Literature DB >> 24851673

Fragment quantum mechanical calculation of proteins and its applications.

Xiao He1, Tong Zhu, Xianwei Wang, Jinfeng Liu, John Z H Zhang.   

Abstract

Conspectus The desire to study molecular systems that are much larger than what the current state-of-the-art ab initio or density functional theory methods could handle has naturally led to the development of novel approximate methods, including semiempirical approaches, reduced-scaling methods, and fragmentation methods. The major computational limitation of ab initio methods is the scaling problem, because the cost of ab initio calculation scales nth power or worse with system size. In the past decade, the fragmentation approach based on chemical locality has opened a new door for developing linear-scaling quantum mechanical (QM) methods for large systems and for applications to large molecular systems such as biomolecules. The fragmentation approach is highly attractive from a computational standpoint. First, the ab initio calculation of individual fragments can be conducted almost independently, which makes it suitable for massively parallel computations. Second, the electron properties, such as density and energy, are typically combined in a linear fashion to reproduce those for the entire molecular system, which makes the overall computation scale linearly with the size of the system. In this Account, two fragmentation methods and their applications to macromolecules are described. They are the electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method and the automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) approach. The EE-GMFCC method is developed from the MFCC approach, which was initially used to obtain accurate protein-ligand QM interaction energies. The main idea of the MFCC approach is that a pair of conjugate caps (concaps) is inserted at the location where the subsystem is divided by cutting the chemical bond. In addition, the pair of concaps is fused to form molecular species such that the overcounted effect from added concaps can be properly removed. By introducing the electrostatic embedding field in each fragment calculation and two-body interaction energy correction on top of the MFCC approach, the EE-GMFCC method is capable of accurately reproducing the QM molecular properties (such as the dipole moment, electron density, and electrostatic potential), the total energy, and the electrostatic solvation energy from full system calculations for proteins. On the other hand, the AF-QM/MM method was used for the efficient QM calculation of protein nuclear magnetic resonance (NMR) parameters, including the chemical shift, chemical shift anisotropy tensor, and spin-spin coupling constant. In the AF-QM/MM approach, each amino acid and all the residues in its vicinity are automatically assigned as the QM region through a distance cutoff for each residue-centric QM/MM calculation. Local chemical properties of the central residue can be obtained from individual QM/MM calculations. The AF-QM/MM approach precisely reproduces the NMR chemical shifts of proteins in the gas phase from full system QM calculations. Furthermore, via the incorporation of implicit and explicit solvent models, the protein NMR chemical shifts calculated by the AF-QM/MM method are in excellent agreement with experimental values. The applications of the AF-QM/MM method may also be extended to more general biological systems such as DNA/RNA and protein-ligand complexes.

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Year:  2014        PMID: 24851673     DOI: 10.1021/ar500077t

Source DB:  PubMed          Journal:  Acc Chem Res        ISSN: 0001-4842            Impact factor:   22.384


  21 in total

1.  Accurate Backbone 13 C and 15 N Chemical Shift Tensors in Galectin-3 Determined by MAS NMR and QM/MM: Details of Structure and Environment Matter.

Authors:  Jodi Kraus; Rupal Gupta; Manman Lu; Angela M Gronenborn; Mikael Akke; Tatyana Polenova
Journal:  Chemphyschem       Date:  2020-06-04       Impact factor: 3.102

2.  Toward Closing the Gap: Quantum Mechanical Calculations and Experimentally Measured Chemical Shifts of a Microcrystalline Lectin.

Authors:  Matthew Fritz; Caitlin M Quinn; Mingzhang Wang; Guangjin Hou; Xingyu Lu; Leonardus M I Koharudin; Tatyana Polenova; Angela M Gronenborn
Journal:  J Phys Chem B       Date:  2016-12-21       Impact factor: 2.991

3.  Solving the scalability issue in quantum-based refinement: Q|R#1.

Authors:  Min Zheng; Nigel W Moriarty; Yanting Xu; Jeffrey R Reimers; Pavel V Afonine; Mark P Waller
Journal:  Acta Crystallogr D Struct Biol       Date:  2017-11-30       Impact factor: 7.652

4.  Quantum mechanical force fields for condensed phase molecular simulations.

Authors:  Timothy J Giese; Darrin M York
Journal:  J Phys Condens Matter       Date:  2017-08-17       Impact factor: 2.333

5.  AFNMR: automated fragmentation quantum mechanical calculation of NMR chemical shifts for biomolecules.

Authors:  Jason Swails; Tong Zhu; Xiao He; David A Case
Journal:  J Biomol NMR       Date:  2015-08-02       Impact factor: 2.835

6.  Ab initio-enabled phase transition prediction of solid carbon dioxide at ultra-high temperatures.

Authors:  Lei Huang; Yanqiang Han; Xiao He; Jinjin Li
Journal:  RSC Adv       Date:  2019-12-24       Impact factor: 4.036

7.  Hydrogen-bond structure dynamics in bulk water: insights from ab initio simulations with coupled cluster theory.

Authors:  Jinfeng Liu; Xiao He; John Z H Zhang; Lian-Wen Qi
Journal:  Chem Sci       Date:  2017-12-04       Impact factor: 9.825

Review 8.  Recent Progress in Treating Protein-Ligand Interactions with Quantum-Mechanical Methods.

Authors:  Nusret Duygu Yilmazer; Martin Korth
Journal:  Int J Mol Sci       Date:  2016-05-16       Impact factor: 5.923

9.  Enhanced NMR Discrimination of Pharmaceutically Relevant Molecular Crystal Forms through Fragment-Based Ab Initio Chemical Shift Predictions.

Authors:  Joshua D Hartman; Graeme M Day; Gregory J O Beran
Journal:  Cryst Growth Des       Date:  2016-10-04       Impact factor: 4.076

10.  A quantum mechanical computational method for modeling electrostatic and solvation effects of protein.

Authors:  Xianwei Wang; Yang Li; Ya Gao; Zejin Yang; Chenhui Lu; Tong Zhu
Journal:  Sci Rep       Date:  2018-04-03       Impact factor: 4.379

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