Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies

Studying protein dynamics and conformational heterogeneity is crucial for understanding biomolecular systems and treating disease. Despite the deposition of over 215 000 macromolecular structures in the Protein Data Bank and the advent of AI-based structure prediction tools such as AlphaFold2, RoseTTAFold, and ESMFold, static representations are typically produced, which fail to fully capture macromolecular motion. Here, we discuss the importance of integrating experimental structures with computational clustering to explore the conformational landscapes that manifest protein function. We describe the method developed by the Protein Data Bank in Europe - Knowledge Base to identify distinct conformational states, demonstrate the resource's primary use cases, through examples, and discuss the need for further efforts to annotate protein conformations with functional information. Such initiatives will be crucial in unlocking the potential of protein dynamics data, expediting drug discovery research, and deepening our understanding of macromolecular mechanisms.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2023

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the latest developments in the services provided by EMBL-EBI data resources to scientific communities globally. These developments aim to ensure EMBL-EBI resources meet the current and future needs of these scientific communities, accelerating the impact of open biological data for all.

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.

PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank

While the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragments, and cross-references to small molecule databases using UniChem. The toolkit also provides methods for identifying all the covalently attached chemical components in a macromolecular structure and calculating similarity among small molecules. By providing a broad range of functionality, PDBe CCDUtils caters to the needs of researchers in cheminformatics, structural biology, bioinformatics and computational chemistry.

Annotating Macromolecular Complexes in the Protein Data Bank: Improving the FAIRness of Structure Data

Macromolecular complexes are essential functional units in nearly all cellular processes, and their atomic-level understanding is critical for elucidating and modulating molecular mechanisms. The Protein Data Bank (PDB) serves as the global repository for experimentally determined structures of macromolecules. Structural data in the PDB offer valuable insights into the dynamics, conformation, and functional states of biological assemblies. However, the current annotation practices lack standardised naming conventions for assemblies in the PDB, complicating the identification of instances representing the same assembly. In this study, we introduce a method leveraging resources external to PDB, such as the Complex Portal, UniProt and Gene Ontology, to describe assemblies and contextualise them within their biological settings accurately. Employing the proposed approach, we assigned standard names to over 90% of unique assemblies in the PDB and provided persistent identifiers for each assembly. This standardisation of assembly data enhances the PDB, facilitating a deeper understanding of macromolecular complexes. Furthermore, the data standardisation improves the PDB's FAIR attributes, fostering more effective basic and translational research and scientific education.

PDBImages: a command-line tool for automated macromolecular structure visualization

SUMMARY

PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types, and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics.

AVAILABILITY AND IMPLEMENTATION

PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images.

Challenges in bridging the gap between protein structure prediction and functional interpretation

The rapid evolution of protein structure prediction tools has significantly broadened access to protein structural data. Although predicted structure models have the potential to accelerate and impact fundamental and translational research significantly, it is essential to note that they are not validated and cannot be considered the ground truth. Thus, challenges persist, particularly in capturing protein dynamics, predicting multi-chain structures, interpreting protein function, and assessing model quality. Interdisciplinary collaborations are crucial to overcoming these obstacles. Databases like the AlphaFold Protein Structure Database, the ESM Metagenomic Atlas, and initiatives like the 3D-Beacons Network provide FAIR access to these data, enabling their interpretation and application across a broader scientific community. Whilst substantial advancements have been made in protein structure prediction, further progress is required to address the remaining challenges. Developing training materials, nurturing collaborations, and ensuring open data sharing will be paramount in this pursuit. The continued evolution of these tools and methodologies will deepen our understanding of protein function and accelerate disease pathogenesis and drug development discoveries.

Clustering predicted structures at the scale of the known protein universe

Proteins are key to all cellular processes and their structure is important in understanding their function and evolution. Sequence-based predictions of protein structures have increased in accuracy1, and over 214 million predicted structures are available in the AlphaFold database2. However, studying protein structures at this scale requires highly efficient methods. Here, we developed a structural-alignment-based clustering algorithm-Foldseek cluster-that can cluster hundreds of millions of structures. Using this method, we have clustered all of the structures in the AlphaFold database, identifying 2.30 million non-singleton structural clusters, of which 31% lack annotations representing probable previously undescribed structures. Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AlphaFold database. Evolutionary analysis suggests that most clusters are ancient in origin but 4% seem to be species specific, representing lower-quality predictions or examples of de novo gene birth. We also show how structural comparisons can be used to predict domain families and their relationships, identifying examples of remote structural similarity. On the basis of these analyses, we identify several examples of human immune-related proteins with putative remote homology in prokaryotic species, illustrating the value of this resource for studying protein function and evolution across the tree of life.

