Role of protonation states in stability of molecular dynamics simulations of high-resolution membrane protein structures

  • Classical molecular dynamics (MD) simulations provide unmatched spatial and time resolution of protein structure and function. However, accuracy of MD simulations often depends on the quality of force field parameters and the time scale of sampling. Another limitation of conventional MD simulations is that the protonation states of titratable amino acid residues remain fixed during simulations, even though protonation state changes coupled to conformational dynamics are central to protein function. Due to the uncertainty in selecting protonation states, classical MD simulations are sometimes performed with all amino acids modeled in their standard charged states at pH 7. Here we performed and analyzed classical MD simulations on high-resolution cryo-EM structures of two membrane proteins that transfer protons by catalyzing protonation/deprotonation reactions. In simulations performed with amino acids modeled in their standard protonation state the structure diverges far from its starting conformation. In comparison, MD simulations performed with pre-determined protonation states of amino acid residues reproduce the structural conformation, protein hydration, and protein-water and protein-protein interactions of the structure much better. The results suggest it is crucial to perform basic protonation state calculations, especially on structures where protonation changes play an important functional role, prior to launching any MD simulations. Furthermore, the combined approach of protonation state prediction and MD simulations can provide valuable information on the charge states of amino acids in the cryo-EM sample. Even though accurate prediction of protonation states currently remains a challenge, we introduce an approach of combining pKa prediction with cryo-EM density map analysis that helps in improving not only the protonation state predictions, but also the atomic modeling of density data.

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Metadaten
Author:Jonathan LashamORCiD, Amina DjurabekovaORCiD, Volker ZickermannORCiDGND, Janet VonckORCiD, Vivek SharmaORCiD
URN:urn:nbn:de:hebis:30:3-754204
URL:https://www.biorxiv.org/content/10.1101/2023.08.24.554589v1
DOI:https://doi.org/10.1101/2023.08.24.554589
Parent Title (English):bioRxiv
Publisher:bioRxiv
Document Type:Preprint
Language:English
Date of Publication (online):2023/08/25
Date of first Publication:2023/08/25
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2024/04/10
Issue:2023.08.24.554589 Version 1
Edition:Version 1
Page Number:24
HeBIS-PPN:517861763
Institutes:Medizin
Biochemie, Chemie und Pharmazie / Biochemie und Chemie
Angeschlossene und kooperierende Institutionen / MPI für Biophysik
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International