A new descriptor of amino acids based on the three-dimensional vector of atomic interaction field

Zhou, Peng; Zhou, Yuan; Shi-rong, WU; Ли, Бо; Tian, Fengchun; Li, Zhiliang

doi:10.1007/s11434-006-0524-7

Cited by 20 publications

(15 citation statements)

References 34 publications

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“…It has been widely used by Hellberg et al [13]. Besides, data set of dipeptides, a typical sample set for QSAR studies [3,5,13,15,17,[28][29][30][31], is often utilized to test effectiveness of diverse kinds of amino acid descriptors. For a set of peptide analogues, the chemical structure can now be quantified by describing each varied (Table 5), it was indicated that the QSAR model on ACE inhibition was stable and generalized.…”

Section: Results and Analysis Qsar Model For Angiotensin Converting Ementioning

confidence: 99%

SVEEVA Descriptor Application to Peptide QSAR

Tong

Che

Liu

et al. 2011

Archiv der Pharmazie

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A new descriptor, SVEEVA (principal component scores vector of electronic eigenvalue descriptors), was derived from principal component analysis (PCA) of a matrix of 220 electronic eigenvalue descriptors of coded amino acids. SVEEVA scales were then applied in three panels of peptide quantitative structure-activity relationship (QSAR) that were modeled by partial least squares regression (PLS). The obtained models with the correlation coefficient (R²(cum), cross-validation correlation coefficient (Q²(LOO) were 0.894 and 0.839 for 58 angiotensin-converting enzyme inhibitors, 0.995 and 0.949 for 12 antimicrobial polypeptides, and 0.995 and 0.976 for 20 thromboplastin inhibitors. Satisfactory results showed that information related to biological activity can be systemically expressed by SVEEVA scales, which may be a useful structural expression methodology for study on peptide QSAR.

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Section: Results and Analysis Qsar Model For Angiotensin Converting Ementioning

confidence: 99%

SVEEVA Descriptor Application to Peptide QSAR

Tong

Che

Liu

et al. 2011

Archiv der Pharmazie

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“…[37][38][39] All three datasets have been utilized previously in a number of QSAR analysis or for the development of novel descriptors for amino acids. Tropsha et al had utilized the bradykinin-potentiating pentapeptides for the development of a novel strategy for rational design of targeted peptide libraries.…”

Section: Datasetsmentioning

confidence: 99%

“…35,37,[39][40][41]44 Tables 8-10 draw a comparison between the eQSAR models and the published literature for the respective three datasets. For the bradykinin-potentiating pentapeptides, the regression coefficient (r 2 ), and crossvalidation coefficient (q 2 ) are comparable with the Z-scales method by Hellberg et al, and the WHIM descriptors-based statistics.…”

Section: Comparative Studies Of Eqsar Model Statistics With Other Pubmentioning

confidence: 99%

Ensemble QSAR: A QSAR method based on conformational ensembles and metric descriptors

et al. 2011

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Quantitative structure-activity relationship (QSAR) is the most versatile tool in computer-assisted molecular design. One conceptual drawback seen in QSAR approaches is the "one chemical-one structure-one parameter value" dogma where the model development is based on physicochemical description for a single molecular conformation, while ignoring the rest of the conformational space. It is well known that molecules have several low-energy conformations populated at physiological temperature, and each conformer makes a significant impact on associated properties such as biological activity. At the level of molecular interaction, the dynamics around the molecular structure is of prime essence rather than the average structure. As a step toward understanding the role of these discrete microscopic states in biological activity, we have put together a theoretically rigorous and computationally tractable formalism coined as eQSAR. In this approach, the biological activity is modeled as a function of physicochemical description for a selected set of low-energy conformers, rather than that's for a single lowest energy conformation. Eigenvalues derived from the "Physicochemical property integrated distance matrices" (PD-matrices) that encompass both 3D structure and physicochemical properties, have been used as descriptors; is a novel addition. eQSAR is validated on three peptide datasets and explicitly elaborated for bradykinin-potentiating peptides. The conformational ensembles were generated by a simple molecular dynamics and consensus dynamics approaches. The eQSAR models are statistically significant and possess the ability to select the most biologically relevant conformation(s) with the relevant physicochemical attributes that have the greatest meaning for description of the biological activity.

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“…Through simulating structural characteristics of active site to combine within angiotensin I, ACE inhibitors can arrive at the aim of competitively inhibiting effective bioactivity of ACE. As a classical dataset, 58 dipeptides of ACE inhibitors have ever been used to test validity for new types of amino acids descriptors [2,9,[37][38][39][40][41][42] . The primary sequence and bioactivity, expressed with pIC 50 (see supplymenteny materials Table A5 for electronic version), of this dataset are taken from reference [37] Owing genetic algorithm (GA) being a stochastically searching algorithm, the computational results correspond definite uncertainty.…”

Section: Angiotensin Converting Enzyme Inhibitorsmentioning

confidence: 99%

A novel vector of topological and structural information for amino acids and its QSAR applications for peptides and analogues

Shu

et al. 2008

Sci. China Ser. B-Chem.

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A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/ QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting enzyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK) , the R 2 , cross-validation R 2 , and root mean square error of estimation (RMSEE) were as follows: ACEI, R 2 cu ≥0.82, Q 2 cu ≥0.77, E rmse ≤ 0.44 (GPLS+SVM); ESCK, R 2 cu ≥0.84, Q 2 cu ≥0.82, E rmse ≤0.20 (GPLS+INN), respectively.vector of topological and structural information for coded and noncoded amino acids (VTSA), peptide quantitative structure activity relationship (pQSAR), molecular structural characterizing descriptors (MSCD), quantitative sequence activity modelings (QSAMs), angiotensin converting enzyme inhibitors (ACEI), elastase substrate catalyzed kinetics (ESCK)

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A new descriptor of amino acids based on the three-dimensional vector of atomic interaction field

Cited by 20 publications

References 34 publications

SVEEVA Descriptor Application to Peptide QSAR

SVEEVA Descriptor Application to Peptide QSAR

Ensemble QSAR: A QSAR method based on conformational ensembles and metric descriptors

A novel vector of topological and structural information for amino acids and its QSAR applications for peptides and analogues

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