Literature DB >> 11539835

Multipole correction of atomic monopole models of molecular charge distribution. I. Peptides.

W A Sokalski1, D A Keller, R L Ornstein, R Rein.   

Abstract

The defects in atomic monopole models of molecular charge distribution have been analyzed for several model-blocked peptides and compared with accurate quantum chemical values. The results indicate that the angular characteristics of the molecular electrostatic potential around functional groups capable of forming hydrogen bonds can be considerably distorted within various models relying upon isotropic atomic charges only. It is shown that these defects can be corrected by augmenting the atomic point charge models by cumulative atomic multipole moments (CAMMs). Alternatively, sets of off-center atomic point charges could be automatically derived from respective multipoles, providing approximately equivalent corrections. For the first time, correlated atomic multipoles have been calculated for N-acetyl, N'-methylamide-blocked derivatives of glycine, alanine, cysteine, threonine, leucine, lysine, and serine using the MP2 method. The role of the correlation effects in the peptide molecular charge distribution are discussed.

Entities:  

Keywords:  NASA Discipline Exobiology; Non-NASA Center

Mesh:

Substances:

Year:  1993        PMID: 11539835     DOI: 10.1002/jcc.540140812

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  4 in total

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Authors:  Dejun Lin
Journal:  J Chem Phys       Date:  2015-09-21       Impact factor: 3.488

2.  Libraries of atomic multipole moments for precise modeling of electrostatic properties of amino acids.

Authors:  W Sokalski
Journal:  Amino Acids       Date:  1994-02       Impact factor: 3.520

Review 3.  Biomolecular electrostatics and solvation: a computational perspective.

Authors:  Pengyu Ren; Jaehun Chun; Dennis G Thomas; Michael J Schnieders; Marcelo Marucho; Jiajing Zhang; Nathan A Baker
Journal:  Q Rev Biophys       Date:  2012-11       Impact factor: 5.318

4.  Multipolar electrostatics based on the Kriging machine learning method: an application to serine.

Authors:  Yongna Yuan; Matthew J L Mills; Paul L A Popelier
Journal:  J Mol Model       Date:  2014-03-16       Impact factor: 1.810

  4 in total

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