The impact of AlphaFold Protein Structure Database on the fields of life sciences

Arguably, 2020 was the year of high-accuracy protein structure predictions, with AlphaFold 2.0 achieving previously unseen accuracy in the Critical Assessment of Protein Structure Prediction (CASP). In 2021, DeepMind and EMBL-EBI developed the AlphaFold Protein Structure Database to make an unprecedented number of reliable protein structure predictions easily accessible to the broad scientific community. We provide a brief overview and describe the latest developments in the AlphaFold database. We highlight how the fields of data services, bioinformatics, structural biology, and drug discovery are directly affected by the influx of protein structure data. We also show examples of cutting-edge research that took advantage of the AlphaFold database. It is apparent that connections between various fields through protein structures are now possible, but the amount of data poses new challenges. Finally, we give an outlook regarding the future direction of the database, both in terms of data sets and new functionalities.

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.

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.

ModelCIF: An Extension of PDBx/mmCIF Data Representation for Computed Structure Models

ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery of Findable, Accessible, Interoperable, and Reusable (FAIR) data to users worldwide. ModelCIF describes the specific set of attributes and metadata associated with macromolecular structures modeled by solely computational methods and provides an extensible data representation for deposition, archiving, and public dissemination of predicted three-dimensional (3D) models of macromolecules. It is an extension of the Protein Data Bank Exchange / macromolecular Crystallographic Information Framework (PDBx/mmCIF), which is the global data standard for representing experimentally-determined 3D structures of macromolecules and associated metadata. The PDBx/mmCIF framework and its extensions (e.g., ModelCIF) are managed by the Worldwide Protein Data Bank partnership (wwPDB, wwpdb.org) in collaboration with relevant community stakeholders such as the wwPDB ModelCIF Working Group (wwpdb.org/task/modelcif). This semantically rich and extensible data framework for representing computed structure models (CSMs) accelerates the pace of scientific discovery. Herein, we describe the architecture, contents, and governance of ModelCIF, and tools and processes for maintaining and extending the data standard. Community tools and software libraries that support ModelCIF are also described.

3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modeling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank.

PDBe and PDBe-KB: Providing high-quality, up-to-date and integrated resources of macromolecular structures to support basic and applied research and education

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

Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology

The number of unique transmembrane (TM) protein structures doubled in the last four years, which can be attributed to the revolution of cryo-electron microscopy. In addition, AlphaFold2 (AF2) also provided a large number of predicted structures with high quality. However, if a specific protein family is the subject of a study, collecting the structures of the family members is highly challenging in spite of existing general and protein domain-specific databases. Here, we demonstrate this and assess the applicability and usability of automatic collection and presentation of protein structures via the ABC protein superfamily. Our pipeline identifies and classifies transmembrane ABC protein structures using the PFAM search and also aims to determine their conformational states based on special geometric measures, conftors. Since the AlphaFold database contains structure predictions only for single polypeptide chains, we performed AF2-Multimer predictions for human ABC half transporters functioning as dimers. Our AF2 predictions warn of possibly ambiguous interpretation of some biochemical data regarding interaction partners and call for further experiments and experimental structure determination. We made our predicted ABC protein structures available through a web application, and we joined the 3D-Beacons Network to reach the broader scientific community through platforms such as PDBe-KB.

PDBe-KB: collaboratively defining the biological context of structural data

The Protein Data Bank in Europe - Knowledge Base (PDBe-KB, https://pdbe-kb.org) is an open collaboration between world-leading specialist data resources contributing functional and biophysical annotations derived from or relevant to the Protein Data Bank (PDB). The goal of PDBe-KB is to place macromolecular structure data in their biological context by developing standardised data exchange formats and integrating functional annotations from the contributing partner resources into a knowledge graph that can provide valuable biological insights. Since we described PDBe-KB in 2019, there have been significant improvements in the variety of available annotation data sets and user functionality. Here, we provide an overview of the consortium, highlighting the addition of annotations such as predicted covalent binders, phosphorylation sites, effects of mutations on the protein structure and energetic local frustration. In addition, we describe a library of reusable web-based visualisation components and introduce new features such as a bulk download data service and a novel superposition service that generates clusters of superposed protein chains weekly for the whole PDB archive.

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.

