Literature DB >> 15895431

A new set of amino acid descriptors and its application in peptide QSARs.

Hu Mei1, Zhi H Liao, Yuan Zhou, Shengshi Z Li.   

Abstract

In this work, a new set of amino acid descriptors, i.e., VHSE (principal components score Vectors of Hydrophobic, Steric, and Electronic properties), is derived from principal components analysis (PCA) on independent families of 18 hydrophobic properties, 17 steric properties, and 15 electronic properties, respectively, which are included in total 50 physicochemical variables of 20 coded amino acids. Using the stepwise multiple regression (SMR) method combined with partial least squares (PLS), the VHSE scales are then applied to QSAR studies of bitter-tasting dipeptides (BTD), angiotensin-converting enzyme (ACE) inhibitors, and bradykinin-potentiating pentapeptides (BPP). To validate the predictive power of resulting models, external validation are also performed. A comparison of the results to those obtained with z scores and other two-dimensional (2D) or three-dimensional(3D) descriptors shows that the VHSE scales are comparable for parameterizing the structural variability of the peptide series. Copyright 2005 Wiley Periodicals, Inc.

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Year:  2005        PMID: 15895431     DOI: 10.1002/bip.20296

Source DB:  PubMed          Journal:  Biopolymers        ISSN: 0006-3525            Impact factor:   2.505


  27 in total

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3.  Artificial neural network study on organ-targeting peptides.

Authors:  Eunkyoung Jung; Junhyoung Kim; Seung-Hoon Choi; Minkyoung Kim; Hokyoung Rhee; Jae-Min Shin; Kihang Choi; Sang-Kee Kang; Nam Kyung Lee; Yun-Jaie Choi; Dong Hyun Jung
Journal:  J Comput Aided Mol Des       Date:  2009-12-18       Impact factor: 3.686

4.  Modulating and evaluating receptor promiscuity through directed evolution and modeling.

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5.  Identification of tissue-specific targeting peptide.

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6.  Quantitative structure-activity relationship analysis of canonical inhibitors of serine proteases.

Authors:  Daniele Dell'orco; Pier Giuseppe De Benedetti
Journal:  J Comput Aided Mol Des       Date:  2008-01-23       Impact factor: 3.686

7.  Modeling the QSAR of ACE-Inhibitory Peptides with ANN and Its Applied Illustration.

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Review 8.  Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.

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9.  A survey of quantitative descriptions of molecular structure.

Authors:  Rajarshi Guha; Egon Willighagen
Journal:  Curr Top Med Chem       Date:  2012       Impact factor: 3.295

10.  Activity determinants of helical antimicrobial peptides: a large-scale computational study.

Authors:  Yi He; Themis Lazaridis
Journal:  PLoS One       Date:  2013-06-12       Impact factor: 3.240

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