Biology subjects

Varadi, M.

Publications and source records attributed to Varadi, M..

12 recordsLinked to original sources

bioRxiv2025

Distinct response selectivity changes in primary visual and parietal cortex during visual discrimination learning

When animals learn the behavioral relevance of sensory features, response selectivity in primary sensory areas increases for those features. However, the effect of learning on neuronal activity in higher-level areas associated with decision-making in the parietal cortex, compared to primary sensory cortex, is not yet known. We used two-photon calcium imaging to determine how learning modifies neural representations in primary visual cortex (V1) and posterior parietal cortex (PPC) in a visual go/no-go orientation discrimination task. We found that behavior improvements after learning were associated with increased neuronal selectivity in both V1 and PPC. The increased selectivity in PPC was mainly driven by neurons preferring the rewarded go stimulus, while, in V1, neurons increased their selectivity for both the rewarded go and unrewarded no-go stimulus. Furthermore, feature preference was robust in V1 neurons but reorganized in PPC after learning. Finally, feature preference was preserved across contexts in V1 but not in PPC after learning, where many neurons switched their preference to the rewarded feature during active task engagement. Our results demonstrate that learning a visually-guided discrimination task increased information about relevant sensory features through distinct changes in the bottom and top levels of the visual cortical hierarchy. Visual cortex neurons encode visual features with increased reliability but with preserved feature preferences, while parietal neurons reorganize their feature preferences in a task-specific manner. HighlightsO_LIV1 and PPC response selectivity increases after visual discrimination learning. C_LIO_LISelectivity increase in PPC for rewarded features, but in V1 for both rewarded and unrewarded features. C_LIO_LIFeature preference robust in V1 but altered in PPC across learning. C_LIO_LIFeature preference after learning preserved across contexts in V1 but not PPC. C_LI

neuroscience↗

bioRxiv2024

R2DT: a comprehensive platform for visualising RNA secondary structure

RNA secondary (2D) structure visualisation is an essential tool for understanding RNA function. R2DT is a software package designed to visualise RNA 2D structures in consistent, recognisable, and reproducible layouts. The latest release, R2DT 2.0, introduces multiple significant features, including the ability to display position-specific information, such as single nucleotide polymorphisms (SNPs) or SHAPE reactivities. It also offers a new template-free mode allowing visualisation of RNAs without pre-existing templates, alongside a constrained folding mode and support for animated visualisations. Users can interactively modify R2DT diagrams, either manually or using natural language prompts, to generate new templates or create publication-quality images. Additionally, R2DT features faster performance, an expanded template library, and a growing collection of compatible tools and utilities. Already integrated into multiple biological databases, R2DT has evolved into a comprehensive platform for RNA 2D visualisation, accessible at https://r2dt.bio.

bioinformatics↗

bioRxiv2023

PDB NextGen Archive: Centralising Access to Integrated Annotations and Enriched Structural Information by the Worldwide Protein Data Bank

The Protein Data Bank (PDB) archive is the global repository for public-domain 3D biomolecular structural information. The archival nature of the PDB presents certain challenges pertaining to updating or adding associated annotations from trusted external biodata resources. While each Worldwide PDB (wwPDB) partner has made best efforts to provide up-to-date external annotations, accessing and integrating information from disparate wwPDB data centers can be an involved process. To address this issue, the wwPDB has established the PDB Next Generation or NextGen Archive, developed to centralize and streamline access to enriched structural annotations from wwPDB partners and trusted external sources. At present, the archive provides mappings between experimentally determined 3D structures of proteins and UniProt amino acid sequences, together with domain annotations from Pfam, SCOP2, and CATH databases, and intra-molecular connectivity information. Since launch, the PDB NextGen Archive has seen substantial user engagement with over 3.5 million data file downloads, ensuring researchers have access to accurate, up-to-date, and easily accessible structural annotations. Database URLhttp://www.wwpdb.org/ftp/pdb-nextgen-archive-site

bioinformatics↗

bioRxiv2023

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.

bioinformatics↗

bioRxiv2023

Identifying Protein Conformational States in the PDB and Comparison to AlphaFold2 Predictions

Proteins, as molecular machines, are necessarily dynamic macromolecules that carry out essential cellular functions. Recognising their stable conformations is important for understanding the molecular mechanisms of disease. While AI-based computational methods have enabled protein structure prediction, the prediction of protein dynamics remains a challenge. Here, we present a deterministic pipeline that clusters experimentally determined protein structures to comprehensively recognise conformational states across the Protein Data Bank. Our approach clusters protein chains based on a GLObal CONformation (GLOCON) difference score, which is computed from pairwise C-alpha distances. By superposing the clustered structures, differences and similarities in conformational states can be observed. Additionally, we offer users the ability to superpose predicted models from the AlphaFold Database to the clusters of PDB structures. This clustering pipeline significantly advances researchers ability to explore the conformational landscape within the PDB. All clustered and superposed models can be viewed in Mol* on the PDBe Knowledge Base website, or accessed in as raw annotations via our GraphAPI and FTP server. The clustering package is made available as an open-source Python3 package under the Apache-2.0 license.

bioinformatics↗

bioRxiv2023

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 and provided value-added annotations to over 90% of unique assemblies in the PDB. This standardisation of assembly data enhances the PDB, facilitating a deeper understanding of these cellular components. Furthermore, the data standardisation improves the PDBs FAIR attributes, fostering more effective basic and translational research and education across scientific disciplines.

bioinformatics↗

bioRxiv2023

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 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. AvailabilityMethods and data are available at cluster.foldseek.com

bioinformatics↗

bioRxiv2022

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. 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.

bioinformatics↗

bioRxiv2022

Unified access to up-to-date residue-level annotations from UniProt and other biological databases for PDB data via PDBx/mmCIF files

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/).

bioinformatics↗

bioRxiv2022

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 modelling 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.

bioinformatics↗

bioRxiv2022

PDB ProtVista: A reusable and open-source sequence feature viewer

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

bioinformatics↗

bioRxiv2022

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 that 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 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.

bioinformatics↗