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Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides

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Abstract

On the basis of exploratory factor analysis, six multidimensional patterns of 516 amino acid attributes, namely, factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, are proposed to represent structures of 48 bitter-tasting dipeptides and 58 angiotensin-converting enzyme inhibitors. Characteristic parameters related to bioactivities of the peptides studied are selected by genetic algorithm, and quantitative structure–activity relationship (QSAR) models are constructed by partial least square (PLS). Our results by a leave-one-out cross validation are compared with the previously known structure representation method and are shown to give slightly superior or comparative performance. Further, two data sets are divided into training sets and test sets to validate the characterization repertoire of FASGAI. Performance of the PLS models developed by training samples by a leave-one-out cross validation and external validation for test samples are satisfying. These results demonstrate that FASGAI is an effective representation technique of peptide structures, and that FASGAI vectors have many preponderant characteristics such as straightforward physicochemical information, high characterization competence and easy manipulation. They can be further applied to investigate the relationship between structures and functions of various peptides, even proteins.

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Abbreviations

FASGAI:

Factor analysis scales of generalized amino acid information

QSAR:

Quantitative structure–activity relationship

PLS:

Partial least squares

GA-PLS:

Genetic algorithm-partial least square

BTD:

Bitter-tasting dipeptide

ACE:

Angiotensin-converting enzyme

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Acknowledgments

We thank the reviewers for the constructive comments. This work was supported by the National high-tech Research Program (The “863” Program) (2006AA02Z312), National 111 Programme of Introducing Talents of Discipline to Universities (0507111106) and Innovative Group Program for Graduates of Chongqing University, Science and Innovation Fund (200711C1A0010260).

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Authors and Affiliations

  1. College of Bioengineering, Chongqing University, 400030, Chongqing, China

    G. Liang, L. Yang, L. Kang & H. Mei

  2. College of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China

    Z. Li

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  1. G. Liang
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  2. L. Yang
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  3. L. Kang
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  4. H. Mei
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Correspondence to G. Liang.

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Liang, G., Yang, L., Kang, L. et al. Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides. Amino Acids 37, 583–591 (2009). https://doi.org/10.1007/s00726-008-0177-8

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