Protein Data Bank Japan: Celebrating our 20th anniversary during a global pandemic as the Asian hub of three dimensional macromolecular structural data

Binding Mechanism between Platelet Glycoprotein and Cyclic Peptide Elucidated by McMD-Based Dynamic Docking

The cyclic peptide OS1 (amino acid sequence: CTERMALHNLC), which has a disulfide bond between both termini cysteine residues, inhibits complex formation between the platelet glycoprotein Ibα (GPIbα) and the von Willebrand factor (vWF) by forming a complex with GPIbα. To study the binding mechanism between GPIbα and OS1 and, therefore, the inhibition mechanism of the protein-protein GPIbα-vWF complex, we have applied our multicanonical molecular dynamics (McMD)-based dynamic docking protocol starting from the unbound state of the peptide. Our simulations have reproduced the experimental complex structure, although the top-ranking structure was an intermediary one, where the peptide was bound in the same location as in the experimental structure; however, the β-switch of GPIbα attained a different conformation. Our analysis showed that subsequent refolding of the β-switch results in a more stable binding configuration, although the transition to the native configuration appears to take some time, during which OS1 could dissociate. Our results show that conformational changes in the β-switch are crucial for successful binding of OS1. Furthermore, we identified several allosteric binding sites of GPIbα that might also interfere with vWF binding, and optimization of the peptide to target these allosteric sites might lead to a more effective inhibitor, as these are not dependent on the β-switch conformation.

Inherited CARD9 Deficiency Due to a Founder Effect in East Asia

Autosomal recessive CARD9 deficiency can underly deep and superficial fungal diseases. We identified two Japanese patients, suffering from superficial and invasive Candida albicans diseases, carrying biallelic variants of CARD9. Both patients, in addition to another Japanese and two Korean patients who were previously reported, carried the c.820dup CARD9 variant, either in the homozygous (two patients) or heterozygous (three patients) state. The other CARD9 alleles were c.104G > A, c.1534C > T and c.1558del. The c.820dup CARD9 variant has thus been reported, in the homozygous or heterozygous state, in patients originating from China, Japan, or South Korea. The Japanese, Korean, and Chinese patients share a 10 Kb haplotype encompassing the c.820dup CARD9 variant. This variant thus originates from a common ancestor, estimated to have lived less than 4,000 years ago. While phaeohyphomycosis caused by Phialophora spp. was common in the Chinese patients, none of the five patients in our study displayed Phialophora spp.-induced disease. This difference between Chinese and our patients probably results from environmental factors. (161/250).

Effect of Conformational Variability on the Drug Resistance of Candida auris ERG11p and FKS1

Candida auris infection has been recognized as an urgent threat to antifungal drug resistance, and the Eagle effect of C. auris FKS1 (1,3-β-d-glucan synthase) wild-type isolates has also been noted. The Eagle effect, namely, where higher concentrations of antifungals reduce fungicidal activity relative to lower concentrations, is a confounding factor of apparent antifungal resistance, but the detailed mechanism remains unclear. Here, we present the conformational variability of mutation sites for ERG11p (lanosterol 14α-demethylase) and FKS1 from deep neural network-based prediction along with the reported X-ray crystallographic and cryo-electron microscopy (cryo-EM) structures of antifungals. The sequence variability maps provide valuable insights into the inconsistent correlation between azole resistance and the mysterious Eagle effect with the dispersion of minimal inhibitory concentration (MIC) for echinocandin resistance. The conformational variability prediction supports the hypothesis that mutations K143R of clade I, VF125AL of clade III, and Y132F of clade IV for C. auris ERG11p make the corresponding site variable and that an increased population of invisible variable conformations potentially contributes to triazole resistance. In contrast, the predicted rigid conformation by the S639F mutation of hot spot region 1 (HS1) for FKS1 suggests that caspofungin (CAS) is involved in an uncompetitive inhibition, and a decreased population of the CAS-bound state of FKS1 with Rho1 leads to drug resistance. The predicted variable HS1 region for FKS1 WT isolates and the rigid one for FKS1 S639F mutants support the in vivo drug response and the in vitro MIC dispersion. A plausible mechanism of the Eagle effect is hereby proposed, namely, that a high concentration of CAS with a high membrane affinity reduces the population of the CAS-bound state of FKS1 with Rho1, as well as accompanying events such as aggregation or association depending on the conformational variability of HS1.