The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an extensive, public database of highly accurate protein structure models. The models are the products of AlphaFold2, an Artificial Intelligence algorithm developed by DeepMind. AlphaFold enabled scientists to investigate an unprecedented number of protein structures. The database we describe here provides access to these predicted models and information on their accuracy. The first version of AlphaFold DB contains over 360,000 models of 21 biologically essential species.

PDBeCIF: an open-source mmCIF/CIF parsing and processing package

Background

Biomacromolecular structural data outgrew the legacy Protein Data Bank (PDB) format which the scientific community relied on for decades, yet the use of its successor PDBx/Macromolecular Crystallographic Information File format (PDBx/mmCIF) is still not widespread. Perhaps one of the reasons is the availability of easy to use tools that only support the legacy format, but also the inherent difficulties of processing mmCIF files correctly, given the number of edge cases that make efficient parsing problematic. Nevertheless, to fully exploit macromolecular structure data and their associated annotations such as multiscale structures from integrative/hybrid methods or large macromolecular complexes determined using traditional methods, it is necessary to fully adopt the new format as soon as possible.

Results

To this end, we developed PDBeCIF, an open-source Python project for manipulating mmCIF and CIF files. It is part of the official list of mmCIF parsers recorded by the wwPDB and is heavily employed in the processes of the Protein Data Bank in Europe. The package is freely available both from the PyPI repository (http://pypi.org/project/pdbecif) and from GitHub (https://github.com/pdbeurope/pdbecif) along with rich documentation and many ready-to-use examples.

Conclusions

PDBeCIF is an efficient and lightweight Python 2.6+/3+ package with no external dependencies. It can be readily integrated with 3rd party libraries as well as adopted for broad scientific analyses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04271-9.

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies

SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins

The Protein Ensemble Database (PED) (https://proteinensemble.org), which holds structural ensembles of intrinsically disordered proteins (IDPs), has been significantly updated and upgraded since its last release in 2016. The new version, PED 4.0, has been completely redesigned and reimplemented with cutting-edge technology and now holds about six times more data (162 versus 24 entries and 242 versus 60 structural ensembles) and a broader representation of state of the art ensemble generation methods than the previous version. The database has a completely renewed graphical interface with an interactive feature viewer for region-based annotations, and provides a series of descriptors of the qualitative and quantitative properties of the ensembles. High quality of the data is guaranteed by a new submission process, which combines both automatic and manual evaluation steps. A team of biocurators integrate structured metadata describing the ensemble generation methodology, experimental constraints and conditions. A new search engine allows the user to build advanced queries and search all entry fields including cross-references to IDP-related resources such as DisProt, MobiDB, BMRB and SASBDB. We expect that the renewed PED will be useful for researchers interested in the atomic-level understanding of IDP function, and promote the rational, structure-based design of IDP-targeting drugs.

Interaction between the scaffold proteins CBP by IQGAP1 provides an interface between gene expression and cytoskeletal activity

Crosstalk between cellular pathways is often mediated through scaffold proteins that function as platforms for the assembly of signaling complexes. Based on yeast two-hybrid analysis, we report here the interaction between two complex scaffold proteins, CREB-binding protein (CBP) and the Ras GTPase-activating-like protein 1 (IQGAP1). Dissection of the interaction between the two proteins reveals that the central, thus far uncharacterized, region of IQGAP1 interacts with the HAT domain and the C-terminal intrinsically disordered region of CBP (termed ID5). Structural analysis of ID5 by solution NMR spectroscopy and SAXS reveals the presence of two regions with pronounced helical propensity. The ID5 region(s) involved in the interaction of nanomolar affinity were delineated by solution NMR titrations and pull-down assays. Moreover, we found that IQGAP1 acts as an inhibitor of the histone acetyltransferase (HAT) activity of CBP. In in vitro assays, the CBP-binding region of IQGAP1 positively and negatively regulates the function of HAT proteins of different families including CBP, KAT5 and PCAF. As many signaling pathways converge on CBP and IQGAP1, their interaction provides an interface between transcription regulation and the coordination of cytoskeleton. Disruption or alteration of the interaction between these scaffold proteins may lead to cancer development or metastatic processes, highlighting the importance of this interaction.

PDBe-KB: a community-driven resource for structural and functional annotations

The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages-the PDBe-KB aggregated views of structure data-which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession.

