An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics

An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics

Bioactive peptides and peptidomimetics play a pivotal role in the regulation of many biological processes such as cellular apoptosis, host defense, and biomineralization. In this work, we develop a novel structural matrix, Index of Natural and Non-natural Amino Acids (NNAAIndex), to systematically c...

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Main Authors: Liang, Guizhao, Liu, Yonglan, Shi, Bozhi, Zhao, Jun, Zheng, Jie
Format: Online
Language:English
Published: Public Library of Science 2013
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3720802/
id pubmed-3720802
recordtype oai_dc
spelling pubmed-37208022013-08-09 An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics Liang, Guizhao Liu, Yonglan Shi, Bozhi Zhao, Jun Zheng, Jie Research Article Bioactive peptides and peptidomimetics play a pivotal role in the regulation of many biological processes such as cellular apoptosis, host defense, and biomineralization. In this work, we develop a novel structural matrix, Index of Natural and Non-natural Amino Acids (NNAAIndex), to systematically characterize a total of 155 physiochemical properties of 22 natural and 593 non-natural amino acids, followed by clustering the structural matrix into 6 representative property patterns including geometric characteristics, H-bond, connectivity, accessible surface area, integy moments index, and volume and shape. As a proof-of-principle, the NNAAIndex, combined with partial least squares regression or linear discriminant analysis, is used to develop different QSAR models for the design of new peptidomimetics using three different peptide datasets, i.e., 48 bitter-tasting dipeptides, 58 angiotensin-converting enzyme inhibitors, and 20 inorganic-binding peptides. A comparative analysis with other QSAR techniques demonstrates that the NNAAIndex method offers a stable and predictive modeling technique for in silico large-scale design of natural and non-natural peptides with desirable bioactivities for a wide range of applications. Public Library of Science 2013-07-23 /pmc/articles/PMC3720802/ /pubmed/23935845 http://dx.doi.org/10.1371/journal.pone.0067844 Text en © 2013 Liang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Liang, Guizhao
Liu, Yonglan
Shi, Bozhi
Zhao, Jun
Zheng, Jie
spellingShingle Liang, Guizhao
Liu, Yonglan
Shi, Bozhi
Zhao, Jun
Zheng, Jie
An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics
author_facet Liang, Guizhao
Liu, Yonglan
Shi, Bozhi
Zhao, Jun
Zheng, Jie
author_sort Liang, Guizhao
title An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics
title_short An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics
title_full An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics
title_fullStr An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics
title_full_unstemmed An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics
title_sort index for characterization of natural and non-natural amino acids for peptidomimetics
description Bioactive peptides and peptidomimetics play a pivotal role in the regulation of many biological processes such as cellular apoptosis, host defense, and biomineralization. In this work, we develop a novel structural matrix, Index of Natural and Non-natural Amino Acids (NNAAIndex), to systematically characterize a total of 155 physiochemical properties of 22 natural and 593 non-natural amino acids, followed by clustering the structural matrix into 6 representative property patterns including geometric characteristics, H-bond, connectivity, accessible surface area, integy moments index, and volume and shape. As a proof-of-principle, the NNAAIndex, combined with partial least squares regression or linear discriminant analysis, is used to develop different QSAR models for the design of new peptidomimetics using three different peptide datasets, i.e., 48 bitter-tasting dipeptides, 58 angiotensin-converting enzyme inhibitors, and 20 inorganic-binding peptides. A comparative analysis with other QSAR techniques demonstrates that the NNAAIndex method offers a stable and predictive modeling technique for in silico large-scale design of natural and non-natural peptides with desirable bioactivities for a wide range of applications.
publisher Public Library of Science
publishDate 2013
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3720802/
_version_ 1611997233412046848

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