Effective Fragment Potentials for Flexible Molecules: Transferability of Parameters and Amino Acid Database

Abstract

An accurate but efficient description of noncovalent interactions is a key to predictive modeling of biological and materials systems. The effective fragment potential (EFP) is an ab initio-based force field that provides a physically meaningful decomposition of noncovalent interactions of a molecular system into Coulomb, polarization, dispersion, and exchange-repulsion components. An EFP simulation protocol consists of two steps, preparing parameters for molecular fragments by a series of ab initio calculations on each individual fragment, and calculation of interaction energy and properties of a total molecular system based on the prepared parameters. As the fragment parameters (distributed multipoles, polarizabilities, localized wave function, etc.) depend on a fragment geometry, straightforward application of the EFP method requires recomputing parameters of each fragment if its geometry changes, for example, during thermal fluctuations of a molecular system. Thus, recomputing fragment parameters can easily become both computational and human bottlenecks and lead to a loss of efficiency of a simulation protocol. An alternative approach, in which fragment parameters are adjusted to different fragment geometries, referred to as “flexible EFP”, is explored here. The parameter adjustment is based on translations and rotations of local coordinate frames associated with fragment atoms. The protocol is validated on extensive benchmark of amino acid dimers extracted from molecular dynamics snapshots of a cryptochrome protein. A parameter database for standard amino acids is developed to automate flexible EFP simulations in proteins. To demonstrate applicability of flexible EFP in large-scale protein simulations, binding energies and vertical electron ionization and electron attachment energies of a lumiflavin chromophore of the cryptochrome protein are computed. The results obtained with flexible EFP are in a close agreement with the standard EFP procedure but provide a significant reduction in computational cost.

Polarization energies in the fragment molecular orbital method

“…Many equations above formulated for DFT are used unchanged for DFTB (namely, Equations 4,6,11,12,14,17,18,19,and 22), so that only DFTB-specific terms are derived below. The embedding for DFTB has a different form: point charges with a distance-dependent γ function, compared to the density in DFT, divided by distance.…”

“…2,3 One route to obtaining detailed properties is via localized molecules orbitals 4,5,6 ; another is via molecular fragments. 7,8,9,10,11,12,13,14,15,16 Properties of fragments can be obtained, such as interaction energies, which can be decomposed into components. 17,18,19,20 The fragment molecular orbital (FMO) method 21,22,23 has been used both as a low-scaling way of obtaining total properties such as binding energies 24 or electron densities, 25,26 and a convenient scheme for computing the properties of fragments.…”

“…One route to obtaining detailed properties is via localized molecules orbitals 4,5,6 ; another is via molecular fragments 7,8,9,10,11,12,13,14,15,16 . Properties of fragments can be obtained, such as interaction energies, which can be decomposed into components 17,18,19,20 …”

The catalytic activity and adsorption in faujasite and ZSM-5 zeolites: the role of differential stabilization and charge delocalization

“…15 To reduce the cost of QM calculations, various low scaling approaches 16 have been proposed, including fragment-based approaches. [17][18][19][20][21][22][23][24][25][26][27] Another route to reducing the cost is to use modern parametrized QM methods, 28 such as densityfunctional tight-binding (DFTB). 29 The fragment molecular orbital (FMO) method [30][31][32][33] has been used mainly for biochemical applications, 34 although some FMO calculations of a cluster model of adsorption on crystal surfaces have been reported, [35][36][37][38] including an evaluation of the accuracy of FMO fragmentation for faujasite zeolite 39,40 and mesoporous silica.…”

“…To reduce the cost of QM calculations, various low scaling approaches 16 have been proposed, including fragment-based approaches. 17–27 Another route to reducing the cost is to use modern parametrized QM methods, 28 such as density-functional tight-binding (DFTB). 29…”