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Cheriyan M Walters MJ Kang BD Anzaldi LL Toone EJ Fierke CA 《Bioorganic & medicinal chemistry》2011,19(21):6447-6453
The use of biological catalysts for industrial scale synthetic chemistry is highly attractive, given their cost effectiveness, high specificity that obviates the need for protecting group chemistry, and the environmentally benign nature of enzymatic procedures. Here we evolve the naturally occurring 2-keto-3-deoxy-6-phosphogluconate (KDPG) aldolases from Thermatoga maritima and Escherichia coli, into enzymes that recognize a nonfunctionalized electrophilic substrate, 2-keto-4-hydroxyoctonoate (KHO). Using an in vivo selection based on pyruvate auxotrophy, mutations were identified that lower the K(M) value up to 100-fold in E. coli KDPG aldolase, and that enhance the efficiency of retro-aldol cleavage of KHO by increasing the value of k(cat)/K(M) up to 25-fold in T. maritima KDPG aldolase. These data indicate that numerous mutations distal from the active site contribute to enhanced 'uniform binding' of the substrates, which is the first step in the evolution of novel catalytic activity. 相似文献
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Background
Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used. 相似文献7.
Background
Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring. 相似文献8.
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