RCSB protein Data Bank: exploring protein 3D similarities via comprehensive structural alignments.

MOTIVATION: Tools for pairwise alignments between 3D structures of proteins are of fundamental importance for structural biology and bioinformatics, enabling visual exploration of evolutionary and functional relationships. However, the absence of a user-friendly, browser-based tool for creating alignments and visualizing them at both 1D sequence and 3D structural levels makes this process unnecessarily cumbersome. RESULTS: We introduce a novel pairwise structure alignment tool (rcsb.org/alignment) that seamlessly integrates into the RCSB Protein Data Bank (RCSB PDB) research-focused RCSB.org web portal. Our tool and its underlying application programming interface (alignment.rcsb.org) empowers users to align several protein chains with a reference structure by providing access to established alignment algorithms (FATCAT, CE, TM-align, or Smith-Waterman 3D). The user-friendly interface simplifies parameter setup and input selection. Within seconds, our tool enables visualization of results in both sequence (1D) and structural (3D) perspectives through the RCSB PDB RCSB.org Sequence Annotations viewer and Mol* 3D viewer, respectively. Users can effortlessly compare structures deposited in the PDB archive alongside more than a million incorporated Computed Structure Models coming from the ModelArchive and AlphaFold DB. Moreover, this tool can be used to align custom structure data by providing a link/URL or uploading atomic coordinate files directly. Importantly, alignment results can be bookmarked and shared with collaborators. By bridging the gap between 1D sequence and 3D structures of proteins, our tool facilitates deeper understanding of complex evolutionary relationships among proteins through comprehensive sequence and structural analyses. AVAILABILITY AND IMPLEMENTATION: The alignment tool is part of the RCSB PDB research-focused RCSB.org web portal and available at rcsb.org/alignment. Programmatic access is available via alignment.rcsb.org. Frontend code has been published at github.com/rcsb/rcsb-pecos-app. Visualization is powered by the open-source Mol* viewer (github.com/molstar/molstar and github.com/molstar/rcsb-molstar) plus the Sequence Annotations in 3D Viewer (github.com/rcsb/rcsb-saguaro-3d).

RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning.

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

RCSB Protein Data Bank 1D3D module: displaying positional features on macromolecular assemblies.

MOTIVATION: Mapping positional features from one-dimensional (1D) sequences onto three-dimensional (3D) structures of biological macromolecules is a powerful tool to show geometric patterns of biochemical annotations and provide a better understanding of the mechanisms underpinning protein and nucleic acid function at the atomic level. RESULTS: We present a new library designed to display fully customizable interactive views between 1D positional features of protein and/or nucleic acid sequences and their 3D structures as isolated chains or components of macromolecular assemblies. AVAILABILITY AND IMPLEMENTATION: https://github.com/rcsb/rcsb-saguaro-3d. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB.

MOTIVATION: Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task. RESULTS: We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D. AVAILABILITY AND IMPLEMENTATION: Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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

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

RCSB Protein Data Bank 1D tools and services.

MOTIVATION: Interoperability between polymer sequences and structural data is essential for providing a complete picture of protein and gene features and helping to understand biomolecular function. RESULTS: Herein, we present two resources designed to improve interoperability between the RCSB Protein Data Bank, the NCBI and the UniProtKB data resources and visualize integrated data therefrom. The underlying tools provide a flexible means of mapping between the different coordinate spaces and an interactive tool allows convenient visualization of the 1-dimensional data over the web. AVAILABILITYAND IMPLEMENTATION: https://1d-coordinates.rcsb.org and https://rcsb.github.io/rcsb-saguaro. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning.

Cryo-EM Single Particle Analysis workflows require tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Conventional methods for automatic particle picking tend to suffer from high false-positive rates, hampering the reconstruction process. One common cause of this problem is the presence of carbon and different types of high-contrast contaminations. In order to overcome this limitation, we have developed MicrographCleaner, a deep learning package designed to discriminate, in an automated fashion, between regions of micrographs which are suitable for particle picking, and those which are not. MicrographCleaner implements a U-net-like deep learning model trained on a manually curated dataset compiled from over five hundred micrographs. The benchmarking, carried out on approximately one hundred independent micrographs, shows that MicrographCleaner is a very efficient approach for micrograph preprocessing. MicrographCleaner (micrograph_cleaner_em) package is available at PyPI and Anaconda Cloud and also as a Scipion/Xmipp protocol. Source code is available at https://github.com/rsanchezgarc/micrograph_cleaner_em.

