Studying Peptides Biological Activities Based on Multidimensional Descriptors (E) Using Support Vector Regression

Yin, Jinling; Diao, Youxiang; Wen, Zhining; Wang, Zhimeng; Li, Menglong

doi:10.1007/s10989-010-9210-3

Cited by 13 publications

(15 citation statements)

References 30 publications

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“…For example, in the “FASGAI” descriptor set, nine principle components were used in this study while three were reported in previous study [24]. Moreover, some studies applied other methods such as MLR [15] and SVM [35] to build their QSAR models, which may also contribute to different results.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Study of Bitter Di-, Tri- and Tetrapeptides Using Integrated Descriptors

Chung

2019

Molecules

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New quantitative structure–activity relationship (QSAR) models for bitter peptides were built with integrated amino acid descriptors. Datasets contained 48 dipeptides, 52 tripeptides and 23 tetrapeptides with their reported bitter taste thresholds. Independent variables consisted of 14 amino acid descriptor sets. A bootstrapping soft shrinkage approach was utilized for variable selection. The importance of a variable was evaluated by both variable selecting frequency and standardized regression coefficient. Results indicated model qualities for di-, tri- and tetrapeptides with R2 and Q2 at 0.950 ± 0.002, 0.941 ± 0.001; 0.770 ± 0.006, 0.742 ± 0.004; and 0.972 ± 0.002, 0.956 ± 0.002, respectively. The hydrophobic C-terminal amino acid was the key determinant for bitterness in dipeptides, followed by the contribution of bulky hydrophobic N-terminal amino acids. For tripeptides, hydrophobicity of C-terminal amino acids and the electronic properties of the amino acids at the second position were important. For tetrapeptides, bulky hydrophobic amino acids at N-terminus, hydrophobicity and partial specific volume of amino acids at the second position, and the electronic properties of amino acids of the remaining two positions were critical. In summary, this study not only constructs reliable models for predicting the bitterness in different groups of peptides, but also facilitates better understanding of their structure-bitterness relationships and provides insights for their future studies.

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Section: Discussionmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Study of Bitter Di-, Tri- and Tetrapeptides Using Integrated Descriptors

Chung

2019

Molecules

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“…Their model [17] successfully predicted ( R 2 = 0.97) the bitterness of 48 dipeptides ( R 2 values calculated using ((A.6)) of appendix). Table 1 (28 models taken from a reference [17] + 2 other models) summarizes the developed models for dipeptides along with their prediction errors (root mean square errors (RMSE) which were calculated using ((A.1)) of the appendix).…”

Section: Introductionmentioning

confidence: 99%

“…Yin et al [ 17 ] reported 28 developed models in the year 2010 for modeling dipeptide bitterness in comparison with their own model which used E 1 –E 5 amino acid variables (hydrophobicity (E 1 ), steric properties or side chain bulk/molecular size (E 2 ), preferences for amino acids to occur in α -helices (E 3 ), composition (E 4 ), and the net charge (E 5 )) to develop QSBR models using support vector regression (SVM). Their model [ 17 ] successfully predicted ( R 2 = 0.97) the bitterness of 48 dipeptides ( R 2 values calculated using ( (A.6) ) of appendix). Table 1 (28 models taken from a reference [ 17 ] + 2 other models) summarizes the developed models for dipeptides along with their prediction errors (root mean square errors (RMSE) which were calculated using ( (A.1) ) of the appendix).…”

Section: Introductionmentioning

confidence: 99%

QSBR Study of Bitter Taste of Peptides: Application of GA-PLS in Combination with MLR, SVM, and ANN Approaches

Soltani

Haghaei

Shayanfar

et al. 2013

BioMed Research International

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Detailed information about the relationships between structures and properties/activities of peptides as drugs and nutrients is useful in the development of drugs and functional foods containing peptides as active compounds. The bitterness of the peptides is an undesirable property which should be reduced during drug/nutrient production, and quantitative structure bitter taste relationship (QSBR) studies can help researchers to design less bitter peptides with higher target efficiency. Calculated structural parameters were used to develop three different QSBR models (i.e., multiple linear regression, support vector machine, and artificial neural network) to predict the bitterness of 229 peptides (containing 2–12 amino acids, obtained from the literature). The developed models were validated using internal and external validation methods, and the prediction errors were checked using mean percentage deviation and absolute average error values. All developed models predicted the activities successfully (with prediction errors less than experimental error values), whereas the prediction errors for nonlinear methods were less than those for linear methods. The selected structural descriptors successfully differentiated between bitter and nonbitter peptides.

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“… successfully generalized this scale to 87 kinds of amino acids including coded amino acids. In the recent year, a set of descriptors E for peptide QSAR was proposed from the five PCs on the basis of a multidimensional scaling of 237 physical–chemical properties of the natural amino acid side chain . A new series of amino acid indices based on quantum topological molecular similarity (QTMS) descriptors were proposed for peptide QSAR studies.…”

Section: Introductionmentioning

confidence: 99%

A descriptor of amino acids SVWG and its applications in peptide QSAR

Tong

Yang

Liu

et al. 2012

Journal of Chemometrics

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A descriptor, SVWG (principal component scores vector of WHIM (weighted holistic invariant molecular index) descriptors and GETWAY (GEometry, Topology, and Atom‐Weights AssemblY) descriptors), was derived from a principal component analysis of a matrix of two structural variables of coded amino acid. SVWG scales were then applied in three panels of peptide quantitative structure activity relationship (QSAR) that were molded by partial least squares regression. The obtained models with the correlation coefficient (Rcum2) and cross‐validation correlation coefficient (QLOO2) were 0.893 and 0.830 for angiotensin‐converting enzyme inhibitors, 0.990 and 0.968 for antimicrobial peptides, and 0.955 and 0.908 for oxytocin, respectively. Satisfactory results showed that information related to biological activity can be systemically expressed by SVWG scales, which may be a useful structural expression methodology for study on peptide QSAR. Copyright © 2012 John Wiley & Sons, Ltd.

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Studying Peptides Biological Activities Based on Multidimensional Descriptors (E) Using Support Vector Regression

Cited by 13 publications

References 30 publications

Quantitative Structure–Activity Relationship Study of Bitter Di-, Tri- and Tetrapeptides Using Integrated Descriptors

Quantitative Structure–Activity Relationship Study of Bitter Di-, Tri- and Tetrapeptides Using Integrated Descriptors

QSBR Study of Bitter Taste of Peptides: Application of GA-PLS in Combination with MLR, SVM, and ANN Approaches

A descriptor of amino acids SVWG and its applications in peptide QSAR

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