PDBe: improved findability of macromolecular structure data in the PDB

The Protein Data Bank in Europe (PDBe), a founding member of the Worldwide Protein Data Bank (wwPDB), actively participates in the deposition, curation, validation, archiving and dissemination of macromolecular structure data. PDBe supports diverse research communities in their use of macromolecular structures by enriching the PDB data and by providing advanced tools and services for effective data access, visualization and analysis. This paper details the enrichment of data at PDBe, including mapping of RNA structures to Rfam, and identification of molecules that act as cofactors. PDBe has developed an advanced search facility with ∼100 data categories and sequence searches. New features have been included in the LiteMol viewer at PDBe, with updated visualization of carbohydrates and nucleic acids. Small molecules are now mapped more extensively to external databases and their visual representation has been enhanced. These advances help users to more easily find and interpret macromolecular structure data in order to solve scientific problems.

AmyPro: a database of proteins with validated amyloidogenic regions

Soluble functional proteins may transform into insoluble amyloid fibrils that deposit in a variety of tissues. Amyloid formation is a hallmark of age-related degenerative disorders. Perhaps surprisingly, amyloid fibrils can also be beneficial and are frequently exploited for diverse functional roles in organisms. Here we introduce AmyPro, an open-access database providing a comprehensive, carefully curated collection of validated amyloid fibril-forming proteins from all kingdoms of life classified into broad functional categories (http://amypro.net). In particular, AmyPro provides the boundaries of experimentally validated amyloidogenic sequence regions, short descriptions of the functional relevance of the proteins and their amyloid state, a list of the experimental techniques applied to study the amyloid state, important structural/functional/variation/mutation data transferred from UniProt, a list of relevant PDB structures categorized according to protein states, database cross-references and literature references. AmyPro greatly improves on similar currently available resources by incorporating both prions and functional amyloids in addition to pathogenic amyloids, and allows users to screen their sequences against the entire collection of validated amyloidogenic sequence fragments. By enabling further elucidation of the sequential determinants of amyloid fibril formation, we hope AmyPro will enhance the development of new methods for the precise prediction of amyloidogenic regions within proteins.

Protein Data Bank: the single global archive for 3D macromolecular structure data

The Protein Data Bank (PDB) is the single global archive of experimentally determined three-dimensional (3D) structure data of biological macromolecules. Since 2003, the PDB has been managed by the Worldwide Protein Data Bank (wwPDB; wwpdb.org), an international consortium that collaboratively oversees deposition, validation, biocuration, and open access dissemination of 3D macromolecular structure data. The PDB Core Archive houses 3D atomic coordinates of more than 144 000 structural models of proteins, DNA/RNA, and their complexes with metals and small molecules and related experimental data and metadata. Structure and experimental data/metadata are also stored in the PDB Core Archive using the readily extensible wwPDB PDBx/mmCIF master data format, which will continue to evolve as data/metadata from new experimental techniques and structure determination methods are incorporated by the wwPDB. Impacts of the recently developed universal wwPDB OneDep deposition/validation/biocuration system and various methods-specific wwPDB Validation Task Forces on improving the quality of structures and data housed in the PDB Core Archive are described together with current challenges and future plans.

PDBe: towards reusable data delivery infrastructure at protein data bank in Europe

The Protein Data Bank in Europe (PDBe, pdbe.org) is actively engaged in the deposition, annotation, remediation, enrichment and dissemination of macromolecular structure data. This paper describes new developments and improvements at PDBe addressing three challenging areas: data enrichment, data dissemination and functional reusability. New features of the PDBe Web site are discussed, including a context dependent menu providing links to raw experimental data and improved presentation of structures solved by hybrid methods. The paper also summarizes the features of the LiteMol suite, which is a set of services enabling fast and interactive 3D visualization of structures, with associated experimental maps, annotations and quality assessment information. We introduce a library of Web components which can be easily reused to port data and functionality available at PDBe to other services. We also introduce updates to the SIFTS resource which maps PDB data to other bioinformatics resources, and the PDBe REST API.

Affinity purification of human m-calpain through an intrinsically disordered inhibitor, calpastatin

Calpains are calcium-activated proteases that have biomedical and biotechnological potential. Their activity is tightly regulated by their endogenous inhibitor, calpastatin that binds to the enzyme only in the presence of calcium. Conventional approaches to purify calpain comprise multiple chromatographic steps, and are labor-intensive, leading to low yields. Here we report a new purification procedure for the human m-calpain based on its reversible calcium-mediated interaction with the intrinsically disordered calpastatin. We exploit the specific binding properties of human calpastatin domain 1 (hCSD1) to physically capture human m-calpain from a complex biological mixture. The dissociation of the complex is mediated by chelating calcium, upon which heterodimeric calpain elutes while hCSD1 remains immobilized onto the stationary phase. This novel affinity-based purification was compared to the conventional multistep purification strategy and we find that it is robust, it yields a homogeneous preparation, it can be scaled up easily and it rests on a non-disruptive step that maintains close to physiological conditions that allow further biophysical and functional studies.