Computational analysis of bevacizumab binding with protein receptors for its potential anticancer activity

Breast cancer poses a significant global challenge, prompting researchers to explore novel approaches for potential treatments. In this study, we investigated the binding free energy (ΔG) of bevacizumab, an anti-cancer therapy targeting angiogenesis through the inhibition of vascular endothelial growth factor (VEGF), with various proto-oncogenes including CDK4, EGFR, frizzled, IGFR, OmoMYC, and KIT. Our in-silico investigation revealed that hydrogen bonding is pivotal in inducing conformational changes within the DNA structure, impeding its replication and preventing cell death. Molecular docking results revealed the presence of crucial hydrogen bonds and supported the formation of stable bevacizumab complexes. The molecular docking scores for the tested complexes were CDK4 (Score = -7.2 kcal/mol), EGFR (Score = -8.5 kcal/mol), frizzled (Score = -6.9 kcal/mol), IGFR (Score = -7.8 kcal/mol), KIT (Score = -6.5 kcal/mol), and MYC (Score = -8.3 kcal/mol). The binding mode demonstrated vital hydrogen bonds correlated with the observed energy gap. Notably, the calculated binding free energies of the tested compounds are as follows: CDK4 (ΔG = 24275.195 ± 6411.293 kJ/mol), EGFR (ΔG = 363273.625 ± 8731.466 kJ/mol), frizzled (ΔG = 181751.990 ± 28438.515 kJ/mol), IGFR (ΔG = 162414.725 ± 10728.367 kJ/mol), KIT (ΔG = 40162.585 ± 4331.017 kJ/mol), and MYC (ΔG = 434783.463 ± 53989.676 kJ/mol). Furthermore, through extensive 100 ns MD simulations, we observed the formation of a stable bevacizumab complex structure. The simulations confirmed the stability of the bevacizumab complex with the proto-oncogenes. The results of this study highlight the potential of bevacizumab complex as a promising candidate for anticancer treatment. The identification of hydrogen bonding, along with the calculated binding free energies and molecular docking scores, provides valuable insights into the molecular interactions and stability of the bevacizumab complexes. These findings and the extensive MD simulations open new avenues for future research and development of bevacizumab as a targeted therapy for breast cancer and other related malignancies.Communicated by Ramaswamy H. Sarma.

Binding Mechanism of Riboswitch to Natural Ligand Elucidated by McMD-Based Dynamic Docking Simulations

Flavin mononucleotide riboswitches are common among many pathogenic bacteria and are therefore considered to be an attractive target for antibiotics development. The riboswitch binds riboflavin (RBF, also known as vitamin B2), and although an experimental structure of their complex has been solved with the ligand bound deep inside the RNA molecule in a seemingly unreachable state, the binding mechanism between these molecules is not yet known. We have therefore used our Multicanonical Molecular Dynamics (McMD)-based dynamic docking protocol to analyze their binding mechanism by simulating the binding process between the riboswitch aptamer domain and the RBF, starting from the apo state of the riboswitch. Here, the refinement stage was crucial to identify the native binding configuration, as several other binding configurations were also found by McMD-based docking simulations. RBF initially binds the interface between P4 and P6 including U61 and G62, which forms a gateway where the ligand lingers until this gateway opens sufficiently to allow the ligand to pass through and slip into the hidden binding site including A48, A49, and A85.

EMDB-the Electron Microscopy Data Bank

The Electron Microscopy Data Bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide Protein Data Bank consortium (wwPDB; wwpdb.org) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30 000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.

Neuromodulators as Interdomain Signaling Molecules Capable of Occupying Effector Binding Sites in Bacterial Transcription Factors

Hormones and neurotransmitters are important components of inter-kingdom signaling systems that ensure the coexistence of eukaryotes with their microbial community. Their ability to affect bacterial physiology, metabolism, and gene expression was evidenced by various experimental approaches, but direct penetration into bacteria has only recently been reported. This opened the possibility of considering neuromodulators as potential effectors of bacterial ligand-dependent regulatory proteins. Here, we assessed the validity of this assumption for the neurotransmitters epinephrine, dopamine, and norepinephrine and two hormones (melatonin and serotonin). Using flexible molecular docking for transcription factors with ligand-dependent activity, we assessed the ability of neuromodulators to occupy their effector binding sites. For many transcription factors, including the global regulator of carbohydrate metabolism, CRP, and the key regulator of lactose assimilation, LacI, this ability was predicted based on the analysis of several 3D models. By occupying the ligand binding site, neuromodulators can sterically hinder the interaction of the target proteins with the natural effectors or even replace them. The data obtained suggest that the direct modulation of the activity of at least some bacterial transcriptional factors by neuromodulators is possible. Therefore, the natural hormonal background may be a factor that preadapts bacteria to the habitat through direct perception of host signaling molecules.

