostructure data that facilitate discovery and development of therapeutic agents and diagnostic tools. We are in the midst of a revolution in vaccinology. Non-infectious mRNA vaccines have been proven during the coronavirus disease 2019 (COVID-19) pandemic. This new technology underpins nimble discovery and clinical development platforms that use knowledge of 3D viral protein structures for societal benefit. The RCSB PDB supports vaccine designers through expert biocuration and rigorous validation of 3D structures; open-access dissemination of structure information; and search, visualization, and analysis tools for structure-guided design efforts. This resource article examines the structural biology underpinning the success of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) mRNA vaccines and enumerates some of the many protein structures in the PDB archive that could guide design of new countermeasures against existing and emerging viral pathogens.

RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.

PDB-101: Educational resources supporting molecular explorations through biology and medicine

The Protein Data Bank (PDB) archive is a rich source of information in the form of atomic-level three-dimensional (3D) structures of biomolecules experimentally determined using macromolecular crystallography, nuclear magnetic resonance (NMR) spectroscopy, and electron microscopy (3DEM). Originally established in 1971 as a resource for protein crystallographers to freely exchange data, today PDB data drive research and education across scientific disciplines. In 2011, the online portal PDB-101 was launched to support teachers, students, and the general public in PDB archive exploration (pdb101.rcsb.org). Maintained by the Research Collaboratory for Structural Bioinformatics PDB, PDB-101 aims to help train the next generation of PDB users and to promote the overall importance of structural biology and protein science to nonexperts. Regularly published features include the highly popular Molecule of the Month series, 3D model activities, molecular animation videos, and educational curricula. Materials are organized into various categories (Health and Disease, Molecules of Life, Biotech and Nanotech, and Structures and Structure Determination) and searchable by keyword. A biennial health focus frames new resource creation and provides topics for annual video challenges for high school students. Web analytics document that PDB-101 materials relating to fundamental topics (e.g., hemoglobin, catalase) are highly accessed year-on-year. In addition, PDB-101 materials created in response to topical health matters (e.g., Zika, measles, coronavirus) are well received. PDB-101 shows how learning about the diverse shapes and functions of PDB structures promotes understanding of all aspects of biology, from the central dogma of biology to health and disease to biological energy.

Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules' characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank's resources will increase substantially in the coming years.

Modernized uniform representation of carbohydrate molecules in the Protein Data Bank

Since 1971, the Protein Data Bank (PDB) has served as the single global archive for experimentally determined 3D structures of biological macromolecules made freely available to the global community according to the FAIR principles of Findability-Accessibility-Interoperability-Reusability. During the first 50 years of continuous PDB operations, standards for data representation have evolved to better represent rich and complex biological phenomena. Carbohydrate molecules present in more than 14,000 PDB structures have recently been reviewed and remediated to conform to a new standardized format. This machine-readable data representation for carbohydrates occurring in the PDB structures and the corresponding reference data improves the findability, accessibility, interoperability and reusability of structural information pertaining to these molecules. The PDB Exchange MacroMolecular Crystallographic Information File data dictionary now supports (i) standardized atom nomenclature that conforms to International Union of Pure and Applied Chemistry-International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) recommendations for carbohydrates, (ii) uniform representation of branched entities for oligosaccharides, (iii) commonly used linear descriptors of carbohydrates developed by the glycoscience community and (iv) annotation of glycosylation sites in proteins. For the first time, carbohydrates in PDB structures are consistently represented as collections of standardized monosaccharides, which precisely describe oligosaccharide structures and enable improved carbohydrate visualization, structure validation, robust quantitative and qualitative analyses, search for dendritic structures and classification. The uniform representation of carbohydrate molecules in the PDB described herein will facilitate broader usage of the resource by the glycoscience community and researchers studying glycoproteins.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Targeting protein phosphatase PP2A for cancer therapy: development of allosteric pharmaceutical agents

Tumor initiation is driven by oncogenes that activate signaling networks for cell proliferation and survival involving protein phosphorylation. Protein kinases in these pathways have proven to be effective targets for pharmaceutical inhibitors that have progressed to the clinic to treat various cancers. Here, we offer a narrative about the development of small molecule modulators of the protein Ser/Thr phosphatase 2A (PP2A) to reduce the activation of cell proliferation and survival pathways. These novel drugs promote the assembly of select heterotrimeric forms of PP2A that act to limit cell proliferation. We discuss the potential for the near-term translation of this approach to the clinic for cancer and other human diseases.

Open-access data: A cornerstone for artificial intelligence approaches to protein structure prediction

The Protein Data Bank (PDB) was established in 1971 to archive three-dimensional (3D) structures of biological macromolecules as a public good. Fifty years later, the PDB is providing millions of data consumers around the world with open access to more than 175,000 experimentally determined structures of proteins and nucleic acids (DNA, RNA) and their complexes with one another and small-molecule ligands. PDB data users are working, teaching, and learning in fundamental biology, biomedicine, bioengineering, biotechnology, and energy sciences. They also represent the fields of agriculture, chemistry, physics and materials science, mathematics, statistics, computer science, and zoology, and even the social sciences. The enormous wealth of 3D structure data stored in the PDB has underpinned significant advances in our understanding of protein architecture, culminating in recent breakthroughs in protein structure prediction accelerated by artificial intelligence approaches and deep or machine learning methods.

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

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.

Impact of structural biologists and the Protein Data Bank on small-molecule drug discovery and development

The Protein Data Bank (PDB) is an international core data resource central to fundamental biology, biomedicine, bioenergy, and biotechnology/bioengineering. Now celebrating its 50th anniversary, the PDB houses >175,000 experimentally determined atomic structures of proteins, nucleic acids, and their complexes with one another and small molecules and drugs. The importance of three-dimensional (3D) biostructure information for research and education obtains from the intimate link between molecular form and function evident throughout biology. Among the most prolific consumers of PDB data are biomedical researchers, who rely on the open access resource as the authoritative source of well-validated, expertly curated biostructures. This review recounts how the PDB grew from just seven protein structures to contain more than 49,000 structures of human proteins that have proven critical for understanding their roles in human health and disease. It then describes how these structures are used in academe and industry to validate drug targets, assess target druggability, characterize how tool compounds and other small-molecules bind to drug targets, guide medicinal chemistry optimization of binding affinity and selectivity, and overcome challenges during preclinical drug development. Three case studies drawn from oncology exemplify how structural biologists and open access to PDB structures impacted recent regulatory approvals of antineoplastic drugs.

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

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 versus 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.

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

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.