Work

Addressing Long‐Standing Challenges in Computational Enzymology With Large QM ‐Cluster Models of the [Ni, Fe]‐Hydrogenase Proton Transfer

Year: 2025

Type: article

Source: Journal of Computational Chemistry

DOI: 10.1002/jcc.70234

Authors: Tejaskumar A. Suhagia, Qianyi Cheng, Thomas J. Summers, Makenzie C. Griffing, Nathan J. DeYonker

Institution: University of Memphis

Language: English

Topic: Metalloenzymes and iron-sulfur proteins

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: closed

Funders: National Science Foundation, U.S. Department of Energy, University of Memphis

Awards: Graduate Research Fellowship Porgram (GRFP), CAREER: Innovation: The Three R's: A Model-Building Toolkit for Rational, Reproducible, and Rigorous Computational Enzymology, NEW THEORY AND ONTOLOGIES FOR QUANTUM MECHANICAL CLUSTER MODELING OF PROTEINS AND ENZYMES

OpenAlex ID: W4414998302

FWCI: 0.139

Cites: 87

Cited by: 1

Related to: