Streamlining and Optimizing Strategies of Electrostatic Parameterization

Abstract

Accurate characterization of electrostatic interactions is crucial in molecular simulation. Various methods and programs have been developed to obtain electrostatic parameters for additive or polarizable models to replicate electrostatic properties obtained from experimental measurements or theoretical calculations. Electrostatic potentials (ESPs), a set of physically well-defined observables from quantum mechanical (QM) calculations, are well suited for optimization efforts due to the ease of collecting a large amount of conformation-dependent data. However, a reliable set of QM ESP computed at an appropriate level of theory and atomic basis set is necessary. In addition, despite the recent development of the PyRESP program for electrostatic parameterizations of induced dipole-polarizable models, the time-consuming and error-prone input file preparation process has limited the widespread use of these protocols. This work aims to comprehensively evaluate the quality of QM ESPs derived by eight methods, including wave function methods such as Hartree–Fock (HF), second-order Møller–Plesset (MP2), and coupled cluster-singles and doubles (CCSD), as well as five hybrid density functional theory (DFT) methods, used in conjunction with 13 different basis sets. The highest theory levels CCSD/aug-cc-pV5Z (a5z) and MP2/aug-cc-pV5Z (a5z) were selected as benchmark data over two homemade data sets. The results show that the hybrid DFT method, ωB97X-D, combined with the aug-cc-pVTZ (a3z) basis set, performs well in reproducing ESPs while taking both accuracy and efficiency into consideration. Moreover, a flexible and user-friendly program called PyRESP_GEN was developed to streamline input file preparation. The restraining strengths, along with strategies for polarizable Gaussian multipole (pGM) model parameterizations, were also optimized. These findings and the program presented in this work facilitate the development and application of induced dipole-polarizable models, such as pGM models, for molecular simulations of both chemical and biological significance.

Assessment of Amino Acid Electrostatic Parametrizations of the Polarizable Gaussian Multipole Model

“…The components f e and f t represent distance-dependent damping functions that modify T ij to circumvent the problem termed the “polarization catastrophe”, where induced dipole moments become divergent due to cooperative induction between closely positioned dipoles. , Several damping schemes proposed by Thole have been integrated into the AMBER ff12pol force field. − However, Thole’s approaches have a drawback in that they solely address interactions between induced dipoles, leading to an inconsistent treatment of polarizations arising from fixed charges and permanent multipoles. Elking et al proposed an alternative damping scheme for modeling atomic electric multipoles using Gaussian density profiles, − which was later named the polarizable Gaussian Multipole (pGM) model. − The pGM model rectifies the limitation of Thole’s strategies by uniformly screening all short-range electrostatic interactions in a physically consistent manner, including interactions of charge–charge, charge–dipole, charge–quadrupole, dipole–dipole, etc. The formulas for damping functions f e and f t in the pGM model are as follows .25ex2ex S italicij = r ij 2 ( R i 2 + R j 2 ) f normale = erf false( S ij false) − 2 π S italicij exp false( prefix− S italicij 2 false) f normalt = erf false( S ij false) − 2 π S italicij exp false( prefix− S …”

“…Recently, the programs PyRESP_GEN and PyRESP have been developed for pGM model electrostatic parametrizations, , which extends the RESP program to accommodate both permanent and induced atomic dipoles and has been integrated into the AmberTools package. , An efficient simulation algorithm for the pGM model has also been incorporated into the SANDER program and is presently being adapted for utilization on parallel platforms through the PMEMD program. ,, Numerous previous studies have highlighted the efficacy of the pGM models in terms of accuracy in modeling molecular properties, including molecular polarizability anisotropy, ESPs surrounding molecules, molecular electric moments, interaction energies, and many-body interaction energies . Furthermore, the pGM model has been shown to possess transferability from training molecules and conformations to testing molecules (such as oligomers, molecule complexes, or polymers) and different conformations …”

“…Recently, the programs PyRESP_GEN and PyRESP have been developed for pGM model electrostatic parametrizations, 52,53 which extends the RESP program to accommodate both permanent and induced atomic dipoles and has been integrated into the AmberTools package. 54,55 An efficient simulation algorithm for the pGM model has also been incorporated into the SANDER program and is presently being adapted for utilization on parallel platforms through the PMEMD program.…”

“…46,61 Intramolecular equivalencing was applied in both stages. 52,53 In the first stage, the following five types of intramolecular equivalencing were enforced: First, for each tetrapeptide (ACE-ALA-X-ALA-NME) except when X is PRO, all four peptide bond atoms (−CO−NH−) were equivalenced. Second, due to the cyclic structure of proline, when X is PRO, only three intact peptide bond atoms (−CO−NH−) are present in the tetrapeptide and were equivalenced.…”

“…The recently developed PyRESP_GEN program was used to prepare input files for parametrizing molecules from each data set for each electrostatic model. 52 Due to the slow speed resulting from the fact that Python is an interpreted language, it is impractical to perform 40-conf fittings and 100-conf fittings described above using the PyRESP program. Therefore, an inhouse Fortran program named POLRESP that translated directly from the original PyRESP program was used to carry out all parametrization works.…”