Détail du document
Identifiant

oai:pubmedcentral.nih.gov:9451...

Sujet
Chemistry
Auteur
Orjuela, Adrián L. Núñez-Zarur, Francisco Alí-Torres, Jorge
Langue
en
Editeur

The Royal Society of Chemistry

Catégorie

PMC full-text journals

Année

2022

Date de référencement

11/10/2022

Mots clés
alzheimer model complexes iron
Métrique

Résumé

Iron complexes play a key role in several biological processes, and they are also related to the development of neurological disorders, such as Alzheimer's and Parkinson's diseases.

One of the main properties involved in these processes is the standard reduction potential (SRP) of iron complexes.

However, the calculation of this property is challenging, mainly due to problems in the electronic structure description, solvent effects and the thermodynamic cycles used for its calculation.

In this work, we proposed a computational protocol for the calculation of SRPs of iron complexes by evaluating a wide range of density functionals for the electronic structure description, two implicit solvent models with varying radii and two thermodynamic cycles.

Results show that the M06L density functional in combination with the SMD solvation model and the isodesmic method provides good results compared with SRP experimental values for a set of iron complexes.

Finally, this protocol was applied to three Fe(2+/3+)-Aβ model systems involved in the development of Alzheimer's disease and the obtained SRP values are in good agreement with those reported previously by means of MP2 calculations.

Orjuela, Adrián L.,Núñez-Zarur, Francisco,Alí-Torres, Jorge, 2022, A computational protocol for the calculation of the standard reduction potential of iron complexes: application to Fe(2+/3+)-Aβ model systems relevant to Alzheimer's disease, The Royal Society of Chemistry

Partager

Source

Articles recommandés par ES/IODE IA

A comprehensive AI model development framework for consistent Gleason grading
intelligence cancer scanners pai artificial annotation pathologists approach model grading image prostate quality 0 images ai