Protein Data Bank: the single global archive for 3D macromolecular structure data

Burley, S.K.; Berman, Helen M.; Bhikadiya, Charmi; Bi, Chunxiao; Chen, Li; Costanzo, L. Di; Christie, Cole H.; Duarte, José M.; Dutta, Shuchismita; Feng, Zhang; Ghosh, Sutapa; Goodsell, David S.; Green, Rachel Kramer; Guranovic, Vladimir; Guzenko, Dmytro; Hudson, Brian P.; Liang, Yu-He; Lowe, Robert; Peisach, Ezra; Periskova, Irina; Randle, Chris; Rose, Alexander; Sekharan, Monica; Shao, Chenghua; Tao, Yi-Ping; Valasatava, Yana; Voigt, Maria; Westbrook, John D.; Young, Jasmine; Zardecki, Christine; Zhuravleva, Marina A.; Kurisu, Genji; Nakamura, Haruki; Kengaku, Yumiko; Cho, Hasumi; Junko, Sato; Kim, Ju Yaen; Ikegawa, Yasuyo; Nakagawa, Atsushi; Yamashita, Reiko; Kudou, Takahiro; Bekker, Gert‐Jan; Suzuki, Hirofumi; Iwata, Takeshi; Yokochi, Masashi; Kobayashi, Naohiro; Fujiwara, Toshimichi; Velankar, Sameer; Kleywegt, Gerard J.; Anyango, Stephen; Armstrong, David; Berrisford, John M.; Conroy, Matthew J.; Dana, Jose M.; Deshpande, Mandar; Gane, Paul J.; Gáborová, Romana; Gupta, Deepti; Gutmanas, Aleksandras; Koča, Jaroslav; Mak, Lora; Mir, Saqib; Mukhopadhyay, Abhik; Nadzirin, Nurul; Nair, Sreenath; Patwardhan, Ardan; Paysan-Lafosse, Typhaine; Pravda, Lukáš; Salih, Osman; Sehnal, David; Váradi, Mihály; Vařeková, Radka Svobodová; Markley, John L.; Hoch, Jeffrey C.; Romero, Pedro; Baskaran, Kumaran; Maziuk, Dimitri; Ulrich, Eldon L.; Wedell, Jonathan R.; Yao, Hongyang; Livny, Miron; Ioannidis, Yannis

doi:10.1093/nar/gky949

Cited by 811 publications

(380 citation statements)

References 40 publications

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“…Peptidase families were assembled into a clan when the structural coordinates for a protein from each family were available at the Protein Data Bank [14] and these were compared using the DALI website [15]. A root mean square value of 6 standard deviation units or more was used to indicate that the structures were homologues.…”

Section: Detection and Classification Of Homologuesmentioning

confidence: 99%

Origins of peptidases

Rawlings

Bateman

2019

Biochimie

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a b s t r a c tThe distribution of all peptidase homologues across all phyla of organisms was analysed to determine within which kingdom each of the 271 families originated. No family was found to be ubiquitous and even peptidases thought to be essential for life, such as signal peptidase and methionyl aminopeptides are missing from some clades. There are 33 peptidase families common to archaea, bacteria and eukaryotes and are assumed to have originated in the last universal common ancestor (LUCA). These include peptidases with different catalytic types, exo-and endopeptidases, peptidases with different tertiary structures and peptidases from different families but with similar structures. This implies that the different catalytic types and structures pre-date LUCA. Other families have had their origins in the ancestors of viruses, archaea, bacteria, fungi, plants and animals, and a number of families have had their origins in the ancestors of particular phyla. The evolution of peptidases is compared to recent hypotheses about the evolution of organisms.

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Section: Detection and Classification Of Homologuesmentioning

confidence: 99%

Origins of peptidases

Rawlings

Bateman

2019

Biochimie

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“…PDB (Protein Data Bank) [34], PDBsum [35] (a pictorial database of 3D structures in PDB) and DisProt (Database of Protein Disorder) [36] are protein structural databases. PDB is the predominant protein three-dimensional (3D) structural database, comprising~144,000 protein structures.…”

Section: Bioinformatics Repositories For Proteomicsmentioning

confidence: 99%

Bioinformatics for Renal and Urinary Proteomics: Call for Aggrandization

Paul

Vimala

Gopal

et al. 2020

IJMS

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The clinical sampling of urine is noninvasive and unrestricted, whereby huge volumes can be easily obtained. This makes urine a valuable resource for the diagnoses of diseases. Urinary and renal proteomics have resulted in considerable progress in kidney-based disease diagnosis through biomarker discovery and treatment. This review summarizes the bioinformatics tools available for this area of proteomics and the milestones reached using these tools in clinical research. The scant research publications and the even more limited bioinformatic tool options available for urinary and renal proteomics are highlighted in this review. The need for more attention and input from bioinformaticians is highlighted, so that progressive achievements and releases can be made. With just a handful of existing tools for renal and urinary proteomic research available, this review identifies a gap worth targeting by protein chemists and bioinformaticians. The probable causes for the lack of enthusiasm in this area are also speculated upon in this review. This is the first review that consolidates the bioinformatics applications specifically for renal and urinary proteomics.

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“…The three-dimensional (3D) structure of OTA was retrieved from PubChem (https://pubchem.ncbi. nlm.nih.gov; PubChem CID: 442530, accessed on 17 December 2018) and it was used as the template to screen a database of ligands derived from all of the small molecules in the bound state with the macromolecules recorded into the RCSB PDB databank (https://www.rcsb.org) [35] (Figure 2). All of the structures of small molecules were organized in the so defined ligand repository and, for the benefit of the present work, all non-redundant 3D structures of ligands were downloaded from the Ligand Expo Download page (http://ligand-expo.rcsb.org) in the chemical table file format (sdf format; 26.868 ligands accessed on December 17, 2018).…”

Section: Assembly Of the Ligands Database For Target Fishingmentioning

confidence: 99%

An In Silico Target Fishing Approach to Identify Novel Ochratoxin A Hydrolyzing Enzyme

Dellafiora

Gonaus²,

Streit³

et al. 2020

Toxins

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Ochratoxin A (OTA), a mycotoxin that is of utmost concern in food and feed safety, is produced by fungal species that mainly belong to the Aspergillus and Penicillium genera. The development of mitigation strategies to reduce OTA content along the supply chains is key to ensuring safer production of food and feed. Enzyme-based strategies are among the most promising methods due to their specificity, efficacy, and multi-situ applicability. In particular, some enzymes are already known for hydrolyzing OTA into ochratoxin alpha (OTα) and phenylalanine (Phe), eventually resulting in detoxification action. Therefore, the discovery of novel OTA hydrolyzing enzymes, along with the advancement of an innovative approach for their identification, could provide a broader basis to develop more effective mitigating strategies in the future. In the present study, a hybrid in silico/in vitro workflow coupling virtual screening with enzymatic assays was applied in order to identify novel OTA hydrolyzing enzymes. Among the various hits, porcine carboxypeptidase B was identified for the first time as an effective OTA hydrolyzing enzyme. The successful experimental endorsement of findings of the workflow confirms that the presented strategy is suitable for identifying novel OTA hydrolyzing enzymes, and it might be relevant for the discovery of other mycotoxin- mitigating enzymes.

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Protein Data Bank: the single global archive for 3D macromolecular structure data

Cited by 811 publications

References 40 publications

Origins of peptidases

Origins of peptidases

Bioinformatics for Renal and Urinary Proteomics: Call for Aggrandization

An In Silico Target Fishing Approach to Identify Novel Ochratoxin A Hydrolyzing Enzyme

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