BIPSPI: a method for the prediction of partner-specific protein-protein interfaces.

Motivation: Protein-Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction. Computational approaches are orders of magnitude cheaper and faster than experimental ones, leading to proliferation of multiple methods aimed to predict which residues belong to the interface of an interaction. Results: We present BIPSPI, a new machine learning-based method for the prediction of partner-specific PPI sites. Contrary to most binding site prediction methods, the proposed approach takes into account a pair of interacting proteins rather than a single one in order to predict partner-specific binding sites. BIPSPI has been trained employing sequence-based and structural features from both protein partners of each complex compiled in the Protein-Protein Docking Benchmark version 5.0 and in an additional set independently compiled. Also, a version trained only on sequences has been developed. The performance of our approach has been assessed by a leave-one-out cross-validation over different benchmarks, outperforming state-of-the-art methods. Availability and implementation: BIPSPI web server is freely available at http://bipspi.cnb.csic.es. BIPSPI code is available at https://github.com/bioinsilico/BIPSPI. Docker image is available at https://hub.docker.com/r/bioinsilico/bipspi/. Supplementary information: Supplementary data are available at Bioinformatics online.

3DBIONOTES v3.0: crossing molecular and structural biology data with genomic variations.

MOTIVATION: Many diseases are associated to single nucleotide polymorphisms that affect critical regions of proteins as binding sites or post translational modifications. Therefore, analysing genomic variants with structural and molecular biology data is a powerful framework in order to elucidate the potential causes of such diseases. RESULTS: A new version of our web framework 3DBIONOTES is presented. This version offers new tools to analyse and visualize protein annotations and genomic variants, including a contingency analysis of variants and amino acid features by means of a Fisher exact test, the integration of a gene annotation viewer to highlight protein features on gene sequences and a protein-protein interaction viewer to display protein annotations at network level. AVAILABILITY AND IMPLEMENTATION: The web server is available at https://3dbionotes.cnb.csic.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: Spanish National Institute for Bioinformatics (INB ELIXIR-ES) and Biocomputing Unit, National Centre of Biotechnology (CSIC)/Instruct Image Processing Centre, C/ Darwin nº 3, Campus of Cantoblanco, 28049 Madrid, Spain.

Validation of electron microscopy initial models via small angle X-ray scattering curves.

MOTIVATION: Cryo electron microscopy (EM) is currently one of the main tools to reveal the structural information of biological macromolecules. The re-construction of three-dimensional (3D) maps is typically carried out following an iterative process that requires an initial estimation of the 3D map to be refined in subsequent steps. Therefore, its determination is key in the quality of the final results, and there are cases in which it is still an open issue in single particle analysis (SPA). Small angle X-ray scattering (SAXS) is a well-known technique applied to structural biology. It is useful from small nanostructures up to macromolecular ensembles for its ability to obtain low resolution information of the biological sample measuring its X-ray scattering curve. These curves, together with further analysis, are able to yield information on the sizes, shapes and structures of the analyzed particles. RESULTS: In this paper, we show how the low resolution structural information revealed by SAXS is very useful for the validation of EM initial 3D models in SPA, helping the following refinement process to obtain more accurate 3D structures. For this purpose, we approximate the initial map by pseudo-atoms and predict the SAXS curve expected for this pseudo-atomic structure. The match between the predicted and experimental SAXS curves is considered as a good sign of the correctness of the EM initial map. AVAILABILITY AND IMPLEMENTATION: The algorithm is freely available as part of the Scipion 1.2 software at http://scipion.i2pc.es/.

Study protocol of a quasi-experimental trial to compare two models of home care for older people in the primary setting.

