001     829374
005     20240625095126.0
024 7 _ |a 10.3390/biomedicines5010009
|2 doi
024 7 _ |a 2128/14870
|2 Handle
024 7 _ |a WOS:000398714600009
|2 WOS
024 7 _ |a altmetric:20576294
|2 altmetric
024 7 _ |a pmid:28536352
|2 pmid
037 _ _ |a FZJ-2017-03087
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Bochicchio, Anna
|0 P:(DE-Juel1)169975
|b 0
245 _ _ |a Designing the Sniper: Improving Targeted Human Cytolytic Fusion Proteins for Anti-Cancer Therapy via Molecular Simulation
260 _ _ |a Basel
|c 2017
|b MDPI
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1499671017_7964
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Targeted human cytolytic fusion proteins (hCFPs) are humanized immunotoxins for selective treatment of different diseases including cancer. They are composed of a ligand specifically binding to target cells genetically linked to a human apoptosis-inducing enzyme. hCFPs target cancer cells via an antibody or derivative (scFv) specifically binding to e.g., tumor associated antigens (TAAs). After internalization and translocation of the enzyme from endocytosed endosomes, the human enzymes introduced into the cytosol are efficiently inducing apoptosis. Under in vivo conditions such enzymes are subject to tight regulation by native inhibitors in order to prevent inappropriate induction of cell death in healthy cells. Tumor cells are known to upregulate these inhibitors as a survival mechanism resulting in escape of malignant cells from elimination by immune effector cells. Cytosolic inhibitors of Granzyme B and Angiogenin (Serpin P9 and RNH1, respectively), reduce the efficacy of hCFPs with these enzymes as effector domains, requiring detrimentally high doses in order to saturate inhibitor binding and rescue cytolytic activity. Variants of Granzyme B and Angiogenin might feature reduced affinity for their respective inhibitors, while retaining or even enhancing their catalytic activity. A powerful tool to design hCFPs mutants with improved potency is given by in silico methods. These include molecular dynamics (MD) simulations and enhanced sampling methods (ESM). MD and ESM allow predicting the enzyme-protein inhibitor binding stability and the associated conformational changes, provided that structural information is available. Such “high-resolution” detailed description enables the elucidation of interaction domains and the identification of sites where particular point mutations may modify those interactions. This review discusses recent advances in the use of MD and ESM for hCFP development from the viewpoints of scientists involved in both fields.
536 _ _ |a 899 - ohne Topic (POF3-899)
|0 G:(DE-HGF)POF3-899
|c POF3-899
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Jordaan, Sandra
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Losasso, Valeria
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Chetty, Shivan
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Casasnovas Perera, Rodrigo
|0 P:(DE-Juel1)157186
|b 4
700 1 _ |a Ippoliti, Emiliano
|0 P:(DE-Juel1)146009
|b 5
700 1 _ |a Barth, Stefan
|0 P:(DE-HGF)0
|b 6
|e Corresponding author
700 1 _ |a Carloni, Paolo
|0 P:(DE-Juel1)145614
|b 7
|e Corresponding author
773 _ _ |a 10.3390/biomedicines5010009
|g Vol. 5, no. 1, p. 9 -
|0 PERI:(DE-600)2720867-9
|n 1
|p 9
|t Biomedicines
|v 5
|y 2017
|x 2227-9059
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/829374/files/biomedicines-05-00009-v2.pdf
856 4 _ |y OpenAccess
|x icon
|u https://juser.fz-juelich.de/record/829374/files/biomedicines-05-00009-v2.gif?subformat=icon
856 4 _ |y OpenAccess
|x icon-1440
|u https://juser.fz-juelich.de/record/829374/files/biomedicines-05-00009-v2.jpg?subformat=icon-1440
856 4 _ |y OpenAccess
|x icon-180
|u https://juser.fz-juelich.de/record/829374/files/biomedicines-05-00009-v2.jpg?subformat=icon-180
856 4 _ |y OpenAccess
|x icon-640
|u https://juser.fz-juelich.de/record/829374/files/biomedicines-05-00009-v2.jpg?subformat=icon-640
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/829374/files/biomedicines-05-00009-v2.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:829374
|p openaire
|p open_access
|p driver
|p VDB
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)169975
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)157186
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)146009
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 7
|6 P:(DE-Juel1)145614
913 1 _ |a DE-HGF
|b Programmungebundene Forschung
|l ohne Programm
|1 G:(DE-HGF)POF3-890
|0 G:(DE-HGF)POF3-899
|2 G:(DE-HGF)POF3-800
|v ohne Topic
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2017
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a WoS
|0 StatID:(DE-HGF)0112
|2 StatID
|b Emerging Sources Citation Index
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Thomson Reuters Master Journal List
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IAS-5-20120330
|k IAS-5
|l Computational Biomedicine
|x 0
920 1 _ |0 I:(DE-Juel1)INM-9-20140121
|k INM-9
|l Computational Biomedicine
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IAS-5-20120330
980 _ _ |a I:(DE-Juel1)INM-9-20140121
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21