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      Insights from molecular dynamics simulations for computational protein design

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          Abstract

          A grand challenge in the field of structural biology is to design and engineer proteins that exhibit targeted functions.

          Abstract

          A grand challenge in the field of structural biology is to design and engineer proteins that exhibit targeted functions. Although much success on this front has been achieved, design success rates remain low, an ever-present reminder of our limited understanding of the relationship between amino acid sequences and the structures they adopt. In addition to experimental techniques and rational design strategies, computational methods have been employed to aid in the design and engineering of proteins. Molecular dynamics (MD) is one such method that simulates the motions of proteins according to classical dynamics. Here, we review how insights into protein dynamics derived from MD simulations have influenced the design of proteins. One of the greatest strengths of MD is its capacity to reveal information beyond what is available in the static structures deposited in the Protein Data Bank. In this regard simulations can be used to directly guide protein design by providing atomistic details of the dynamic molecular interactions contributing to protein stability and function. MD simulations can also be used as a virtual screening tool to rank, select, identify, and assess potential designs. MD is uniquely poised to inform protein design efforts where the application requires realistic models of protein dynamics and atomic level descriptions of the relationship between dynamics and function. Here, we review cases where MD simulations were used to modulate protein stability and protein function by providing information regarding the conformation(s), conformational transitions, interactions, and dynamics that govern stability and function. In addition, we discuss cases where conformations from protein folding/unfolding simulations have been exploited for protein design, yielding novel outcomes that could not be obtained from static structures.

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          Most cited references 190

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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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            Intrinsic dynamics of an enzyme underlies catalysis.

            A unique feature of chemical catalysis mediated by enzymes is that the catalytically reactive atoms are embedded within a folded protein. Although current understanding of enzyme function has been focused on the chemical reactions and static three-dimensional structures, the dynamic nature of proteins has been proposed to have a function in catalysis. The concept of conformational substates has been described; however, the challenge is to unravel the intimate linkage between protein flexibility and enzymatic function. Here we show that the intrinsic plasticity of the protein is a key characteristic of catalysis. The dynamics of the prolyl cis-trans isomerase cyclophilin A (CypA) in its substrate-free state and during catalysis were characterized with NMR relaxation experiments. The characteristic enzyme motions detected during catalysis are already present in the free enzyme with frequencies corresponding to the catalytic turnover rates. This correlation suggests that the protein motions necessary for catalysis are an intrinsic property of the enzyme and may even limit the overall turnover rate. Motion is localized not only to the active site but also to a wider dynamic network. Whereas coupled networks in proteins have been proposed previously, we experimentally measured the collective nature of motions with the use of mutant forms of CypA. We propose that the pre-existence of collective dynamics in enzymes before catalysis is a common feature of biocatalysts and that proteins have evolved under synergistic pressure between structure and dynamics.
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              De novo computational design of retro-aldol enzymes.

              The creation of enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Using new algorithms that rely on hashing techniques to construct active sites for multistep reactions, we designed retro-aldolases that use four different catalytic motifs to catalyze the breaking of a carbon-carbon bond in a nonnatural substrate. Of the 72 designs that were experimentally characterized, 32, spanning a range of protein folds, had detectable retro-aldolase activity. Designs that used an explicit water molecule to mediate proton shuffling were significantly more successful, with rate accelerations of up to four orders of magnitude and multiple turnovers, than those involving charged side-chain networks. The atomic accuracy of the design process was confirmed by the x-ray crystal structure of active designs embedded in two protein scaffolds, both of which were nearly superimposable on the design model.
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                Author and article information

                Journal
                MSDEBG
                Molecular Systems Design & Engineering
                Mol. Syst. Des. Eng.
                Royal Society of Chemistry (RSC)
                2058-9689
                2017
                2017
                : 2
                : 1
                : 9-33
                Affiliations
                [1 ]Department of Bioengineering
                [2 ]University of Washington
                [3 ]Seattle
                [4 ]USA
                Article
                10.1039/C6ME00083E
                © 2017
                Product
                Self URI (article page): http://xlink.rsc.org/?DOI=C6ME00083E

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