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Struct Funct Genet (struct funct + genet)
Selected AbstractspKa optimized catalysis in serine proteinases, an ab initio study on the catalaytic HisINTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 11 2007Péter Hudáky Abstract First principle models of catalytic apparatus of enzymes can be used for studying stability as well as the atomic details of a catalytic mechanism. For example, the catalytic triad of chymotrypsin was recently investigated by using an ab initio geometry optimized (Hudáky and Perczel, Proteins: Struct Funct Genet, 2006, 62, 749) self-stabilizing molecule ensemble without the presence of the complete enzyme and substrate. Several parameters of the above catalytic reaction turned out to be the same within the model and the in vitro enzymatic reaction. Among the numerous parameters of the catalytic process geometrical changes of the catalytic histidine was investigated here and the variation of its pKa value was determined. A relatively large range, 3.5 unit, was determined as the variation of pKa as function of the conformational subspace available in serine proteases. Comparing PDB structures of the free and the complex enzymes it was shown, that histidine, after accepting the proton from the OH group of the catalytic serine, undergoes a minor conformational change accompanied by a 2.5 unit decrease in pKa. We conclude that the changes of pKa during catalysis are predominantly determined by the geometrical arrangement of the histidine moiety and this change serves as a significant driving force in the catalytic process. © 2007 Wiley Periodicals, Inc. Int J Quantum Chem, 2007 [source] Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide componentsJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 9 2007Hao Lin Abstract The proteins structure can be mainly classified into four classes: all-,, all - ,, ,/,, and , + , protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007 [source] Using pseudo amino acid composition to predict protein structural classes: Approached with complexity measure factorJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 4 2006Xuan Xiao Abstract The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. Based on the concept of the pseudo amino acid composition [Chou, K. C. Proteins Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60], a novel approach for measuring the complexity of a protein sequence was introduced. The advantage by incorporating the complexity measure factor into the pseudo amino acid composition as one of its components is that it can catch the essence of the overall sequence pattern of a protein and hence more effectively reflect its sequence-order effects. It was demonstrated thru the jackknife crossvalidation test that the overall success rate by the new approach was significantly higher than those by the others. It has not escaped our notice that the introduction of the complexity measure factor can also be used to improve the prediction quality for, among many other protein attributes, subcellular localization, enzyme family class, membrane protein type, and G-protein couple receptor type. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 478,482, 2006 [source] | ||||||||||