Periodic Reporting for period 1 - EnzVolNet (COMPUTATIONAL EVOLUTION OF ENZYME VARIANTS THROUGH CONFORMATIONAL NETWORKS)
Reporting period: 2017-05-01 to 2019-04-30
Natural enzymes have evolved to perform their functions under complex selective pressures, being capable of accelerating reactions by several orders of magnitude. In particular, heteromeric enzyme complexes catalyze an enormous array of useful reactions that are often allosterically regulated by different protein partners. Unfortunately, the underlying physical principles of this regulation are still under debate, which makes the alteration of enzyme structure towards useful isolated subunits a tremendous challenge for modern chemical biology. Exploitation of isolated enzyme subunits, however, is advantageous for biosynthetic applications as it reduces the metabolic stress on the host cell and greatly simplifies efforts to engineer specific properties of the enzyme. Current approaches to alter natural enzyme complexes are based on the evaluation of thousands of variants, which make them economically unviable and the resulting catalytic efficiencies lag far behind their natural counterparts. The revolutionary nature of EnzVolNet relies on the application of conformational network models (e.g Markov State Models) to extract the essential functional protein dynamics and key conformational states, reducing the complexity of the enzyme design paradigm and completely reformulating previous computational design approaches. This new strategy will be applied to develop stand-alone enzymes from heteromeric protein complexes, with advantageous biosynthetic properties and improve activity and substrate scope.
Previous work by Dr. Sílvia Osuna, supervisor of the current proposal among others, showed that explicit water MD simulations provide a greater discriminative power than analysis of static structures alone, provided by X-ray crystallography or quantum calculations. Nevertheless, a general limitation of MD simulations is the lack of sufficient phase-space sampling due to limited computational resources, which is especially the case for systems with a huge number of degrees of freedom like enzymes. Starting this previously generated simulation data, massive parallel independent trajectories were ran for each LovD system studied to reconstruct long time scale processes. Afterwards, a MSM was generated for each LovD system and check for convergence. A Hidden Markov models (HMM) was used to optimize both, the state decomposition and the transition probabilities between states, for optimal construction of the MSM. The Graphics Processing Unit (GPU)-based cluster at the host organization was used, together with the GPU version of the AMBER16 package, and the PyEMMA (http://pyemma.org(opens in new window)) software for the generation, analysis, and validation of the Markov models. MSM unravelled the thermodynamics and kinetics of complex conformational rearrangements in wild-type and LovD variants, characterizing the enzyme most populated conformational states, as well as the kinetics for interconversion. Identification of active HMM state was performed by monitoring LovD active site distances, in particular the Lys79-Tyr188 distance. Distance analysis revealed and stabilization of the active HMM macrocluster, as the DE process evolved (WT -> LovD1 -> LovD3 -> LovD6 -> LovD9). Therefore, mutations introduced in LovD along the DE process exerted an stabilisation effect on its active conformations, increasing the population of the conformations preactivated for catalysis.
Markov State Models were also applied to the second proposed enzyme model, TrpS, a more complex α2β2 heterodimer enzyme system catalyzing the condensation of indole and L-serine to form L-tryptophan. In this case, MSM failed to reproduce the conformational dynamics of the open-to-closed COMM domain transition leading to improved catalysis in the evolved variants. Therefore, the contingency plan based on metadynamics was applied. In particular, metadynamics in combination with path-collective variables were used to recover the free-energy profiles associated with the TrpS and TrpS0B2 evolved variant open-to-closed COMM domain transition, at different reaction intermediates. Similar to LovD, energy profiles revealed an stabilisation or population shift towards closed/active COMM domain conformations as the enzymatic reaction progresses. Moreover, mutations introduced in the stand-alone TrpS0B2 variant were able to retain and improve the conformational plasticity observed in the WT TrpS, thus explaining the higher catalytic activity of the stand-alone variant.
