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.
Highlights
IntroductionOccurring bioactive peptides such as amyloid peptides, antimicrobial peptides, cell penetration peptides, and fusion peptides play various biological roles (e.g. hormones, enzyme substrates and inhibitors, neurotransmitters, drugs and antibiotics, and self-assembly building blocks) in regulating various biological processes and metabolisms [1,2,3]
Occurring bioactive peptides such as amyloid peptides, antimicrobial peptides, cell penetration peptides, and fusion peptides play various biological roles in regulating various biological processes and metabolisms [1,2,3]
Since each natural or non-natural amino acid is represented by 6 NNAAIndex factors, the sequence and structural features of any peptide can be characterized by constructing a 66n NNAAIndex matrix, where n is the number of amino acids
Summary
Occurring bioactive peptides such as amyloid peptides, antimicrobial peptides, cell penetration peptides, and fusion peptides play various biological roles (e.g. hormones, enzyme substrates and inhibitors, neurotransmitters, drugs and antibiotics, and self-assembly building blocks) in regulating various biological processes and metabolisms [1,2,3]. Most of these native peptides suffer from poor bioavailability and poor proteolytic stability, which greatly limit their in vitro and in vivo applications To address these limitations, using the existing peptides as structural templates and high-throughput screening approaches together with combinatorial library and analogue chemistry synthesis have been widely used to brute-force search and systematically design new stable and active peptide mimetics [4]. Cell-phage and mirror-phage approaches in combination with mutationgenetics are powerful high-throughput techniques to screen and identify active peptides and to construct combinatorial synthetic peptide libraries These approaches have produced a number of FDA-approved peptide-based drugs including ACE inhibitors, HIV protease inhibitors, and cancer immunotherapeutics [3,8]. These experimental screening approaches provide little structural and binding information of designed peptides, which often lead to irrational design and many inactive compounds
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