Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen: http://dx.doi.org/10.25673/113166
Titel: Utilization of AlphaFold models for drug discovery : feasibility and challenges : histone deacetylase 11 as a case study
Autor(en): Baselious, Fady
Robaa, DinaIn der Gemeinsamen Normdatei der DNB nachschlagen
Sippl, WolfgangIn der Gemeinsamen Normdatei der DNB nachschlagen
Erscheinungsdatum: 2023
Art: Artikel
Sprache: Englisch
Zusammenfassung: Histone deacetylase 11 (HDAC11), an enzyme that cleaves acyl groups from acylated lysine residues, is the sole member of class IV of HDAC family with no reported crystal structure so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms which complicates the conventional template-based homology modeling. AlphaFold is a neural network machine learning approach for predicting the 3D structures of proteins with atomic accuracy even in absence of similar structures. However, the structures predicted by AlphaFold are missing small molecules as ligands and cofactors. In our study, we first optimized the HDAC11 AlphaFold model by adding the catalytic zinc ion followed by assessment of the usability of the model by docking of the selective inhibitor FT895. Minimization of the optimized model in presence of transplanted inhibitors, which have been described as HDAC11 inhibitors, was performed. Four complexes were generated and proved to be stable using three replicas of 50 ns MD simulations and were successfully utilized for docking of the selective inhibitors FT895, MIR002 and SIS17. For SIS17, The most reasonable pose was selected based on structural comparison between HDAC6, HDAC8 and the HDAC11 optimized AlphaFold model. The manually optimized HDAC11 model is thus able to explain the binding behavior of known HDAC11 inhibitors and can be used for further structure-based optimization.
URI: https://opendata.uni-halle.de//handle/1981185920/115121
http://dx.doi.org/10.25673/113166
Open-Access: Open-Access-Publikation
Nutzungslizenz: (CC BY-NC-ND 4.0) Creative Commons Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International(CC BY-NC-ND 4.0) Creative Commons Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
Journal Titel: Computers in biology and medicine
Verlag: Elsevier Science
Verlagsort: Amsterdam [u.a.]
Band: 167
Originalveröffentlichung: 10.1016/j.compbiomed.2023.107700
Enthalten in den Sammlungen:Open Access Publikationen der MLU

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
1-s2.0-S0010482523011654-main.pdf13.26 MBAdobe PDFMiniaturbild
Öffnen/Anzeigen