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Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.

The COVID-19 pandemic has once again highlighted the ubiquity and persistence of health inequities along with our inability to respond to them in a timely and effective manner. There is an opportunity to address the limitations of our current approaches through new models of informatics-enabled research and clinical practice that shift the norm from small- to large-scale patient engagement. We propose augmenting our approach to address health inequities through informatics-enabled citizen science, challenging the types of questions being asked, prioritized, and acted upon. We envision this democratization of informatics that builds upon the inclusive tradition of community-based participatory research (CBPR) as a logical and transformative step toward improving individual, community, and population health in a way that deeply reflects the needs of historically marginalized populations.

The COVID-19 pandemic has once again highlighted the ubiquity and persistence of health inequities along with our inability to respond to them in a timely and effective manner. There is an opportunity to address the limitations of our current approaches through new models of informatics-enabled research and clinical practice that shift the norm from small- to large-scale patient engagement. We propose augmenting our approach to address health inequities through informatics-enabled citizen science, challenging the types of questions being asked, prioritized, and acted upon. We envision this democratization of informatics that builds upon the inclusive tradition of community-based participatory research (CBPR) as a logical and transformative step toward improving individual, community, and population health in a way that deeply reflects the needs of historically marginalized populations.

Many proteins fold into highly regular and repetitive three dimensional structures. The analysis of structural patterns and repeated elements is fundamental to understand protein function and evolution. We present recent improvements to the CE-Symm tool for systematically detecting and analyzing the internal symmetry and structural repeats in proteins. In addition to the accurate detection of internal symmetry, the tool is now capable of i) reporting the type of symmetry, ii) identifying the smallest repeating unit, iii) describing the arrangement of repeats with transformation operations and symmetry axes, and iv) comparing the similarity of all the internal repeats at the residue level. CE-Symm 2.0 helps the user investigate proteins with a robust and intuitive sequence-to-structure analysis, with many applications in protein classification, functional annotation and evolutionary studies. We describe the algorithmic extensions of the method and demonstrate its applications to the study of interesting cases of protein evolution.

SummaryWe developed a new software tool, BioJava-ModFinder, for identifying protein modifications observed in 3D structures archived in the Protein Data Bank (PDB). Information on more than 400 types of protein modifications were collected and curated from annotations in PDB, RESID, and PSI-MOD. We divided these modifications into three categories: modified residues, attachment modifications, and cross-links. We have developed a systematic method to identify these modifications in 3D protein structures. We have integrated this package with the RCSB PDB web application and added protein modification annotations to the sequence diagram and structure display. By scanning all 3D structures in the PDB using BioJava-ModFinder, we identified more than 30 000 structures with protein modifications, which can be searched, browsed, and visualized on the RCSB PDB website.Availability and implementationBioJava-ModFinder is available as open source (LGPL license) at ( https://github.com/biojava/biojava/tree/master/biojava-modfinder ). The RCSB PDB can be accessed at http://www.rcsb.org .Contactpwrose@ucsd.edu.

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

MotivationCircular permutation is an important type of protein rearrangement. Natural circular permutations have implications for protein function, stability and evolution. Artificial circular permutations have also been used for protein studies. However, such relationships are difficult to detect for many sequence and structure comparison algorithms and require special consideration.ResultsWe developed a new algorithm, called Combinatorial Extension for Circular Permutations (CE-CP), which allows the structural comparison of circularly permuted proteins. CE-CP was designed to be user friendly and is integrated into the RCSB Protein Data Bank. It was tested on two collections of circularly permuted proteins. Pairwise alignments can be visualized both in a desktop application or on the web using Jmol and exported to other programs in a variety of formats.Availability and implementationThe CE-CP algorithm can be accessed through the RCSB website at http://www.rcsb.org/pdb/workbench/workbench.do. Source code is available under the LGPL 2.1 as part of BioJava 3 (http://biojava.org; http://github.com/biojava/biojava).Contactsbliven@ucsd.edu or info@rcsb.org.

The RCSB Protein Data Bank (RCSB PDB, http://www.rcsb.org) provides access to 3D structures of biological macromolecules and is one of the leading resources in biology and biomedicine worldwide. Our efforts over the past 2 years focused on enabling a deeper understanding of structural biology and providing new structural views of biology that support both basic and applied research and education. Herein, we describe recently introduced data annotations including integration with external biological resources, such as gene and drug databases, new visualization tools and improved support for the mobile web. We also describe access to data files, web services and open access software components to enable software developers to more effectively mine the PDB archive and related annotations. Our efforts are aimed at expanding the role of 3D structure in understanding biology and medicine.

