Biology subjects

Anyango, S.

Publications and source records attributed to Anyango, S..

5 recordsLinked to original sources

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↗

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↗