Prediction of conformationally dependent atomic multipole moments in carbohydrates

Abstract

The conformational flexibility of carbohydrates is challenging within the field of computational chemistry. This flexibility causes the electron density to change, which leads to fluctuating atomic multipole moments. Quantum Chemical Topology (QCT) allows for the partitioning of an “atom in a molecule,” thus localizing electron density to finite atomic domains, which permits the unambiguous evaluation of atomic multipole moments. By selecting an ensemble of physically realistic conformers of a chemical system, one evaluates the various multipole moments at defined points in configuration space. The subsequent implementation of the machine learning method kriging delivers the evaluation of an analytical function, which smoothly interpolates between these points. This allows for the prediction of atomic multipole moments at new points in conformational space, not trained for but within prediction range.In this work, we demonstrate that the carbohydrates erythrose and threose are amenable to the above methodology. We investigate how kriging models respond when the training ensemble incorporating multiple energy minima and their environment in conformational space. Additionally, we evaluate the gains in predictive capacity of our models as the size of the training ensemble increases. We believe this approach to be entirely novel within the field of carbohydrates. For a modest training set size of 600, more than 90% of the external test configurations have an error in the total (predicted) electrostatic energy (relative to ab initio) of maximum 1 kJ mol−1 for open chains and just over 90% an error of maximum 4 kJ mol−1 for rings. © 2015 Wiley Periodicals, Inc.

Polarizable multipolar electrostatics for cholesterol

“…An energy-minimized structure for cholesterol is calculated using GAUSSIAN03 42 at the B3LYP/apc-1 level 43 . This energy minimum is used as a reference point from which the in-house computer program TYCHE (written by group member Salvatore Cardamone) generates 2000 unique geometries via the molecule's normal modes of vibration 39,40 . No normal mode is distorted beyond 15 % of its original value found in the minimum energy geometry, a greater perturbation than commonly found in force field studies 44 .…”

Toward amino acid typing for proteins in FFLUX

“…[53] The resulting energy minimum is used as a template from which hundreds of distorted geometries are generated through the molecule's normal modes of vibration based on in-house methodology. [37,54] No bond distance or valence angle is distorted beyond 615% of its original value in the minimum energy geometry. This tripeptide (VAV) can then be converted to other tripeptides by substituting relevant sidechains to create the tripeptides VAA, AAA, GAA, and GAG.…”

“…[27,28] In the past we have shown FFLUX to be applicable to water clusters, [29,30] methanol, [31] N-methylacetamide [32] and all amino acids [33,34] including aromatic amino acids, [35] alanine helices, [36] and carbohydrates. [37] In previous publications, we have explored the concept of transferability using the machine learning method kriging [38] for small molecules and now turn our attention toward the challenge of predicting interatomic electrostatic interaction in proteins. In such systems, polarization has proven to be an important factor in hydrogen bonding [39][40][41][42][43] and structural determination, [44][45][46] and many groups work to incorporate these effects into modern force fields.…”

Accurate prediction of the energetics of weakly bound complexes using the machine learning method kriging

“…More precisely, FFLUX needs to be trained by a sufficient number of relevant geometries such that it can interpolate a property of a given atom of interest between the data learnt. The selected [12] machine learning method is Kriging [13], which has been tested successfully on a variety of systems, including ethanol [14], (peptide-capped) alanine [15], the microhydrated sodium ion [15], N-methylacetamide (NMA) and histidine [16], the four aromatic (peptidecapped) amino acids [17], all naturally occurring amino acids [18], helical deca-alanines [19,20], water clusters [21], cholesterol [22] and carbohydrates [23]. This collective work shows an existing proof-of-concept that kriging models generate sufficiently accurate atomic property models, and they do this directly from the coordinates of the surrounding atoms.…”