Making your raw data available to the macromolecular crystallography community

A recent editorial in the IUCr macromolecular crystallography journals [Helliwell et al. (2019), Acta Cryst. D75, 455-457] called for the implementation of the FAIR data principles. This implies that the authors of a paper that describes research on a macromolecular structure should make their raw diffraction data available. Authors are already used to submitting the derived data (coordinates) and the processed data (structure factors, merged or unmerged) to the PDB, but may still be uncomfortable with making the raw diffraction images available. In this paper, some guidelines and instructions on depositing raw data to Zenodo are given.

Elucidation of binding mechanism, affinity, and complex structure between mWT1 tumor-associated antigen peptide and HLA-A*24:02

We have applied our advanced computational and experimental methodologies to investigate the complex structure and binding mechanism of a modified Wilms' Tumor 1 (mWT1) protein epitope to the understudied Asian-dominant allele HLA-A*24:02 (HLA-A24) in aqueous solution. We have applied our developed multicanonical molecular dynamics (McMD)-based dynamic docking method to analyze the binding pathway and mechanism, which we verified by comparing the highest probability structures from simulation with our experimentally solved x-ray crystal structure. Subsequent path sampling MD simulations elucidated the atomic details of the binding process and indicated that first an encounter complex is formed between the N-terminal's positive charge of the 9-residue mWT1 fragment peptide and a cluster of negative residues on the surface of HLA-A24, with the major histocompatibility complex (MHC) molecule preferring a predominantly closed conformation. The peptide first binds to this closed MHC conformation, forming an encounter complex, after which the binding site opens due to increased entropy of the binding site, allowing the peptide to bind to form the native complex structure. Further sequence and structure analyses also suggest that although the peptide loading complex would help with stabilizing the MHC molecule, the binding depends in a large part on the intrinsic affinity between the MHC molecule and the antigen peptide. Finally, our computational tools and analyses can be of great benefit to study the binding mechanism of different MHC types to their antigens, where it could also be useful in the development of higher affinity variant peptides and for personalized medicine.

AREA-AFFINITY: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities

Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.

Idiotope-Driven T-Cell/B-Cell Collaboration-Based T-Cell Epitope Prediction Using B-Cell Receptor Repertoire Sequences in Infectious Diseases

T-cell recognition of antigen epitopes is a crucial step for the induction of adaptive immune responses, and the identification of such T-cell epitopes is, therefore, important for understanding diverse immune responses and controlling T-cell immunity. A number of bioinformatic tools exist that predict T-cell epitopes; however, many of these methods highly rely on evaluating conventional peptide presentation by major histocompatibility complex (MHC) molecules, but they ignore epitope sequences recognized by T-cell receptor (TCR). Immunogenic determinant idiotopes are present on the variable regions of immunoglobulin molecules expressed on and secreted by B-cells. In idiotope-driven T-cell/B-cell collaboration, B-cells present the idiotopes on MHC molecules for recognition by idiotope-specific T-cells. According to the idiotype network theory formulated by Niels Jerne, such idiotopes found on anti-idiotypic antibodies exhibit molecular mimicry of antigens. Here, by combining these concepts and defining the patterns of TCR-recognized epitope motifs (TREMs), we developed a T-cell epitope prediction method that identifies T-cell epitopes derived from antigen proteins by analyzing B-cell receptor (BCR) sequences. This method allowed us to identify T-cell epitopes that contain the same TREM patterns between BCR and viral antigen sequences in two different infectious diseases caused by dengue virus and SARS-CoV-2 infection. The identified epitopes were among the T-cell epitopes detected in previous studies, and T-cell stimulatory immunogenicity was confirmed. Thus, our data support this method as a powerful tool for the discovery of T-cell epitopes from BCR sequences.

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.

Mutual induced-fit mechanism drives binding between intrinsically disordered Bim and cryptic binding site of Bcl-xL

The intrinsically disordered region (IDR) of Bim binds to the flexible cryptic site of Bcl-xL, a pro-survival protein involved in cancer progression that plays an important role in initiating apoptosis. However, their binding mechanism has not yet been elucidated. We have applied our dynamic docking protocol, which correctly reproduced both the IDR properties of Bim and the native bound configuration, as well as suggesting other stable/meta-stable binding configurations and revealed the binding pathway. Although the cryptic site of Bcl-xL is predominantly in a closed conformation, initial binding of Bim in an encounter configuration leads to mutual induced-fit binding, where both molecules adapt to each other; Bcl-xL transitions to an open state as Bim folds from a disordered to an α-helical conformation while the two molecules bind each other. Finally, our data provides new avenues to develop novel drugs by targeting newly discovered stable conformations of Bcl-xL.

