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      OptZyme: computational enzyme redesign using transition state analogues.

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          Abstract

          OptZyme is a new computational procedure for designing improved enzymatic activity (i.e., kcat or kcat/KM) with a novel substrate. The key concept is to use transition state analogue compounds, which are known for many reactions, as proxies for the typically unknown transition state structures. Mutations that minimize the interaction energy of the enzyme with its transition state analogue, rather than with its substrate, are identified that lower the transition state formation energy barrier. Using Escherichia coli β-glucuronidase as a benchmark system, we confirm that KM correlates (R(2) = 0.960) with the computed interaction energy between the enzyme and the para-nitrophenyl- β, D-glucuronide substrate, kcat/KM correlates (R(2) = 0.864) with the interaction energy of the transition state analogue, 1,5-glucarolactone, and kcat correlates (R(2) = 0.854) with a weighted combination of interaction energies with the substrate and transition state analogue. OptZyme is subsequently used to identify mutants with improved KM, kcat, and kcat/KM for a new substrate, para-nitrophenyl- β, D-galactoside. Differences between the three libraries reveal structural differences that underpin improving KM, kcat, or kcat/KM. Mutants predicted to enhance the activity for para-nitrophenyl- β, D-galactoside directly or indirectly create hydrogen bonds with the altered sugar ring conformation or its substituents, namely H162S, L361G, W549R, and N550S.

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          Most cited references38

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          Protein structure prediction and structural genomics.

          Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics.
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            Kemp elimination catalysts by computational enzyme design.

            The design of new enzymes for reactions not catalysed by naturally occurring biocatalysts is a challenge for protein engineering and is a critical test of our understanding of enzyme catalysis. Here we describe the computational design of eight enzymes that use two different catalytic motifs to catalyse the Kemp elimination-a model reaction for proton transfer from carbon-with measured rate enhancements of up to 10(5) and multiple turnovers. Mutational analysis confirms that catalysis depends on the computationally designed active sites, and a high-resolution crystal structure suggests that the designs have close to atomic accuracy. Application of in vitro evolution to enhance the computational designs produced a >200-fold increase in k(cat)/K(m) (k(cat)/K(m) of 2,600 M(-1)s(-1) and k(cat)/k(uncat) of >10(6)). These results demonstrate the power of combining computational protein design with directed evolution for creating new enzymes, and we anticipate the creation of a wide range of useful new catalysts in the future.
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              Protein structure modeling with MODELLER.

              Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of similar protocols (correction of protcols) has resulted in models of useful accuracy for domains in more than half of all known protein sequences.
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                Author and article information

                Journal
                PLoS ONE
                PloS one
                Public Library of Science (PLoS)
                1932-6203
                1932-6203
                2013
                : 8
                : 10
                Affiliations
                [1 ] Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
                Article
                PONE-D-13-22331
                10.1371/journal.pone.0075358
                3792102
                24116038
                35d42387-2f43-414a-b29c-a8fa7f43b581
                History

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