Just a Flexible Linker? The Structural and Dynamic Properties of CBP-ID4 Revealed by NMR Spectroscopy

Here, we present a structural and dynamic description of CBP-ID4 at atomic resolution. ID4 is the fourth intrinsically disordered linker of CREB-binding protein (CBP). In spite of the largely disordered nature of CBP-ID4, NMR chemical shifts and relaxation measurements show a significant degree of α-helix sampling in the protein regions encompassing residues 2-25 and 101-128 (1852-1875 and 1951-1978 in full-length CBP). Proline residues are uniformly distributed along the polypeptide, except for the two α-helical regions, indicating that they play an active role in modulating the structural features of this CBP fragment. The two helical regions are lacking known functional motifs, suggesting that they represent thus-far uncharacterized functional modules of CBP. This work provides insights into the functions of this protein linker that may exploit its plasticity to modulate the relative orientations of neighboring folded domains of CBP and fine-tune its interactions with a multitude of partners.

Start2Fold: a database of hydrogen/deuterium exchange data on protein folding and stability

Proteins fulfil a wide range of tasks in cells; understanding how they fold into complex three-dimensional (3D) structures and how these structures remain stable while retaining sufficient dynamics for functionality is essential for the interpretation of overall protein behaviour. Since the 1950's, solvent exchange-based methods have been the most powerful experimental means to obtain information on the folding and stability of proteins. Considerable expertise and care were required to obtain the resulting datasets, which, despite their importance and intrinsic value, have never been collected, curated and classified. Start2Fold is an openly accessible database (http://start2fold.eu) of carefully curated hydrogen/deuterium exchange (HDX) data extracted from the literature that is open for new submissions from the community. The database entries contain (i) information on the proteins investigated and the underlying experimental procedures and (ii) the classification of the residues based on their exchange protection levels, also allowing for the instant visualization of the relevant residue groups on the 3D structures of the corresponding proteins. By providing a clear hierarchical framework for the easy sharing, comparison and (re-)interpretation of HDX data, Start2Fold intends to promote a better understanding of how the protein sequence encodes folding and structure as well as the development of new computational methods predicting protein folding and stability.

Functional Advantages of Conserved Intrinsic Disorder in RNA-Binding Proteins

Proteins form large macromolecular assemblies with RNA that govern essential molecular processes. RNA-binding proteins have often been associated with conformational flexibility, yet the extent and functional implications of their intrinsic disorder have never been fully assessed. Here, through large-scale analysis of comprehensive protein sequence and structure datasets we demonstrate the prevalence of intrinsic structural disorder in RNA-binding proteins and domains. We addressed their functionality through a quantitative description of the evolutionary conservation of disordered segments involved in binding, and investigated the structural implications of flexibility in terms of conformational stability and interface formation. We conclude that the functional role of intrinsically disordered protein segments in RNA-binding is two-fold: first, these regions establish extended, conserved electrostatic interfaces with RNAs via induced fit. Second, conformational flexibility enables them to target different RNA partners, providing multi-functionality, while also ensuring specificity. These findings emphasize the functional importance of intrinsically disordered regions in RNA-binding proteins.

Computational approaches for inferring the functions of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are ubiquitously involved in cellular processes and often implicated in human pathological conditions. The critical biological roles of these proteins, despite not adopting a well-defined fold, encouraged structural biologists to revisit their views on the protein structure-function paradigm. Unfortunately, investigating the characteristics and describing the structural behavior of IDPs is far from trivial, and inferring the function(s) of a disordered protein region remains a major challenge. Computational methods have proven particularly relevant for studying IDPs: on the sequence level their dependence on distinct characteristics determined by the local amino acid context makes sequence-based prediction algorithms viable and reliable tools for large scale analyses, while on the structure level the in silico integration of fundamentally different experimental data types is essential to describe the behavior of a flexible protein chain. Here, we offer an overview of the latest developments and computational techniques that aim to uncover how protein function is connected to intrinsic disorder.

