Web-ARM

Web-ARM automates construction and analysis of QM/MM models of rhodopsins to enable prediction and study of their photophysical properties.


Key Features:

  • Automated Model Construction: Utilizes the python-based a-ARM protocol to generate QM/MM models from crystallographic structures or comparative models provided in PDB format.
  • Excitation Energy Calculations: Computes excitation energies at the CASPT2//CASSCF/Amber level of theory to predict trends in UV-vis absorption maximum wavelengths.
  • Mutant and Variant Screening Support: Provides rapid model generation to support screening of rhodopsin mutants and variants for photophysical property prediction.

Scientific Applications:

  • Structural and Functional Analysis: Enables exploration of the structural and functional dynamics of rhodopsins, including the effects of mutations on protein behavior.
  • Photophysical Prediction and Color Tuning: Supports prediction of UV-vis absorption maxima and investigation of rhodopsin color tuning mechanisms.
  • Mutant Screening and Variant Design: Facilitates screening and comparison of rhodopsin mutants and variants to identify candidates with desired photophysical properties.
  • Education and Training: Provides practical, model-based exercises for teaching concepts related to photochemistry and rhodopsin function.

Methodology:

Automated generation of QM/MM models via the python-based a-ARM protocol from crystallographic structures or comparative models in PDB format, and computation of excitation energies at the CASPT2//CASSCF/Amber level to predict UV-vis absorption maxima.

Topics

Details

Programming Languages:
Python
Added:
1/18/2021
Last Updated:
3/14/2021

Operations

Publications

Pedraza-González L, Marín MDC, Jorge AN, Ruck TD, Yang X, Valentini A, Olivucci M, De Vico L. Web-ARM: A Web-Based Interface for the Automatic Construction of QM/MM Models of Rhodopsins. Journal of Chemical Information and Modeling. 2020;60(3):1481-1493. doi:10.1021/acs.jcim.9b00615. PMID:31909998. PMCID:PMC7101466.

PMID: 31909998
PMCID: PMC7101466
Funding: - National Science Foundation: CHE-CLP-1710191 - Universit?? degli Studi di Siena: Assegno Premiale 2017 - Ministero dell???Istruzione, dell???Universit?? e della Ricerca: Dipartimento di Eccellenza 2018 - 2022 - Fonds De La Recherche Scientifique - FNRS: T.0132.16 - U.S. Department of Health and Human Services: GM126627 01