We are analyzing https://link.springer.com/article/10.1007/s00726-008-0177-8.

Title:
Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides | Amino Acids
Description:
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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Keywords {🔍}

article, google, scholar, cas, pubmed, amino, chem, qsar, analysis, protein, gonzálezdíaz, acid, peptides, peptide, acids, uriarte, information, quantitative, structureactivity, prediction, structure, med, sci, data, partial, molina, multivariate, descriptors, chemistry, comput, wold, privacy, cookies, content, fasgai, relationship, pls, sets, access, model, squares, recognition, inf, studies, res, indices, modeling, chongqing, publish, research,

Topics {✒️}

quantitative structure–activity relationships quantitative structure–activity relationship month download article/chapter potential anti-tumor atcun wiley-vch verlag gmbh oliveira-dos-santos aj angiotensin-converting enzyme 2 stable protein mutants protein kinases based characteristic parameters related structure representation method amino acid descriptors multivariate sequence-property analysis related subjects de la riva full article pdf squares genetic algorithm-partial privacy choices/manage cookies amino acid attributes factor analysis scales generalized linear model amino acid indices protein structure proteinogenic amino acids preprocessing peptide sequences high characterization competence amino acids characterization exploratory factor analysis bitter tasting threshold straightforward physicochemical information author information authors structure prediction chemical descriptors relevant protein kinase recognition pls models developed european economic area scope submit manuscript give slightly superior effective representation technique vazquez-padrón ri d-optimal designs genetic algorithm optimization calcium channel antagonists ms-whim scores innovative group program bradykinin potentiating peptides biologically active peptides biological activity conditions privacy policy comprehensive data source

Schema {🗺️}

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         description: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.
         datePublished:2008-09-28T00:00:00Z
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         Factor analysis scales of generalized amino acid information
         Quantitative structure–activity relationship
         Partial least squares
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         general
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         Biochemical Engineering
         Life Sciences
         Proteomics
         Neurobiology
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