The second research goal was to develop new computational tools to identify which residues affect the enzyme active site conformational dynamics, unveiling the most important positions for stand-alone enzyme activity and substrate scope. Computational methods, especially Molecular Dynamics (MD) simulations, are particularly useful to recover the motions of enzymes critical for its function. However, the large number of degrees of freedom present in biomacromolecules, including enzymes, hampers the extraction of essential mo-tions. In this work, statistical or machine-learning techniques were used to learn functional relationships from MD simulation data without requiring a detailed model of the un-derlying physics or biological relations. In particular, a series of machine learning methods (Random Forest, logistic regression, support vector machines, xgboost classifier, etc.) were used to find new patterns from MD simulation data. Feature extraction from the best classification model lead to an understanding of the conformational rearrangements along the experimental directed evolution process. Finally, data gathered from previous work packages lead to the proposal of new point mutations for LovD and TrpB to test experimentally for improved catalytic activity.
Markov State Models were also applied to the second proposed enzyme model, TrpS, a more complex α2β2 heterodimer enzyme system catalyzing the condensation of indole and L-serine to form L-tryptophan. In this case, MSM failed to reproduce the conformational dynamics of the open-to-closed COMM domain transition leading to improved catalysis in the evolved variants. Therefore, the contingency plan based on metadynamics was applied. In particular, metadynamics in combination with path-collective variables were used to recover the free-energy profiles associated with the TrpS and TrpS0B2 evolved variant open-to-closed COMM domain transition, at different reaction intermediates. Similar to LovD, energy profiles revealed an stabilisation or population shift towards closed/active COMM domain conformations as the enzymatic reaction progresses. Moreover, mutations introduced in the stand-alone TrpS0B2 variant were able to retain and improve the conformational plasticity observed in the WT TrpS, thus explaining the higher catalytic activity of the stand-alone variant.
The second research goal was to develop new computational tools to identify which residues affect the enzyme active site conformational dynamics, unveiling the most important positions for stand-alone enzyme activity and substrate scope. Computational methods, especially Molecular Dynamics (MD) simulations, are particularly useful to recover the motions of enzymes critical for its function. However, the large number of degrees of freedom present in biomacromolecules, including enzymes, hampers the extraction of essential mo-tions. In this work, statistical or machine-learning techniques were used to learn functional relationships from MD simulation data without requiring a detailed model of the un-derlying physics or biological relations. In particular, a series of machine learning methods (Random Forest, logistic regression, support vector machines, xgboost classifier, etc.) were used to find new patterns from MD simulation data. Feature extraction from the best classification model lead to an understanding of the conformational rearrangements along the experimental directed evolution process. Finally, data gathered from previous work packages lead to the proposal of new point mutations for LovD and TrpB to test experimentally for improved catalytic activity.
This proposal was highly innovative, as it was aimed at increasing our understanding of the effect of beneficial mutations, especially those far from the active site, on the network of enzyme/biocatalyst motions and allosteric regulation. Advances in our understanding of such processes provide means to modulate an control enzyme function and to develop new biocatalysts able to accelerate arbitrary chemical transformations. Biocatalysts accelerate chemical transformations in a specific and selective manner, reducing pollution (CO2 emissions) and costs, and creating a greater sustainability. Industries are starting to use biocatalysts to replace transition metal catalysts. For instance, MERCK is using a transaminase for the manufacture of their best-selling drug called Januvia®. However, the development of biocatalysts relevant for industrial processes requires the experimental test of many variants. In this proposal, a novel computational protocol for the design of new stand-alone biocatalysts with improved desired properties was proposed. The application of this novel strategy, based on the combination of MSM and machine learning techniques, can be used to thoroughly investigate new enzyme systems with available DE studies and to unveil the basis for their improved catalysis and to propose new mutation sites to generate variants with improved properties.
The development of new efficient computational protocols to design active biocatalyst/enzyme mutants for a given reaction would reduce the experimental costs of the directed evolution process, as fewer designs would have to be tested, and therefore impact the EU industry competitiveness.
The development of new efficient computational protocols to design active biocatalyst/enzyme mutants for a given reaction would reduce the experimental costs of the directed evolution process, as fewer designs would have to be tested, and therefore impact the EU industry competitiveness.