SummaryThe Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) resource provides tools for query, analysis and visualization of the 3D structures in the PDB archive. As the mobile Web is starting to surpass desktop and laptop usage, scientists and educators are beginning to integrate mobile devices into their research and teaching. In response, we have developed the RCSB PDB Mobile app for the iOS and Android mobile platforms to enable fast and convenient access to RCSB PDB data and services. Using the app, users from the general public to expert researchers can quickly search and visualize biomolecules, and add personal annotations via the RCSB PDB's integrated MyPDB service.Availability and implementationRCSB PDB Mobile is freely available from the Apple App Store and Google Play (http://www.rcsb.org).

SummaryThe Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) resource provides tools for query, analysis and visualization of the 3D structures in the PDB archive. As the mobile Web is starting to surpass desktop and laptop usage, scientists and educators are beginning to integrate mobile devices into their research and teaching. In response, we have developed the RCSB PDB Mobile app for the iOS and Android mobile platforms to enable fast and convenient access to RCSB PDB data and services. Using the app, users from the general public to expert researchers can quickly search and visualize biomolecules, and add personal annotations via the RCSB PDB's integrated MyPDB service.Availability and implementationRCSB PDB Mobile is freely available from the Apple App Store and Google Play (http://www.rcsb.org).

The RCSB Protein Data Bank (RCSB PDB, http://www.rcsb.org) provides access to 3D structures of biological macromolecules and is one of the leading resources in biology and biomedicine worldwide. Our efforts over the past 2 years focused on enabling a deeper understanding of structural biology and providing new structural views of biology that support both basic and applied research and education. Herein, we describe recently introduced data annotations including integration with external biological resources, such as gene and drug databases, new visualization tools and improved support for the mobile web. We also describe access to data files, web services and open access software components to enable software developers to more effectively mine the PDB archive and related annotations. Our efforts are aimed at expanding the role of 3D structure in understanding biology and medicine.

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) develops tools and resources that provide a structural view of biology for research and education. The RCSB PDB web site (http://www.rcsb.org) uses the curated 3D macromolecular data contained in the PDB archive to offer unique methods to access, report and visualize data. Recent activities have focused on improving methods for simple and complex searches of PDB data, creating specialized access to chemical component data and providing domain-based structural alignments. New educational resources are offered at the PDB-101 educational view of the main web site such as Author Profiles that display a researcher's PDB entries in a timeline. To promote different kinds of access to the RCSB PDB, Web Services have been expanded, and an RCSB PDB Mobile application for the iPhone/iPad has been released. These improvements enable new opportunities for analyzing and understanding structure data.

UnlabelledBioJava is an open-source project for processing of biological data in the Java programming language. We have recently released a new version (3.0.5), which is a major update to the code base that greatly extends its functionality.ResultsBioJava now consists of several independent modules that provide state-of-the-art tools for protein structure comparison, pairwise and multiple sequence alignments, working with DNA and protein sequences, analysis of amino acid properties, detection of protein modifications and prediction of disordered regions in proteins as well as parsers for common file formats using a biologically meaningful data model.AvailabilityBioJava is an open-source project distributed under the Lesser GPL (LGPL). BioJava can be downloaded from the BioJava website (http://www.biojava.org). BioJava requires Java 1.6 or higher. All inquiries should be directed to the BioJava mailing lists. Details are available at http://biojava.org/wiki/BioJava:MailingLists.

UnlabelledBioJava is an open-source project for processing of biological data in the Java programming language. We have recently released a new version (3.0.5), which is a major update to the code base that greatly extends its functionality.ResultsBioJava now consists of several independent modules that provide state-of-the-art tools for protein structure comparison, pairwise and multiple sequence alignments, working with DNA and protein sequences, analysis of amino acid properties, detection of protein modifications and prediction of disordered regions in proteins as well as parsers for common file formats using a biologically meaningful data model.AvailabilityBioJava is an open-source project distributed under the Lesser GPL (LGPL). BioJava can be downloaded from the BioJava website (http://www.biojava.org). BioJava requires Java 1.6 or higher. All inquiries should be directed to the BioJava mailing lists. Details are available at http://biojava.org/wiki/BioJava:MailingLists.