DisCons: a novel tool to quantify and classify evolutionary conservation of intrinsic protein disorder

Background

Analyzing the amino acid sequence of an intrinsically disordered protein (IDP) in an evolutionary context can yield novel insights on the functional role of disordered regions and sequence element(s). However, in the case of many IDPs, the lack of evolutionary conservation of the primary sequence can hamper the study of functionality, because the conservation of their disorder profile and ensuing function(s) may not appear in a traditional analysis of the evolutionary history of the protein.

Results

Here we present DisCons (Disorder Conservation), a novel pipelined tool that combines the quantification of sequence- and disorder conservation to classify disordered residue positions. According to this scheme, the most interesting categories (for functional purposes) are constrained disordered residues and flexible disordered residues. The former residues show conservation of both the sequence and the property of disorder and are associated mainly with specific binding functionalities (e.g., short, linear motifs, SLiMs), whereas the latter class correspond to segments where disorder as a feature is important for function as opposed to the identity of the underlying sequence (e.g., entropic chains and linkers). DisCons therefore helps with elucidating the function(s) arising from the disordered state by analyzing individual proteins as well as large-scale proteomics datasets.

Conclusions

DisCons is an openly accessible sequence analysis tool that identifies and highlights structurally disordered segments of proteins where the conformational flexibility is conserved across homologs, and therefore potentially functional. The tool is freely available both as a web application and as stand-alone source code hosted at http://pedb.vib.be/discons.

DisCons: a novel tool to quantify and classify evolutionary conservation of intrinsic protein disorder

BACKGROUND

Analyzing the amino acid sequence of an intrinsically disordered protein (IDP) in an evolutionary context can yield novel insights on the functional role of disordered regions and sequence element(s). However, in the case of many IDPs, the lack of evolutionary conservation of the primary sequence can hamper the study of functionality, because the conservation of their disorder profile and ensuing function(s) may not appear in a traditional analysis of the evolutionary history of the protein.

RESULTS

Here we present DisCons (Disorder Conservation), a novel pipelined tool that combines the quantification of sequence- and disorder conservation to classify disordered residue positions. According to this scheme, the most interesting categories (for functional purposes) are constrained disordered residues and flexible disordered residues. The former residues show conservation of both the sequence and the property of disorder and are associated mainly with specific binding functionalities (e.g., short, linear motifs, SLiMs), whereas the latter class correspond to segments where disorder as a feature is important for function as opposed to the identity of the underlying sequence (e.g., entropic chains and linkers). DisCons therefore helps with elucidating the function(s) arising from the disordered state by analyzing individual proteins as well as large-scale proteomics datasets.

CONCLUSIONS

DisCons is an openly accessible sequence analysis tool that identifies and highlights structurally disordered segments of proteins where the conformational flexibility is conserved across homologs, and therefore potentially functional. The tool is freely available both as a web application and as stand-alone source code hosted at http://pedb.vib.be/discons .

pE-DB: a database of structural ensembles of intrinsically disordered and of unfolded proteins

The goal of pE-DB (http://pedb.vib.be) is to serve as an openly accessible database for the deposition of structural ensembles of intrinsically disordered proteins (IDPs) and of denatured proteins based on nuclear magnetic resonance spectroscopy, small-angle X-ray scattering and other data measured in solution. Owing to the inherent flexibility of IDPs, solution techniques are particularly appropriate for characterizing their biophysical properties, and structural ensembles in agreement with these data provide a convenient tool for describing the underlying conformational sampling. Database entries consist of (i) primary experimental data with descriptions of the acquisition methods and algorithms used for the ensemble calculations, and (ii) the structural ensembles consistent with these data, provided as a set of models in a Protein Data Bank format. PE-DB is open for submissions from the community, and is intended as a forum for disseminating the structural ensembles and the methodologies used to generate them. While the need to represent the IDP structures is clear, methods for determining and evaluating the structural ensembles are still evolving. The availability of the pE-DB database is expected to promote the development of new modeling methods and leads to a better understanding of how function arises from disordered states.

Low or absent SPARC expression in acute myeloid leukemia with MLL rearrangements is associated with sensitivity to growth inhibition by exogenous SPARC protein

Secreted protein, acidic and rich in cysteine (SPARC), is a matricellular glycoprotein with growth-inhibitory and antiangiogenic functions. Although SPARC has been implicated as a tumor suppressor in humans, its function in normal or malignant hematopoiesis has not previously been studied. We found that the leukemic cells of AML patients with MLL gene rearrangements express low to undetectable amounts of SPARC whereas normal hematopoietic progenitors and most AML patients express this gene. SPARC RNA and protein levels were also low or undetectable in AML cell lines with MLL translocations. Consistent with its tumor suppressive effects in various solid tumor models, exogenous SPARC protein selectively reduced the growth of cell lines with MLL rearrangements by inhibiting cell cycle progression from G1 to S phase. The lack of SPARC expression in MLL-rearranged cell lines was associated with dense promoter methylation. However, we found no evidence of methylation-based silencing of SPARC in primary patient samples. Our results suggest that low or absent SPARC expression is a consistent feature of AML cells with MLL rearrangements and that SPARC may function as a tumor suppressor in this subset of patients. A potential role of exogenous SPARC in the therapy of MLL-rearranged AML warrants further investigation.

Critical role of conserved proline residues in the transmembrane segment 4 voltage sensor function and in the gating of L-type calcium channels

The fourth transmembrane segment (S4) has been shown to function as a voltage sensor in voltage-gated channels. On membrane depolarization, a stretch of S4 moves outward and initiates a number of conformational changes that ultimately lead to channel opening. Conserved proline residues are in the middle of the S4 of motifs I and III in voltage-dependent Ca2+ channels. Because proline often introduces a "kink" into a helical structure of proteins, these residues might have an intrinsic function in the voltage sensor. Here, we report that the removal of S4 prolines results in a dramatic shortening of channel open time whereas the introduction of extra prolines to the corresponding positions in motif IIS4 and IVS4 lengthens channel open time. The number of S4s with a proline residue showed a clear positive correlation with the mean open time of the channel. The mean open time was >11-fold longer for a channel mutagenized to have prolines in all four S4s compared with a channel that had no prolines in the S4 region. Additionally, prolines in the S4s slowed activation kinetics and shifted the voltage dependence of activation and inactivation in a hyperpolarized direction. Our results strongly suggest that proline residues in the S4s are critical for stabilizing the open state of the channel. Moreover, it is suggested that motif IS4 and IIIS4 contribute to the channel opening more efficiently than motif IIS4 and IVS4.

cAMP-dependent phosphorylation sites and macroscopic activity of recombinant cardiac L-type calcium channels

The involvement of cAMP-dependent phosphorylation sites in establishing the basal activity of cardiac L-type Ca2+ channels was studied in HEK 293 cells transiently cotransfected with mutants of the human cardiac alpha1 and accessory subunits. Systematic individual or combined elimination of high consensus protein kinase A (PKA) sites, by serine to alanine substitutions at the amino and carboxyl termini of the alpha1 subunit, resulted in Ca2+ channel currents indistinguishable from those of wild type channels. Dihydropyridine (DHP)-binding characteristics were also unaltered. To explore the possible involvement of nonconsensus sites, deletion mutants were used. Carboxyl-terminal truncations of the alpha1 subunit distal to residue 1597 resulted in increased channel expression and current amplitudes. Modulation of PKA activity in cells transfected with the wild type channel or any of the mutants did not alter Ca2+ channel functions suggesting that cardiac Ca2+ channels expressed in these cells behave, in terms of lack of PKA control, like Ca2+ channels of smooth muscle cells.

Endogenous cardiac Ca2+ channels do not overcome the E-C coupling defect in immortalized dysgenic muscle cells: evidence for a missing link

The expression of subunit genes of the Ca2+ channel complex was studied in differentiating, immortalized mouse mdg cells. These cells expressed alpha 1 and alpha 2/delta transcripts of the skeletal muscle Ca2+ channel genes, a cardiac Ca2+ channel alpha 1 subunit gene and several known transcript variants of skeletal, cardiac and brain beta genes. The mdg mutation is retained in the 129DA3 cell line and occurs exclusively at nucleotide position 4010 in the skeletal alpha 1 transcript in which a cytosine residue is deleted. In early stages of differentiation and fusion, Ba2+ currents were detected in dysgenic myotubes the same as the cardiac L-type Ca2+ channel. These data provide specific structural evidence [Chaudhari, N. (1992) J. Biol. Chem. 267, 25636-25639] for the major genetic defect in mouse muscular dysgenesis and show a change in the expression levels of alpha 1S and alpha 1C. The upregulation of the expression of alpha 1C results in functional Ca2+ channel activity, however, presumably not sufficient for excitation-contraction coupling.

