FFLUX : Transferability of polarizable machine-learned electrostatics in peptide chains
© 2017 Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 38(2017), 13 vom: 15. Mai, Seite 1005-1014 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2017
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't QTAIM atomic charge force field machine learning peptides quantum chemical topology transferability Amino Acids mehr... |
| Zusammenfassung: | © 2017 Wiley Periodicals, Inc. The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a kriging model. Such a knowledgeable atom needs to be informed about a sufficiently large environment around it. The resulting complexity can be tackled by collecting the 20 natural amino acids into a few groups. Using substituted deca-alanines, we present the proof-of-concept that a given atom's charge can be modeled by a few kriging models only. © 2017 Wiley Periodicals, Inc |
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| Beschreibung: | Date Completed 13.05.2019 Date Revised 13.05.2019 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.24775 |