The RCSB Protein Data Bank (RCSB PDB, www.pdb.org) is a key online resource for structural biology and related scientific disciplines. The website is used on average by 165,000 unique visitors per month, and more than 2000 other websites link to it. The amount and complexity of PDB data as well as the expectations on its usage are growing rapidly. Therefore, ensuring the reliability and robustness of the RCSB PDB query and distribution systems are crucially important and increasingly challenging. This article describes quality assurance for the RCSB PDB website at several distinct levels, including: (i) hardware redundancy and failover, (ii) testing protocols for weekly database updates, (iii) testing and release procedures for major software updates and (iv) miscellaneous monitoring and troubleshooting tools and practices. As such it provides suggestions for how other websites might be operated.

IEDB-3D is the 3D structural component of the Immune Epitope Database (IEDB) available via the 'Browse by 3D Structure' page at http://www.iedb.org. IEDB-3D catalogs B- and T-cell epitopes and Major Histocompatibility Complex (MHC) ligands for which 3D structures of complexes with antibodies, T-cell receptors or MHC molecules are available in the Protein Data Bank (PDB). Journal articles that are primary citations of PDB structures and that define immune epitopes are curated within IEDB as any other reference along with accompanying functional assays and immunologically relevant information. For each curated structure, IEDB-3D provides calculated data on intermolecular contacts and interface areas and includes an application, EpitopeViewer, to visualize the structures. IEDB-3D is fully embedded within IEDB, thus allowing structural data, both curated and calculated, and all accompanying information to be queried using multiple search interfaces. These include queries for epitopes recognized in different pathogens, eliciting different functional immune responses, and recognized by different components of the immune system. The query results can be downloaded in Microsoft Excel format, or the entire database, together with structural data both curated and calculated, can be downloaded in either XML or MySQL formats.

The RCSB Protein Data Bank (RCSB PDB) web site (http://www.pdb.org) has been redesigned to increase usability and to cater to a larger and more diverse user base. This article describes key enhancements and new features that fall into the following categories: (i) query and analysis tools for chemical structure searching, query refinement, tabulation and export of query results; (ii) web site customization and new structure alerts; (iii) pair-wise and representative protein structure alignments; (iv) visualization of large assemblies; (v) integration of structural data with the open access literature and binding affinity data; and (vi) web services and web widgets to facilitate integration of PDB data and tools with other resources. These improvements enable a range of new possibilities to analyze and understand structure data. The next generation of the RCSB PDB web site, as described here, provides a rich resource for research and education.

IEDB-3D is the 3D structural component of the Immune Epitope Database (IEDB) available via the 'Browse by 3D Structure' page at http://www.iedb.org. IEDB-3D catalogs B- and T-cell epitopes and Major Histocompatibility Complex (MHC) ligands for which 3D structures of complexes with antibodies, T-cell receptors or MHC molecules are available in the Protein Data Bank (PDB). Journal articles that are primary citations of PDB structures and that define immune epitopes are curated within IEDB as any other reference along with accompanying functional assays and immunologically relevant information. For each curated structure, IEDB-3D provides calculated data on intermolecular contacts and interface areas and includes an application, EpitopeViewer, to visualize the structures. IEDB-3D is fully embedded within IEDB, thus allowing structural data, both curated and calculated, and all accompanying information to be queried using multiple search interfaces. These include queries for epitopes recognized in different pathogens, eliciting different functional immune responses, and recognized by different components of the immune system. The query results can be downloaded in Microsoft Excel format, or the entire database, together with structural data both curated and calculated, can be downloaded in either XML or MySQL formats.

SummaryWith the continuous growth of the RCSB Protein Data Bank (PDB), providing an up-to-date systematic structure comparison of all protein structures poses an ever growing challenge. Here, we present a comparison tool for calculating both 1D protein sequence and 3D protein structure alignments. This tool supports various applications at the RCSB PDB website. First, a structure alignment web service calculates pairwise alignments. Second, a stand-alone application runs alignments locally and visualizes the results. Third, pre-calculated 3D structure comparisons for the whole PDB are provided and updated on a weekly basis. These three applications allow users to discover novel relationships between proteins available either at the RCSB PDB or provided by the user.Availability and implementationA web user interface is available at http://www.rcsb.org/pdb/workbench/workbench.do. The source code is available under the LGPL license from http://www.biojava.org. A source bundle, prepared for local execution, is available from http://source.rcsb.orgContactandreas@sdsc.edu; pbourne@ucsd.edu.

