Papers by Keyword: Tertiary Structure

Abstract: In this paper we intend to apply a new method to predict tertiary structure. A novel hybrid feature adopted is composed of physicochemical composition (PCC), recurrence quantification analysis (RQA) and pseudo amino acid composition (PseAA). We use the Error Correcting Output Coding (ECOC) based on three flexible neural tree models as the classifiers. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 60.23%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.

Abstract: In this paper we intend to apply a new method to predict tertiary structure. Several feature extraction methods adopted are physicochemical composition, recurrence quantification analysis (RQA) , pseudo amino acid composition (PseAA) and Distance frequency. We construct the binary tree Classification model, and adopt flexible neural tree models as the classifiers. We will train a number of based classifiers through different features extraction methods for every node of binary tree, then employ the selective ensemble method to ensemble them. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 63.58%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.

Abstract: Aspergillus niger can produce an acid-stable α-amylase as well as an acid-unstable one, the relation between the structural differences and their acid-resistant capability is of great interest. In this paper, the primary parameters, secondary and tertiary structures of these two types of α-amylases was analyzed and compared, the results shows that they has 67% sequence identity and high homology in secondary and tertiary structure. The acid-stable α-amylase has lower isoelectric point and higher molar percent of acidic amino acid than the acid-unstable one. The four major structural differences of these two types of enzymes is located at residues 132-138, 206-212, 329-335 and 378-382 and formed vary fold pattern.

Abstract: α-Glucosidases play critical role both in primary metabolism and in glycoconjugate biosynthesis and processing. In this paper, the reasonable three-dimensional molecular model of AglA was generated by homology modeling. This modeled protein is divided into five major structural domains, and the catalytic domain is classical (β/α) 8 barrel with the active site pocket positioned at its C-terminal side. With analyses of conserved residues and overlay of homology structures, the residues Tyr 662, Tyr527, Glu521, His238 and Tyr235 was predicted as the main substrate binding sites, and residues Asp490, Glu493 and Asp660 were deduced to be the acid/base catalytic residues.