Effect of Conformational Variability on Seasonable Thermal Stability and Cell Entry of Omicron Variants

The Omicron BA.1 variant of SARS-CoV-2 preferentially infects through the cathepsin-mediated endocytic pathway, but the mechanism of cell entry has not been solved yet because BA.4/5 is more fusogenic and more efficiently spread in human lung cells than BA.2. It has been unclear why the Omicron spike is inefficiently cleaved in virions compared with Delta, and how the relatively effective reproduction proceeds without the cell entry through plasma membrane fusion. Conformational variability from deep neural network-based prediction correlates well with the thermodynamic stability of variants. The difference of seasonable pandemic variants in summer and those in winter is distinguishable by this conformational stability, and the geographical optimization of variants is also traceable. Further, the predicted conformational variability maps rationalize the less efficient S1/S2 cleavage of Omicron variants and provide a valuable insight into the cell entry through the endocytic pathway. It is concluded that conformational variability prediction is able to complement transformation information on motifs in protein structures for drug discovery.

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.

History of Protein Data Bank Japan: standing at the beginning of the age of structural genomics

Prof. Haruki Nakamura, who is the former head of Protein Data Bank Japan (PDBj) and an expert in computational biology, retired from Osaka University at the end of March 2018. He founded PDBj at the Institute for Protein Research, together with other faculty members, researchers, engineers, and annotators in 2000, and subsequently established the worldwide Protein Data Bank (wwPDB) in 2003 to manage the core archive of the Protein Data Bank (PDB), collaborating with RCSB-PDB in the USA and PDBe in Europe. As the former head of PDBj and also an expert in structural bioinformatics, he has grown PDBj to become a well-known data center within the structural biology community and developed several related databases, tools and integrated with new technologies, such as the semantic web, as primary services offered by PDBj.

Biological Magnetic Resonance Data Bank

The Biological Magnetic Resonance Data Bank (BMRB, https://bmrb.io) is the international open data repository for biomolecular nuclear magnetic resonance (NMR) data. Comprised of both empirical and derived data, BMRB has applications in the study of biomacromolecular structure and dynamics, biomolecular interactions, drug discovery, intrinsically disordered proteins, natural products, biomarkers, and metabolomics. Advances including GHz-class NMR instruments, national and trans-national NMR cyberinfrastructure, hybrid structural biology methods and machine learning are driving increases in the amount, type, and applications of NMR data in the biosciences. BMRB is a Core Archive and member of the World-wide Protein Data Bank (wwPDB).

Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future

As a discipline, structural biology has been transformed by the three-dimensional electron microscopy (3DEM) "Resolution Revolution" made possible by convergence of robust cryo-preservation of vitrified biological materials, sample handling systems, and measurement stages operating a liquid nitrogen temperature, improvements in electron optics that preserve phase information at the atomic level, direct electron detectors (DEDs), high-speed computing with graphics processing units, and rapid advances in data acquisition and processing software. 3DEM structure information (atomic coordinates and related metadata) are archived in the open-access Protein Data Bank (PDB), which currently holds more than 11,000 3DEM structures of proteins and nucleic acids, and their complexes with one another and small-molecule ligands (~ 6% of the archive). Underlying experimental data (3DEM density maps and related metadata) are stored in the Electron Microscopy Data Bank (EMDB), which currently holds more than 21,000 3DEM density maps. After describing the history of the PDB and the Worldwide Protein Data Bank (wwPDB) partnership, which jointly manages both the PDB and EMDB archives, this review examines the origins of the resolution revolution and analyzes its impact on structural biology viewed through the lens of PDB holdings. Six areas of focus exemplifying the impact of 3DEM across the biosciences are discussed in detail (icosahedral viruses, ribosomes, integral membrane proteins, SARS-CoV-2 spike proteins, cryogenic electron tomography, and integrative structure determination combining 3DEM with complementary biophysical measurement techniques), followed by a review of 3DEM structure validation by the wwPDB that underscores the importance of community engagement.

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.

