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WIREs Comput Mol Sci
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The challenge of predicting distal active site mutations in computational enzyme design

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Abstract Many computational enzyme design approaches have been developed in recent years that focus on a reduced set of key enzymatic features. Initial protocols mostly focused on the chemical steps(s) through transition state stabilization, whereas most recent approaches exploit the enzyme conformational dynamics often crucial for substrate binding, product release, and allosteric regulation. The detailed evaluation of the conformational landscape of many laboratory‐evolved enzymes has revealed dramatic changes on the relative stabilities of the conformational states after mutation, favoring those conformational states key for the novel functionality. Of note is that these mutations are often located all around the enzyme structure, which contrasts with most of the computational design strategies that reduce the problem into active site alterations. Recent computational strategies have been developed that consider enzyme design as a population shift problem, that is, redistribution of the relative stabilities of the conformational states induced by mutations. These strategies focus on reconstructing the conformational landscape of the enzyme, applying correlation‐based tools to elucidate the underlying allosteric network of interactions and identify potential mutation hotspots located at the active site, but most importantly at distal positions for the first time. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Software > Molecular Modeling
Tryptophan synthase (TrpS) conformational landscapes and SPM comparison to directed evolution hotspots. (a) The reconstructed conformational landscape of: the allosterically‐regulated TrpS complex, the isolated TrpB unit, and laboratory‐evolved stand‐alone TrpB0B2. TrpB0B2 recovers the allosterically‐driven conformational ensemble (open (O), partially‐closed (PC), and closed (C) states present). The closed state is more accessible than in TrpS, which explains the higher catalytic activity of the evolved variant. (b). Representation of the open (dark blue), partially‐closed (teal), and closed (light blue) conformation of the COMM domain from X‐ray data. The mutations introduced with DE are shown in blue spheres. (c). SPM identifies key positions for COMM domain conformational dynamics, which include two positions targeted with DE (P12L and E17G), three positions (Y301, D300, and Y69) are making persistent interactions with DE residues (F274S, T292S, and I68V), and only T321A is nor in the path neither makes stable interactions
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MAO‐N conformational landscape and SPM analysis along DE pathway. (a). Representation of MAO‐N homodimeric structure: the flexible β‐hairpin of both monomers is in the X‐ray (closed) conformation and is shown in raspberry, FAD cofactor in blue and sticks, and the positions introduced with DE for generating MAO‐N D5 variant are shown in spheres. (b). Zoom of the β‐hairpin conformation in the open state. (c). Reconstructed free energy landscape with Markov State Model (MSM) for MAO‐N WT. Each conformational state: open, closed, and partially closed is shown with a weighted sphere according to the relative populations, and arrows denote the conversions between states and the associated timescales. (d). Computed SPM on MAO‐N D5. The five mutations introduced with DE for generating MAO‐N D5 from WT are all situated in an adjacent position to the SPM path (shown in purple) and making favorable interactions with SPM residues, except M348K (shown in yellow orange) that is far away from the path. The four additional mutations for generating MAO‐N D9 from D5 are all of them at adjacent SPM positions (shown in pink), except the active site W430G mutation that is deviated from the path (shown in green). The SPM of MAO‐N WT is also displayed, which shows how that the communication between subunits is altered along the DE pathway
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Retro‐aldolase SPM comparison to directed evolution (DE) mutation hotspots. (a). Thirteen mutations were introduced via DE, among which seven are included in SPM (shown in blue spheres), four are located at an adjacent position (purple spheres), and only two are deviated more than six positions in sequence (yellow). (b). SPM of RA95.5–8. (c). Conformational landscape of one of the intermediate RA variants that presents moderate RA activity. Active states presenting short distances (in Å) between the base and the alcohol of the Schiff‐base intermediate are sampled (highlighted with a green tick), and inactive states (long distances, in Å, marked with a red cross) are displayed
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Developed shortest path map (SPM) for enzyme design. The protein is represented with a weighted graph as done in previous allosteric studies,112 however, the initial graph is further simplified by means of Dijkstra algorithm to characterize the shortest paths. The generated SPM is then drawn on the 3D structure
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Schematic representation of the population shift concept on the free energy landscape induced by mutations toward novel functionalities. The conformational landscape of wild‐type (left) and variant (right) differ in the relative stabilities of the conformational states, being the ones that are functionally relevant for the novel activity stabilized in the variant due to the introduced mutations (located at the active site and/or distal)
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Enzyme free energy landscape (FEL) and timescales of conformational changes key for enzymatic activity. In the scheme, energy minima named Conf 1 (blue) and Conf 2 (red) are composed by multiple related conformations that can be interconverted in the ps‐ns timescale, for instance by side‐chain rotations. The conformational transition from Conf 1 to Conf 2 has a higher barrier, and thus a slower associated timescale (μs‐ms), for instance allosteric transitions or domain motions
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Key features of enzyme catalysis that should be optimized for novel function. In many enzymes, the chemical step(s) are rate‐limiting, still conformational dynamics play a crucial role for substrate binding, product release and allosteric regulation. By introducing mutations, conformational change can be rate‐limiting (such as the case of variant 2). The main computational tools that can be used for describing the chemical step(s) or the conformational dynamics are also detailed
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Selected examples of laboratory‐evolved enzymes with directed evolution: Phosphotriesterase (PTE) to arylesterase (AE) conversion,27 Tryptophan Synthase (TrpS) for stand‐alone activity,38 Monoamine Oxidase (MAO‐N) for broadening substrate scope,39 and Retro‐aldolase (RA) for enhanced activity.10,31 The enzyme activity along the rounds of evolution is shown in blue (right y‐axis), and the mean distance between the Cα of the introduced mutations with respect to the Cα of the key catalytic residue in orange (and in Å, left y‐axis). The distance from the active site of each mutation is marked with a sphere
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Software > Molecular Modeling
Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods
Structure and Mechanism > Computational Biochemistry and Biophysics

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