An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics |
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| Authors: | Guizhao Liang Yonglan Liu Bozhi Shi Jun Zhao Jie Zheng |
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| Institution: | 1. Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College Chongqing University, Chongqing, China.; 2. Department of Chemical and Biomolecular Engineering, The University of Akron, Akron, Ohio, United States of America.; Russian Academy of Sciences, Institute for Biological Instrumentation, Russian Federation, |
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| Abstract: | 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. |
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