SummaryWith the continuous growth of the RCSB Protein Data Bank (PDB), providing an up-to-date systematic structure comparison of all protein structures poses an ever growing challenge. Here, we present a comparison tool for calculating both 1D protein sequence and 3D protein structure alignments. This tool supports various applications at the RCSB PDB website. First, a structure alignment web service calculates pairwise alignments. Second, a stand-alone application runs alignments locally and visualizes the results. Third, pre-calculated 3D structure comparisons for the whole PDB are provided and updated on a weekly basis. These three applications allow users to discover novel relationships between proteins available either at the RCSB PDB or provided by the user.Availability and implementationA web user interface is available at http://www.rcsb.org/pdb/workbench/workbench.do. The source code is available under the LGPL license from http://www.biojava.org. A source bundle, prepared for local execution, is available from http://source.rcsb.orgContactandreas@sdsc.edu; pbourne@ucsd.edu.

BackgroundReliable prediction of antibody, or B-cell, epitopes remains challenging yet highly desirable for the design of vaccines and immunodiagnostics. A correlation between antigenicity, solvent accessibility, and flexibility in proteins was demonstrated. Subsequently, Thornton and colleagues proposed a method for identifying continuous epitopes in the protein regions protruding from the protein's globular surface. The aim of this work was to implement that method as a web-tool and evaluate its performance on discontinuous epitopes known from the structures of antibody-protein complexes.ResultsHere we present ElliPro, a web-tool that implements Thornton's method and, together with a residue clustering algorithm, the MODELLER program and the Jmol viewer, allows the prediction and visualization of antibody epitopes in a given protein sequence or structure. ElliPro has been tested on a benchmark dataset of discontinuous epitopes inferred from 3D structures of antibody-protein complexes. In comparison with six other structure-based methods that can be used for epitope prediction, ElliPro performed the best and gave an AUC value of 0.732, when the most significant prediction was considered for each protein. Since the rank of the best prediction was at most in the top three for more than 70% of proteins and never exceeded five, ElliPro is considered a useful research tool for identifying antibody epitopes in protein antigens. ElliPro is available at http://tools.immuneepitope.org/tools/ElliPro.ConclusionThe results from ElliPro suggest that further research on antibody epitopes considering more features that discriminate epitopes from non-epitopes may further improve predictions. As ElliPro is based on the geometrical properties of protein structure and does not require training, it might be more generally applied for predicting different types of protein-protein interactions.

BackgroundReliable prediction of antibody, or B-cell, epitopes remains challenging yet highly desirable for the design of vaccines and immunodiagnostics. A correlation between antigenicity, solvent accessibility, and flexibility in proteins was demonstrated. Subsequently, Thornton and colleagues proposed a method for identifying continuous epitopes in the protein regions protruding from the protein's globular surface. The aim of this work was to implement that method as a web-tool and evaluate its performance on discontinuous epitopes known from the structures of antibody-protein complexes.ResultsHere we present ElliPro, a web-tool that implements Thornton's method and, together with a residue clustering algorithm, the MODELLER program and the Jmol viewer, allows the prediction and visualization of antibody epitopes in a given protein sequence or structure. ElliPro has been tested on a benchmark dataset of discontinuous epitopes inferred from 3D structures of antibody-protein complexes. In comparison with six other structure-based methods that can be used for epitope prediction, ElliPro performed the best and gave an AUC value of 0.732, when the most significant prediction was considered for each protein. Since the rank of the best prediction was at most in the top three for more than 70% of proteins and never exceeded five, ElliPro is considered a useful research tool for identifying antibody epitopes in protein antigens. ElliPro is available at http://tools.immuneepitope.org/tools/ElliPro.ConclusionThe results from ElliPro suggest that further research on antibody epitopes considering more features that discriminate epitopes from non-epitopes may further improve predictions. As ElliPro is based on the geometrical properties of protein structure and does not require training, it might be more generally applied for predicting different types of protein-protein interactions.

BackgroundOne of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.ResultsWe explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.ConclusionWhen attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used.

BackgroundOne of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.ResultsWe explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.ConclusionWhen attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used.