Annotation of biologically relevant ligands in UniProtKB using ChEBI

Coudert, Elisabeth; Géhant, Sébastien; Castro, Edouard de; Pozzato, Monica; Baratin, Delphine; Neto, Teresa Batista; Sigrist, Christian J. A.; Redaschi, Nicole; Bridge, Alan

doi:10.1093/bioinformatics/btac793

Cited by 147 publications

(73 citation statements)

References 37 publications

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“…The use of SIFTS annotations in the COSMIC data resource is an excellent example of how this approach can be used to efficiently and accurately analyse the impact of genetic variants on protein function and stability 61 . This can be further expanded to support a wide range of computational approaches for analysing protein structure and function 62 , including functional annotation 48 , structural comparison 59 , ligand binding analysis 63 , identifying new protein-protein interactions 64 , functional pathways, and potential drug targets 65 on a large scale.…”

Section: Resultsmentioning

confidence: 99%

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

et al. 2023

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More than 61,000 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. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.

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Section: Resultsmentioning

confidence: 99%

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

et al. 2023

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“…The UniProt metal binding annotations were converted to binding site annotations (Coudert et al 2023). This data conversion occurred after we created the AlphaFold set but before we benchmarked our metal binding site placement method.…”

Section: Methodsmentioning

confidence: 99%

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

et al. 2023

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Recent advances have enabled high‐quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and nonenzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub‐angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or nonenzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub‐angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90%–97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

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Section: Methodsmentioning

confidence: 99%

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

Feehan

Copeland

Franklin

et al. 2023

Preprint

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Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and non-enzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or non-enzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90 - 97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

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Annotation of biologically relevant ligands in UniProtKB using ChEBI

Cited by 147 publications

References 37 publications

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

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