Realistic sampling of amino acid geometries for a multipolar polarizable force field.

The Quantum Chemical Topological Force Field (QCTFF) uses the machine learning method kriging to map atomic multipole moments to the coordinates of all atoms in the molecular system. It is important that kriging operates on relevant and realistic training sets of molecular geometries. Therefore, we sampled single amino acid geometries directly from protein crystal structures stored in the Protein Databank (PDB).

Multipolar electrostatics.

Atomistic simulation of chemical systems is currently limited by the elementary description of electrostatics that atomic point-charges offer. Unfortunately, a model of one point-charge for each atom fails to capture the anisotropic nature of electronic features such as lone pairs or π-systems. Higher order electrostatic terms, such as those offered by a multipole moment expansion, naturally recover these important electronic features.

Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging.

As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging.

Visceral fat accumulation and postdexamethasone serum cortisol levels in patients with adrenal incidentaloma.

Objective: Visceral fat is a significant cardiovascular risk factor. Because visceral fat has not been measured systematically in patients with adrenal incidentalomas, we have tested the hypothesis that visceral fat volume may be associated with cutoffs for serum cortisol levels post dexamethasone.

Design: This was a retrospective, cross-sectional study.