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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 status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences.

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 accuracy with over 214 million predicted structures available in the AlphaFold database (AFDB). 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 structures in AFDB, identifying 2.27M non-singleton structural clusters, of which 31% lack annotations representing likely novel structures. Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AFDB. Evolutionary analysis suggests that most clusters are ancient in origin but 4% seem species specific, representing lower quality predictions or examples of de-novo gene birth. Additionally, we show how structural comparisons can be used to predict domain families and their relationships, identifying examples of remote homology. Based on these analyses we identify several examples of human immune related proteins with remote homology in prokaryotic species which illustrates the value of this resource for studying protein function and evolution across the tree of life.

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 accuracy with over 214 million predicted structures available in the AlphaFold database (AFDB). 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 structures in AFDB, identifying 2.27M non-singleton structural clusters, of which 31% lack annotations representing likely novel structures. Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AFDB. Evolutionary analysis suggests that most clusters are ancient in origin but 4% seem species specific, representing lower quality predictions or examples of de-novo gene birth. Additionally, we show how structural comparisons can be used to predict domain families and their relationships, identifying examples of remote homology. Based on these analyses we identify several examples of human immune related proteins with remote homology in prokaryotic species which illustrates the value of this resource for studying protein function and evolution across the tree of life.

The General Assembly of the United Nations has proclaimed 2023 as the International Year of Millets. India and other countries proposed making this statement in order to honour the cultural and nutritional value of millets, and it was officially adopted on December 7th, 2018. Millet refers to several species of small-seeded grasses that are cultivated as cereal crops for human and animal use. Due to their high nutrient density and abundance of essential nutrients like vitamins, minerals, fibre, and protein, they are an essential part of any balanced diet. Millets are significant for both food security and sustainable agriculture since they can be cultivated in marginal sites and are resistant to drought. The purpose of this research paper is to look into consumer attitudes towards millet products in the Mumbai area. Secondary Data and opinion revealing conversations with many individuals interested in the subject were conducted. As well as, random sample of 50 consumers was chosen from various areas of Mumbai, and a self-administered survey questionnaire was used to collect data on demographics, millet knowledge, attitudes towards millet products, and purchasing behaviour. The collected data was analysed and interpreted using descriptive and inferential statistics. The literature review shed light on the health benefits and barriers to millet product consumption. The findings of this study will provide a better understanding of consumer attitudes towards millet products in the Mumbai region and can be used to develop strategies to promote millet-based product consumption.

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.

ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery ofFindable, Accessible, Interoperable, andReusable(FAIR) data to users worldwide. 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, three-dimensional (3D) structures of macromolecules and associated metadata. ModelCIF provides an extensible data representation for deposition, archiving, and public dissemination of predicted 3D models of proteins. 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.

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.

Rise of Double Income families, aspiration to get elevated in careers across the genders, challenges of modern jobs, Time constraints, Diminishing Work Life Balance, lack of availability of affordable and quality service help like (maids, cooks etc.) are some of the major factors, inducing aspiring working population particularly in metro cities of India to adapt to the modern instant food alternatives. There is intense competition in Instant Food like Ready to Cook / Ready To Eat marketers. This promising area of convenient food catering is getting turbulent with introduction of new techniques and innovations. In the series of RTC and RTE, the new offerings by some of the competitors is H.T.E ie Heat To Eat Food alternatives. H.T.E is over simplification of Ready To Eat varieties. As novel and Modern ready to eat food techniques are at nascent stage, such option options seeking target audience is sceptical as well as curious about it. Modern Life Style is very complicated and energy sucking, do desperately need such convenience offerings for Mundane routines. Food is such domain, that is very essential but investing chunk of time in it on day to day basis and that to from Breakfast, Lunch, Snacks, Dinner intervals is considered as CRIMINAL WASTAGE of opportune time, in today’s ambitious career oriented age. Scepticism about taste, quality, content, preservatives etc is obvious but the Energy Saving Super Convenience and notion of Quality Time at disposal is also irresistible. This EXPLORATORY RESEARCH STUDY throws light on Perspectives and Concerns of representative working/employed members/ families pertaining to adaptability of Ready To Cook and Ready To Eat food in the market. Keywords: H.T.E: Heat To Eat Food, Ready To Eat food, Ready To Cook Food, Instant Food, Super Convenient Food

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.

More than 58,000 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. In addition to this fundamental mapping, SIFTS incorporates residue-level annotations from other biological resources such as Pfam, InterPro, SCOP, SCOP2, CATH, IntEnz, GO, PubMed, Ensembl, NCBI taxonomy database and Homologene. The SIFTS data is exported in XML format per individual PDB entry and is also accessible via the PDBe REST API. These mappings have always been maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. In this current work, taking advantage of the extensibility of the core PDBx/mmCIF framework, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProt, Pfam, SCOP2, and CATH mapping information directly into the PDBx/mmCIF files from the PDB archive. The integration of mapping data in the PDBx/mmCIF files provides consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended PDBx/mmCIF format yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at residue level resulting in better data interoperability, supporting improved data analysis and visualisation.Availability and implementationWe expanded the PDBe release pipeline with a process that adds SIFTS annotations to the PDBx/mmCIF files for individual structures in the PDB archive. The scientific community can download these updated PDBx/mmCIF files from the PDBe entry pages(https://pdbe.org/7dr0) and through direct URLs(https://www.ebi.ac.uk/pdbe/static/entry/7o9f_updated.cif), using the PDBe download service(https://www.ebi.ac.uk/pdbe/download/api) or from the EMBL-EBI FTP area(https://ftp.ebi.ac.uk/pub/databases/msd/updated_mmcif/).

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.

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective., AlphaFold is used to predict the structures of almost all of the proteins in the human proteome—the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective.

SummaryThe PDB ProtVista is a reusable and customisable sequence feature viewer that provides intuitive and detailed 2D visualisation of residue-level annotations while also supporting interactive communication with 3D viewers. The Protein Data Bank in Europe (PDBe) team develops and maintains PDB ProtVista. Several web services use it to display structural and functional annotations such as macromolecular interaction interfaces, intrinsic disorder predictions, sequence variants, and sequence conservation.Availability and ImplementationThe PDB ProtVista is a native JavaScript (ES6) application that is freely available from https://github.com/PDBeurope/protvista-pdb. The above URL also provides extensive documentation and step-by-step user guides on integrating PDB ProtVista with existing web applications and 3D molecular viewers. It also offers an example of how to display custom data of the user and functional annotations for PDB and UniProt entries powered by a rich set of PDBe API endpoints.