Literature DB >> 19785474

Do quantum mechanical energies calculated for small models of protein-active sites converge?

LiHong Hu1, Jenny Eliasson, Jimmy Heimdal, Ulf Ryde.   

Abstract

A common approach for the computational modeling of enzyme reactions is to study a rather small model of the active site (20-200 atoms) with quantum mechanical (QM) methods, modeling the rest of the surroundings by a featureless continuum with a dielectric constant of approximately 4. In this paper, we discuss how the residues included in the QM model should be selected and how many residues need to be included before reaction energies converge. As a test case, we use a proton-transfer reaction between a first-sphere cysteine ligand and a second-sphere histidine group in the active site of [Ni,Fe] hydrogenase. We show that it is not a good approach to add groups according to their distance to the active site. A better approach is to add groups according to their contributions to the QM/MM energy difference. However, the energies can still vary by up to 50 kJ/mol for QM systems of sizes up to 230 atoms. In fact, the QM-only approach is based on the hope that a large number of sizable contributions will cancel. Interactions with neutral groups are, in general, short-ranged, with net energy contributions of less than 4 kJ/mol at distances above 5 A from the active site. Interactions with charged groups are much more long-ranged, and interactions with buried charges 20 A from the active site can still contribute by 5 kJ/mol to the reaction energy. Thus, to accurately model the influence of the surroundings on enzyme reaction energies, a detailed and unbiased atomistic account of the surroundings needs to be included.

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Year:  2009        PMID: 19785474     DOI: 10.1021/jp9029024

Source DB:  PubMed          Journal:  J Phys Chem A        ISSN: 1089-5639            Impact factor:   2.781


  17 in total

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Journal:  Phys Chem Chem Phys       Date:  2018-08-08       Impact factor: 3.676

3.  Protonation states of intermediates in the reaction mechanism of [NiFe] hydrogenase studied by computational methods.

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Authors:  Martin R Hediger; Luca De Vico; Julie B Rannes; Christian Jäckel; Werner Besenmatter; Allan Svendsen; Jan H Jensen
Journal:  PeerJ       Date:  2013-08-29       Impact factor: 2.984

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7.  Mapping enzymatic catalysis using the effective fragment molecular orbital method: towards all ab initio biochemistry.

Authors:  Casper Steinmann; Dmitri G Fedorov; Jan H Jensen
Journal:  PLoS One       Date:  2013-04-12       Impact factor: 3.240

8.  Modeling catalytic promiscuity in the alkaline phosphatase superfamily.

Authors:  Fernanda Duarte; Beat Anton Amrein; Shina Caroline Lynn Kamerlin
Journal:  Phys Chem Chem Phys       Date:  2013-06-03       Impact factor: 3.676

9.  Cheminformatic quantum mechanical enzyme model design: A catechol-O-methyltransferase case study.

Authors:  Thomas J Summers; Qianyi Cheng; Manuel A Palma; Diem-Trang Pham; Dudley K Kelso; Charles Edwin Webster; Nathan J DeYonker
Journal:  Biophys J       Date:  2021-08-04       Impact factor: 3.699

10.  A machine learning correction for DFT non-covalent interactions based on the S22, S66 and X40 benchmark databases.

Authors:  Ting Gao; Hongzhi Li; Wenze Li; Lin Li; Chao Fang; Hui Li; LiHong Hu; Yinghua Lu; Zhong-Min Su
Journal:  J Cheminform       Date:  2016-05-03       Impact factor: 5.514

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