Involvement of the carboxyl-terminal region of the alpha 1 subunit in voltage-dependent inactivation of cardiac calcium channels

Intracellular application of proteases increases cardiac calcium current to a level similar to beta-adrenergic stimulation. Using transiently transfected HEK 293 cells, we studied the molecular mechanism underlying calcium channel stimulation by proteolytic treatment. Perfusion of HEK cells, coexpressing the human cardiac (hHT) alpha 1, alpha 2, and beta 3 subunits, with 1 mg/ml of trypsin or carboxypeptidase A, increased the peak amplitude of the calcium channel current 3-4-fold without affecting the voltage dependence. Similar results were obtained in HEK cells cotransfected with hHT alpha 1 and alpha 2 or with alpha 1 alone, suggesting that modification of the alpha 1 subunit itself is responsible for the current enhancement by proteolysis. To further characterize the modification of the alpha 1 subunit by trypsin, we expressed a deletion mutant in which part of the carboxyl-terminal tail up to amino acid 1673 was removed. The expressed calcium channel currents no longer responded to intracellular application of the proteases; however, a 3-fold higher current density as well as faster inactivation compared with the wild type was observed. The results provide evidence that a specific region of the carboxyl-terminal tail of the cardiac alpha 1 subunit is an important regulatory segment that may serve as a critical component of the gating machinery that influences both inactivation properties as well as channel availability.

Coexpression of skeletal muscle voltage-dependent calcium channel alpha 1 and beta cDNAs in mouse Ltk- cells increases the amount of alpha 1 protein in the cell membrane

Coexpression of alpha 1 and beta subunits for all tissue-specific isotypes of calcium channels results in an increase in Ca2+ channel current density and drug receptor function. The quantification of the photoaffinity labeled skeletal muscle alpha 1 subunit polypeptide by two alternative immunoadsorption techniques showed that 3 times higher amount of calcium channel alpha 1 protein appears in the cell membrane when alpha 1 and beta subunits were coexpressed compared to alpha 1 alone. Using Western blotting and immunodetection techniques, we identified two polypeptide bands of alpha 1 with 210 kDa and 170 kD molecular mass, respectively. The combined intensity of these bands was 7-9 times higher when alpha 1 and beta were coexpressed compared to alpha 1 alone. The data provide evidence that coexpression of alpha 1 and beta results in an increased amount of alpha 1 protein in the cell membrane.

Characterization of beta subunit modulation of a rabbit cardiac L-type Ca2+ channel alpha 1 subunit as expressed in mouse L cells

Functional properties of a rabbit cardiac alpha 1 Ca2+ channel subunit (CARD alpha 1) were investigated using the patch-clamp technique in mouse L cells, a recipient cell line which is devoid of any Ca2+ channel subunits. Cell lines resulting from stable transfection of the CARD alpha 1 subunit as well as in coexpression with a beta subunit (CARD alpha 1 beta) derived from skeletal muscle (SKM beta) were characterized. The results show that while the CARD alpha 1-Ca2+ channel activity is negligible, the Ba2+ current density is dramatically increased in the presence of beta subunit (approximately 20-fold). CARD alpha 1- and CARD alpha 1 beta-Ba2+ currents were both sensitive to the 1,4-dihydropyridine (DHP) agonist, Bay K 8644 (5- to 8-fold increase). Activation kinetics of CARD alpha 1- and CARD alpha 1 beta-Ba2+ currents were comparable. The inactivation time-course was faster (3- to 4-fold) for CARD alpha 1 beta-Ba2+ currents. We conclude that the main role of the beta subunit in heart is to modulate the L-type current density and present several lines of evidence that SKM alpha 1 and CARD alpha 1 are differentially regulated by the beta subunit.

Acceleration of activation and inactivation by the beta subunit of the skeletal muscle calcium channel

The L-type voltage-dependent calcium channel is an important link in excitation-contraction coupling of muscle cells (reviewed in refs 2 and 3). The channel has two functional characteristics: calcium permeation and receptor sites for calcium antagonists. In skeletal muscle the channel is a complex of five subunits, alpha 1, alpha 2, beta, gamma and delta. Complementary DNAs to these subunits have been cloned and their amino-acid sequences deduced. The skeletal muscle alpha 1 subunit cDNA expressed in L cells manifests as specific calcium-ion permeation, as well as sensitivity to the three classes of organic calcium-channel blockers. We report here that coexpression of the alpha 1 subunit with other subunits results in significant changes in dihydropyridine binding and gating properties. The available number of drug receptor sites increases 10-fold with an alpha 1 beta combination, whereas the affinity of the dihydropyridine binding site remains unchanged. Also, the presence of the beta subunit accelerates activation and inactivation kinetics of the calcium-channel current.