Advancing the field of computational drug design using multicanonical molecular dynamics-based dynamic docking

Multicanonical molecular dynamics (McMD)-based dynamic docking is a powerful tool to not only predict the native binding configuration between two flexible molecules, but it can also be used to accurately simulate the binding/unbinding pathway. Furthermore, it can also predict alternative binding sites, including allosteric ones, by employing an exhaustive sampling approach. Since McMD-based dynamic docking accurately samples binding/unbinding events, it can thus be used to determine the molecular mechanism of binding between two molecules. We developed the McMD-based dynamic docking methodology based on the powerful, but woefully underutilized McMD algorithm, combined with a toolset to perform the docking and to analyze the results. Here, we showcase three of our recent works, where we have applied McMD-based dynamic docking to advance the field of computational drug design. In the first case, we applied our method to perform an exhaustive search between Hsp90 and one of its inhibitors to successfully predict the native binding configuration in its binding site, as we refined our analysis methods. For our second case, we performed an exhaustive search of two medium-sized ligands and Bcl-xL, which has a cryptic binding site that differs greatly between the apo and holo structures. Finally, we performed a dynamic docking simulation between a membrane-embedded GPCR molecule and a high affinity ligand that binds deep within its receptor's pocket. These advanced simulations showcase the power that the McMD-based dynamic docking method has, and provide a glimpse of the potential our methodology has to unravel and solve the medical and biophysical issues in the modern world.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01010-z.

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.

PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology

PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.

Computational study of the impact of nucleotide variations on highly conserved proteins: In the case of actin

Sequencing of individual human genomes enables studying relationship among nucleotide variations, amino acid substitutions, effect on protein structures and diseases. Many studies have found general tendencies, for instance, that pathogenic variations tend to be found in the buried regions of the protein structures, that benign variations tend to be found on the surface of the proteins, and that variations on evolutionary conserved residues tend to be pathogenic. These tendencies were deduced from globular proteins with standard evolutionary changes in amino acid sequences. In this study, we investigated the variation distribution on actin, one of the highly conserved proteins. Many nucleotide variations and three-dimensional structures of actin have been registered in databases. By combining those data, we found that variations buried inside the protein were rather benign and variations on the surface of the protein were pathogenic. This idiosyncratic distribution of the variation impact is likely ascribed to the extensive use of the surface of the protein for protein-protein interactions in actin.

N-Terminal-Driven Binding Mechanism of an Antigen Peptide to Human Leukocyte Antigen-A*2402 Elucidated by Multicanonical Molecular Dynamic-Based Dynamic Docking and Path Sampling Simulations

We have applied our advanced multicanonical molecular dynamics (McMD)-based dynamic docking methodology to investigate the binding mechanism of an HIV-1 Nef protein epitope to the Asian-dominant allele human leukocyte antigen (HLA)-A*2402. Even though pMHC complex formation [between a Major histocompatibility complex (MHC) class I molecule, which is encoded by an HLA allele, and an antigen peptide] is one of the fundamental processes of the adaptive human immune response, its binding mechanism has not yet been well studied, partially due to the high allelic variation of HLAs in the population. We have used our developed McMD-based dynamic docking method and have successfully reproduced the native complex structure, which is located near the free energy global minimum. Subsequent path sampling MD simulations elucidated the atomic details of the binding process and indicated that the peptide binding is initially driven by the highly positively charged N-terminus of the peptide that is attracted to the various negatively charged residues on the MHC molecule's surface. Upon nearing the pocket, the second tyrosine residue of the peptide anchors the peptide by strongly binding to the B-site of the MHC molecule via hydrophobic driven interactions, resulting in a very strong bound complex structure. Our methodology can be effectively used to predict the bound complex structures between MHC molecules and their antigens to study their binding mechanism in close detail, which would help with the development of new vaccines against cancers, as well as viral infections such as HIV and COVID-19.

Fifty years of Protein Data Bank in the Journal of Biochemistry

Protein Data Bank (PDB), jointly founded in 1971 by Brookhaven National Laboratory, USA, and the Cambridge Crystallographic Data Centre, UK, is the single global archive of experimentally determined biological macromolecular structures. PDB deposition is mandatory for publication in most scientific journals, which means ‘no PDB deposition, no structural publication’. The current PDB archive contains more than 180,000 entries and includes many structures from Asian institutions. The first protein structure from Japan was that of cytochrome c determined by Prof Masao Kakudo’s group at the Institute for Protein Research, Osaka University, in 1971 at a resolution of 4 Å, and a subsequent atomic structure at 2.3 Å resolution was deposited to PDB in 1976 as the 1st Asian and 21st entry of the entire PDB archive. Since then, 317 protein structures whose primary citation was the Journal of Biochemistry (J. Biochem.) have been deposited to PDB. Based on this long history between PDB and J. Biochem., a statistical analysis of all structural reports in J. Biochem. has been carried out using the relational database system at PDBj (https://pdbj.org) and reviewed the yearly distribution, resolution, quality of structure, type of target protein, number of citations and comparison against other major journals.