BACKGROUND: Preventive home visits are suited for patients with reduced mobility, such as older people. Healthcare needs for older patients are expected to increase due to the extended life expectancy estimated in coming years. The implementation of low-cost, patient-centered methodologies may buffer this rise in health care costs without affecting the quality of service. In order to find the best home care model with less investment, this paper describes a study protocol comparing two models of home care for older people. METHODS: We describe a quasi-experimental study that compares the outcome of two different home care models already implemented in two primary care centers in Badalona (Barcelona, Spain). The traditional model (control model) is integrated in the sense that is continuous, the same primary care center team looks after its assigned patients both at the center and in preventive home visits. The new functional home care model (study model), consisting of a highly trained team, is specifically designed to meet patient needs and give total attention to preventive home interventions. The study will start and end on the expected dates, June 2018 to October 2020, and include all patients over 65 years old already enrolled in the home care programs of the primary care centers selected. The primary endpoint assessed will be the difference in hospitalization days between patients included in both home care programs. Other variables regarding health status, quality of care and resource utilization will also be compared between the two models. DISCUSSION: The study in progress will assess whether a functional and highly trained home care team will meet the ever-aging population needs in terms of cost and health outcomes better than a traditional, integrated one. Lessons learned from this pilot study will provide guidelines for a future model of home care based on the IHI Triple Aim: better care, better health, and lower costs. TRIAL REGISTRATION: Registered in ClinicalTrials.gov (Identifier: NCT03461315; March 12, 2018).

3DBIONOTES v2.0: a web server for the automatic annotation of macromolecular structures.

MOTIVATION: Complementing structural information with biochemical and biomedical annotations is a powerful approach to explore the biological function of macromolecular complexes. However, currently the compilation of annotations and structural data is a feature only available for those structures that have been released as entries to the Protein Data Bank. RESULTS: To help researchers in assessing the consistency between structures and biological annotations for structural models not deposited in databases, we present 3DBIONOTES v2.0, a web application designed for the automatic annotation of biochemical and biomedical information onto macromolecular structural models determined by any experimental or computational technique. AVAILABILITY AND IMPLEMENTATION: The web server is available at http://3dbionotes-ws.cnb.csic.es. CONTACT: jsegura@cnb.csic.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

3DCONS-DB: A Database of Position-Specific Scoring Matrices in Protein Structures.

Many studies have used position-specific scoring matrices (PSSM) profiles to characterize residues in protein structures and to predict a broad range of protein features. Moreover, PSSM profiles of Protein Data Bank (PDB) entries have been recalculated in many works for different purposes. Although the computational cost of calculating a single PSSM profile is affordable, many statistical studies or machine learning-based methods used thousands of profiles to achieve their goals, thereby leading to a substantial increase of the computational cost. In this work we present a new database compiling PSSM profiles for the proteins of the PDB. Currently, the database contains 333,532 protein chain profiles involving 123,135 different PDB entries.

3DIANA: 3D Domain Interaction Analysis: A Toolbox for Quaternary Structure Modeling.

Electron microscopy (EM) is experiencing a revolution with the advent of a new generation of Direct Electron Detectors, enabling a broad range of large and flexible structures to be resolved well below 1 nm resolution. Although EM techniques are evolving to the point of directly obtaining structural data at near-atomic resolution, for many molecules the attainable resolution might not be enough to propose high-resolution structural models. However, accessing information on atomic coordinates is a necessary step toward a deeper understanding of the molecular mechanisms that allow proteins to perform specific tasks. For that reason, methods for the integration of EM three-dimensional maps with x-ray and NMR structural data are being developed, a modeling task that is normally referred to as fitting, resulting in the so called hybrid models. In this work, we present a novel application-3DIANA-specially targeted to those cases in which the EM map resolution is medium or low and additional experimental structural information is scarce or even lacking. In this way, 3DIANA statistically evaluates proposed/potential contacts between protein domains, presents a complete catalog of both structurally resolved and predicted interacting regions involving these domains and, finally, suggests structural templates to model the interaction between them. The evaluation of the proposed interactions is computed with DIMERO, a new method that scores physical binding sites based on the topology of protein interaction networks, which has recently shown the capability to increase by 200% the number of domain-domain interactions predicted in interactomes as compared to previous approaches. The new application displays the information at a sequence and structural level and is accessible through a web browser or as a Chimera plugin at http://3diana.cnb.csic.es.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

Using neighborhood cohesiveness to infer interactions between protein domains.

