Progress in computational protein design

Lippow, Shaun M.; Tidor, Bruce

doi:10.1016/j.copbio.2007.04.009

Cited by 193 publications

(168 citation statements)

References 49 publications

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“…[33][34][35]. These studies demonstrate that the precise constellation of catalytic residues and dynamic motions of a protein contribute greatly to the catalytic efficiency of an enzyme.…”

Section: Possible Applicationsmentioning

confidence: 91%

Using affinity chromatography to engineer and characterize pH‐dependent protein switches

et al. 2008

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Conformational changes play important roles in the regulation of many enzymatic reactions. Specific motions of side chains, secondary structures, or entire protein domains facilitate the precise control of substrate selection, binding, and catalysis. Likewise, the engineering of allostery into proteins is envisioned to enable unprecedented control of chemical reactions and molecular assembly processes. We here study the structural effects of engineered ionizable residues in the core of the glutathione-S-transferase to convert this protein into a pHdependent allosteric protein. The underlying rational of these substitutions is that in the neutral state, an uncharged residue is compatible with the hydrophobic environment. In the charged state, however, the residue will invoke unfavorable interactions, which are likely to induce conformational changes that will affect the function of the enzyme. To test this hypothesis, we have engineered a single aspartate, cysteine, or histidine residue at a distance from the active site into the protein. All of the mutations exhibit a dramatic effect on the protein's affinity to bind glutathione. Whereas the aspartate or histidine mutations result in permanently nonbinding or binding versions of the protein, respectively, mutant GST50C exhibits distinct pH-dependent GSH-binding affinity. The crystal structures of the mutant protein GST50C under ionizing and nonionizing conditions reveal the recruitment of water molecules into the hydrophobic core to produce conformational changes that influence the protein's active site. The methodology described here to create and characterize engineered allosteric proteins through affinity chromatography may lead to a general approach to engineer effector-specific allostery into a protein structure.

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“…[33][34][35]. These studies demonstrate that the precise constellation of catalytic residues and dynamic motions of a protein contribute greatly to the catalytic efficiency of an enzyme.…”

Section: Possible Applicationsmentioning

confidence: 91%

Using affinity chromatography to engineer and characterize pH‐dependent protein switches

et al. 2008

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“…Computational design is derivative of structure prediction and includes several steps optimizing structure for new protein folds, improving catalysis or increasing binding anity [34]. There are known several programs and modeling platform (to be explored, e.g.…”

Section: Strategy Of Computational Designmentioning

confidence: 99%

De Novo Designed Proteins - Perspective Materials for Nanotechnology

Grzyb

2012

Acta Phys. Pol. A

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The paper explores the eld of de novo protein design, as a source of material for eective hybrid nanostructures. Main design approaches, namely the intuitional and the computational strategy, are briey overviewed. The achievements in the eld are illustrated with several examples, starting from historical heme binding maquettes to novel non-natural enzymes. Separate paragraph covers the problem of designing peptides, which may act as anchor between biological and non-biological parts of nanostructures. The advantages of de novo designed proteins and still existing problems of the eld are discussed.

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“…So, to overcome these limitations it is essential to introduce many approximations. Strategies to limit the sampling include restricting the backbone and side-chain degrees of freedom [34,82]. In most protein design strategies, sampling is simplified by using a fixed backbone which is normally obtained from an experimentally determined protein structure [98] or a high quality homology model.…”

Section: Introductionmentioning

confidence: 99%

“…Sampling quality involves construction of the models together with the energy and scoring functions necessary to rank them and evaluate molecular interactions, topics extensively reviewed previously [63,[79][80][81][82][83][84][85][86][87][88][89][90][91][92]]. An energy function describes the internal energy of the protein and its interactions with the environment such as other proteins, substrates and solvent, aiming at reproducing the features of the folded protein [34,84,93]. The level of theory used in these and their parameters vary considerably but most implementations include bonded (bonds, angles and torsions), non-bonded terms (van der Waals and electrostatics) and solvent components.…”

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confidence: 99%

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Monza

Acebes

Lucas

et al. 2017

Directed Enzyme Evolution: Advances and Applications

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Directed evolution creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship The final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-319-50413-1_10/fulltext.html of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of art of molecular modeling, discussing their strengths, limitations and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques, and a synergic effort between simulations and experimental validation. IntroductionBiotechnology needs catalysts that can work under harsh conditions, catalyze a broad range of substrates, generate maximum amount of product, and tolerate changes in the environment. Enzymes, which are biodegradable and reusable catalysts [1], in addition to remarkable reaction rates, can work in environmentally friendly pH and temperature ranges, and display control over stereochemistry and regioselectivity which makes them ideal for many applications [2,3]. When thinking about enzymes, people normally associate them to expressions such as "perfect catalysts" or "outstanding reaction rate". In fact, there are examples of enzymes that catalyze reactions at extremely high rates such as triose phosphate isomerase, superoxide dismutase or carbonic anhydrase [4]. These are often limited only by the rate of ligand diffusion into the active site (diffusion-controlled rate). Nevertheless, an extensive analysis by Bar-Even et al., of nearly 2000 enzymes, showed that the median maximal turnover rate value over all measured enzymes is about 10 s −1 nowhere close to the values of 10 5 or 10 6 normally associated with catalysts [5,6].So, it would appear that natural enzymes are "just good enough" for the function they must perform in a given organism [7]. One might conclude that if they had evolved to their optimum performance then trying to improve them (from a kinetic point of view) would be attempting the impossible. On the contrary, as seen by the distribution of reaction rates, k cat , most enzymes function at a lower rate than the diffusion-limit and thus, there is space to further increase their kinetic properties to meet industrial needs. Additionally, we need enzymes capable of catalyzing reactions for which no known enzymes exist, to work with different substrates and for particular conditions that are industrially...

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Progress in computational protein design

Cited by 193 publications

References 49 publications

Using affinity chromatography to engineer and characterize pH‐dependent protein switches

Using affinity chromatography to engineer and characterize pH‐dependent protein switches

De Novo Designed Proteins - Perspective Materials for Nanotechnology

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

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