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A semiempirical method optimized for modeling proteins
CONTEXT: In recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific erro...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444645/ https://www.ncbi.nlm.nih.gov/pubmed/37608199 http://dx.doi.org/10.1007/s00894-023-05695-1 |
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author | Stewart, James J. P. Stewart, Anna C. |
author_facet | Stewart, James J. P. Stewart, Anna C. |
author_sort | Stewart, James J. P. |
collection | PubMed |
description | CONTEXT: In recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific errors have been found that could potentially cast doubt on the validity of these methods for modeling phenomena of biochemical interest such as enzyme catalytic mechanisms and protein-ligand interactions. To correct these and other errors, a new method specifically designed for use in organic and biochemical modeling has been developed. METHODS: Two alterations were made to the procedures used in developing the earlier PMx methods. A minor change was made to the theoretical framework, which affected only the non-quantum theory interatomic interaction function, while the major change involved changing the training set for optimizing parameters, moving the focus to systems of biochemical significance. This involved both the selection of reference data and the weighting factors, i.e., the relative importance that the various data were given. As a result of this change of focus, the accuracy in prediction of heats of formation, hydrogen bonding, and geometric quantities relating to non-covalent interactions in proteins was improved significantly. |
format | Online Article Text |
id | pubmed-10444645 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-104446452023-08-24 A semiempirical method optimized for modeling proteins Stewart, James J. P. Stewart, Anna C. J Mol Model Original Paper CONTEXT: In recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific errors have been found that could potentially cast doubt on the validity of these methods for modeling phenomena of biochemical interest such as enzyme catalytic mechanisms and protein-ligand interactions. To correct these and other errors, a new method specifically designed for use in organic and biochemical modeling has been developed. METHODS: Two alterations were made to the procedures used in developing the earlier PMx methods. A minor change was made to the theoretical framework, which affected only the non-quantum theory interatomic interaction function, while the major change involved changing the training set for optimizing parameters, moving the focus to systems of biochemical significance. This involved both the selection of reference data and the weighting factors, i.e., the relative importance that the various data were given. As a result of this change of focus, the accuracy in prediction of heats of formation, hydrogen bonding, and geometric quantities relating to non-covalent interactions in proteins was improved significantly. Springer Berlin Heidelberg 2023-08-22 2023 /pmc/articles/PMC10444645/ /pubmed/37608199 http://dx.doi.org/10.1007/s00894-023-05695-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Paper Stewart, James J. P. Stewart, Anna C. A semiempirical method optimized for modeling proteins |
title | A semiempirical method optimized for modeling proteins |
title_full | A semiempirical method optimized for modeling proteins |
title_fullStr | A semiempirical method optimized for modeling proteins |
title_full_unstemmed | A semiempirical method optimized for modeling proteins |
title_short | A semiempirical method optimized for modeling proteins |
title_sort | semiempirical method optimized for modeling proteins |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444645/ https://www.ncbi.nlm.nih.gov/pubmed/37608199 http://dx.doi.org/10.1007/s00894-023-05695-1 |
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