MOTIVATION: In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps, or interactomes, for a number of relevant organisms, including human. However, current interactomes provide a somehow limited picture of the molecular details involving protein interactions, mostly because essential experimental information, especially structural data, is lacking. Indeed, the gap between structural and interactomics information is enlarging and thus, for most interactions, key experimental information is missing. We elaborate on the observation that many interactions between proteins involve a pair of their constituent domains and, thus, the knowledge of how protein domains interact adds very significant information to any interactomic analysis. RESULTS: In this work, we describe a novel use of the neighborhood cohesiveness property to infer interactions between protein domains given a protein interaction network. We have shown that some clustering coefficients can be extended to measure a degree of cohesiveness between two sets of nodes within a network. Specifically, we used the meet/min coefficient to measure the proportion of interacting nodes between two sets of nodes and the fraction of common neighbors. This approach extends previous works where homolog coefficients were first defined around network nodes and later around edges. The proposed approach substantially increases both the number of predicted domain-domain interactions as well as its accuracy as compared with current methods.

InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research.

Virtually all the biological processes that occur inside or outside cells are mediated by protein-protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com).

Frag'r'Us: knowledge-based sampling of protein backbone conformations for de novo structure-based protein design.

MOTIVATION: The remodeling of short fragment(s) of the protein backbone to accommodate new function(s), fine-tune binding specificities or change/create novel protein interactions is a common task in structure-based computational design. Alternative backbone conformations can be generated de novo or by redeploying existing fragments extracted from protein structures i.e. knowledge-based. We present Frag'r'Us, a web server designed to sample alternative protein backbone conformations in loop regions. The method relies on a database of super secondary structural motifs called smotifs. Thus, sampling of conformations reflects structurally feasible fragments compiled from existing protein structures. Availability and implementation Frag'r'Us has been implemented as web application and is available at http://www.bioinsilico.org/FRAGRUS.

CAPS-DB: a structural classification of helix-capping motifs.

The regions of the polypeptide chain immediately preceding or following an α-helix are known as Nt- and Ct cappings, respectively. Cappings play a central role stabilizing α-helices due to lack of intrahelical hydrogen bonds in the first and last turn. Sequence patterns of amino acid type preferences have been derived for cappings but the structural motifs associated to them are still unclassified. CAPS-DB is a database of clusters of structural patterns of different capping types. The clustering algorithm is based in the geometry and the (Φ-ψ)-space conformation of these regions. CAPS-DB is a relational database that allows the user to search, browse, inspect and retrieve structural data associated to cappings. The contents of CAPS-DB might be of interest to a wide range of scientist covering different areas such as protein design and engineering, structural biology and bioinformatics. The database is accessible at: http://www.bioinsilico.org/CAPSDB.

A holistic in silico approach to predict functional sites in protein structures.

MOTIVATION: Proteins execute and coordinate cellular functions by interacting with other biomolecules. Among these interactions, protein-protein (including peptide-mediated), protein-DNA and protein-RNA interactions cover a wide range of critical processes and cellular functions. The functional characterization of proteins requires the description and mapping of functional biomolecular interactions and the identification and characterization of functional sites is an important step towards this end. RESULTS: We have developed a novel computational method, Multi-VORFFIP (MV), a tool to predicts protein-, peptide-, DNA- and RNA-binding sites in proteins. MV utilizes a wide range of structural, evolutionary, experimental and energy-based information that is integrated into a common probabilistic framework by means of a Random Forest ensemble classifier. While remaining competitive when compared with current methods, MV is a centralized resource for the prediction of functional sites and is interfaced by a powerful web application tailored to facilitate the use of the method and analysis of predictions to non-expert end-users. AVAILABILITY: http://www.bioinsilico.org/MVORFFIP