Abstract
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 R2, cross-validation R2, and root mean square error of estimation (RMSEE) were as follows: ACEI, Rcu2⩾0.82, Qcu2⩾0.77, Ermse⩽0.44 (GPLS+SVM); ESCK, Rcu2⩾0.84, Qcu2⩾0.82, Ermse⩽0.20 (GPLS+INN), respectively.
Published Version
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