Comprehensive collection and prediction of ABC transmembrane protein structures in the AI era of structural biology

Tordai, Hedvig; Suhajda, Erzsebet; Sillitoe, Ian; Nair, Sreenath; Váradi, Mihály; Hegedüs, Tamás

doi:10.1101/2022.07.08.499254

Cited by 4 publications

(1 citation statement)

References 65 publications

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“…Since the number of known protein complexes is immense, having a comprehensive database for complex structures soon is rather unlikely. Therefore, integrating 3D data from experts in specialised fields of proteins is important, as demonstrated by physiologically and pathologically relevant transmembrane ABC half transporters 26 and by a set of computed structures of core eukaryotic protein complexes deposited in the ModelArchive 27 . Databases such as the Small-Angle Scattering Biological Data Bank (SASBDB) 28 and the Protein Ensemble Database (PED) 22 highlight the dynamic nature of intrinsically disordered proteins (Figure 2B).…”

Section: Introductionmentioning

confidence: 99%

3D-Beacons: Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

Váradi

Nair

Sillitoe

et al. 2022

Preprint

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While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated theoretical protein modelling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various data providers and perform comparative analysis to identify what models 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. 3D-Beacons 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 the Protein Data Bank.

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Section: Introductionmentioning

confidence: 99%

3D-Beacons: Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

Váradi

Nair

Sillitoe

et al. 2022

Preprint

Self Cite

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Genetics of ABCB1 in Cancer

Skinner,

Palkar,

Hong

2023

Cancers

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ABCB1, also known as MDR1, is a gene that encodes P-glycoprotein (P-gp), a membrane-associated ATP-dependent transporter. P-gp is widely expressed in many healthy tissues—in the gastrointestinal tract, liver, kidney, and at the blood–brain barrier. P-gp works to pump xenobiotics such as toxins and drugs out of cells. P-gp is also commonly upregulated across multiple cancer types such as ovarian, breast, and lung. Overexpression of ABCB1 has been linked to the development of chemotherapy resistance across these cancers. In vitro work across a wide range of drug-sensitive and -resistant cancer cell lines has shown that upon treatment with chemotherapeutic agents such as doxorubicin, cisplatin, and paclitaxel, ABCB1 is upregulated. This upregulation is caused in part by a variety of genetic and epigenetic mechanisms. This includes single-nucleotide variants that lead to enhanced P-gp ATPase activity without increasing ABCB1 RNA and protein levels. In this review, we summarize current knowledge of genetic and epigenetic mechanisms leading to ABCB1 upregulation and P-gp-enhanced ATPase activity in the setting of chemotherapy resistance across a variety of cancers.

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AI-Based Protein Interaction Screening and Identification (AISID)

Sha

2022

IJMS

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In this study, we presented an AISID method extending AlphaFold-Multimer’s success in structure prediction towards identifying specific protein interactions with an optimized AISIDscore. The method was tested to identify the binding proteins in 18 human TNFSF (Tumor Necrosis Factor superfamily) members for each of 27 human TNFRSF (TNF receptor superfamily) members. For each TNFRSF member, we ranked the AISIDscore among the 18 TNFSF members. The correct pairing resulted in the highest AISIDscore for 13 out of 24 TNFRSF members which have known interactions with TNFSF members. Out of the 33 correct pairing between TNFSF and TNFRSF members, 28 pairs could be found in the top five (including 25 pairs in the top three) seats in the AISIDscore ranking. Surprisingly, the specific interactions between TNFSF10 (TNF-related apoptosis-inducing ligand, TRAIL) and its decoy receptors DcR1 and DcR2 gave the highest AISIDscore in the list, while the structures of DcR1 and DcR2 are unknown. The data strongly suggests that AlphaFold-Multimer might be a useful computational screening tool to find novel specific protein bindings. This AISID method may have broad applications in protein biochemistry, extending the application of AlphaFold far beyond structure predictions.

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Comprehensive collection and prediction of ABC transmembrane protein structures in the AI era of structural biology

Cited by 4 publications

References 65 publications

3D-Beacons: Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

3D-Beacons: Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

Genetics of ABCB1 in Cancer

AI-Based Protein Interaction Screening and Identification (AISID)

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