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

Choudhary, Preeti; Anyango, Stephen; Berrisford, John M.; Tolchard, James; Váradi, Mihály; Velankar, Sameer

doi:10.1038/s41597-023-02101-6

Cited by 7 publications

(8 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We note that although the unliganded cysteines are more reliable negatives than the cysteines in proteins that have not been cysteine-liganded before, false negatives are still possible. Using the most recent PDBx/mmCIF files by SIFTS, 14 we matched each cysteine with the (gene) accession number and residue ID in the UniProt knowledge base (UniProtKB). 11 76% of the cysteine-liganded proteins are enzymes, including 101 proteases, 59 kinases, and 433 other enzymes ( Figure 1 a).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines

Liu,

Clayton,

Shen

et al. 2024

JACS Au

View full text Add to dashboard Cite

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here, we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structures in the protein data bank. The unseen tests yielded 94 and 93% area under the receiver operating characteristic curves for the tree models and CNNs, respectively. Based on the AlphaFold2 predicted structures, the ML models recapitulated the newly liganded cysteines in the PDB with over 90% recall values. To assist the community of covalent drug discoveries, we report the predicted ligandable cysteines in 392 human kinases and their locations in the sequence-aligned kinase structure, including the PH and SH2 domains. Furthermore, we disseminate a searchable online database LigCys3D (https://ligcys.computchem.org/) and a web prediction server DeepCys (https://deepcys.computchem. org/), both of which will be continuously updated and improved by including newly published experimental data. The present work represents an important step toward the ML-led integration of big genome data and structure models to annotate the human proteome space for the next-generation covalent drug discoveries.

show abstract

Section: Resultsmentioning

confidence: 99%

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines

Liu,

Clayton,

Shen

et al. 2024

JACS Au

View full text Add to dashboard Cite

show abstract

“…We note, although the unliganded cysteines are more reliable negatives than the cysteines in proteins that have not been cysteine-liganded before, false negatives are still possible. Using the most recent PDBx/mmCIF files by SIFTS, 14 we matched each cysteine with the (gene) accession number and residue ID in the UniProt knowledge base (UniProtKB). 11 76% of the cysteine-liganded proteins are enzymes, including 101 proteases, 59 kinases and 433 other enzymes (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…We note, the “L-peptide linking” 38 cysteines that were chemically modified at locations other than the sulfur (SG) atom or simply oxidized were excluded, as well as the cysteines involved in disulfide bonds, zinc-finger coordination, or iron-sulfur clusters. Following the compilation of the cysteine-liganded structures, we used SIFTS 14 to annotate the liganded cysteines with UniProt accession numbers and residue IDs (https://www.uniprot.org), 11 which allowed us to retrieve all PDB entries associated with these cysteines. We refer to a cysteine as positive if it is liganded in any crystal structure, and the other cysteines in these structures are referred to as negatives.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Models to Interrogate Proteomewide Covalent Ligandabilities Directed at Cysteines

Liu,

Clayton,

Shen

et al. 2023

Preprint

View full text Add to dashboard Cite

In the recent decade, targeted covalent inhibition (TCI) has become mainstream in drug discovery and an increasingly large number of cysteine-liganded X-ray structures have been deposited in the protein data bank (PDB). At the same time, a chemoproteomic technique called activity-based protein profiling (ABPP) has ushered in the efforts to map covalently ligandable sites in the entire proteome. Here we asked if the current PDB information is sufficient for developing highly predictive machine-learning (ML) models, and what such models can inform us about the divergence between the cysteine ligandabilities captured by crystallography and those determined by ABPP in cells. The tree-based and convolutional neural network (CNN) models were developed, trained on an exhaustively curated database (LigCys3D) containing over 1,000 liganded cysteines in nearly 800 proteins represented by over 10,000 X-ray structures. In the unseen tests, the tree models and CNNs gave the AUCs of about 94%; however, in the evaluation of a nonoverlapping ABPP dataset, the models gave significantly lower AUCs, especially when AlphaFold2 models were used. Our analysis suggests factors giving rise to the divergence and ways to improve the model transferability. Developing ML models as a surrogate of crystallography may further unleash the power of chemoproteomics. Our work represents a first step in the ML-led integration of big genome data, structure models, and chemoproteomic experiments to annotate the human proteome space for the next-generation drug discoveries.

show abstract

“…The targets were further matched to their corresponding models in the AlphaFold database (AFDB) or other experimental structural templates using the Structure Integration with Function, Taxonomy, and Sequence resource 55 . This allowed for the alignment of modeled residues to the corresponding sequence in UniProt and for the linking of target UniProt IDs to all their experimental structures in the PDB and predicted models in the AFDB 11 …”

Section: Methodsmentioning

confidence: 99%

Outer membrane β‐barrel structure prediction through the lens ofAlphaFold2

2023

View full text Add to dashboard Cite

Most proteins found in the outer membrane of gram‐negative bacteria share a common domain: the transmembrane β‐barrel. These outer membrane β‐barrels (OMBBs) occur in multiple sizes and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ββ‐hairpins. This is part of the reason why predicting their three‐dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close‐to‐experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during their training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs and OMBB‐like folds of various topologies using an in‐house‐developed tool for the analysis of OMBB 3D structures, and barrOs. In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts transmembrane β‐barrel structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel transmembrane β‐barrel topologies identified in high‐throughput OMBB annotation studies or designed de novo.

show abstract

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

Cited by 7 publications

References 80 publications

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines

Machine Learning Models to Interrogate Proteomewide Covalent Ligandabilities Directed at Cysteines

Outer membrane β‐barrel structure prediction through the lens ofAlphaFold2

Contact Info

Product

Resources

About