Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - NetMoDEzyme (Network models for the computational design of proficient enzymes)

Teaser

Billions of years of evolution have made enzymes superb catalysts capable of accelerating reactions by several orders of magnitude. The underlying physical principles of their extraordinary catalytic power still remains highly debated, which makes the alteration of natural...

Summary

Billions of years of evolution have made enzymes superb catalysts capable of accelerating reactions by several orders of magnitude. The underlying physical principles of their extraordinary catalytic power still remains highly debated, which makes the alteration of natural enzyme activities towards synthetically useful targets a tremendous challenge for modern chemical biology. The routine design of enzymes will, however, have large socio-economic benefits, as because of the enzymatic advantages the production costs of many drugs will be reduced and will allow industries to use environmentally friendly alternatives. The goal of this project is to make the routine design of proficient enzymes possible. Current computational and experimental approaches are able to confer natural enzymes new functionalities but are economically unviable and the catalytic efficiencies lag far behind their natural counterparts. The groundbreaking nature of NetMoDEzyme relies on the application of network models to reduce the complexity of the enzyme design paradigm and completely reformulate previous computational design approaches. The new protocol proposed accurately characterizes the enzyme conformational dynamics and customizes the included mutations by exploiting the correlated movement of the enzyme active site residues with distal regions. The guidelines for mutation are withdrawn from the costly directed evolution experimental technique, and the most proficient enzymes are easily identified via chemoinformatic models. The new strategy will be applied to develop proficient enzymes for the synthesis of enantiomerically pure β-blocker drugs for treating cardiovascular problems at a reduced cost. The experimental assays of our computational predictions will finally elucidate the potential of this genuinely new approach for mimicking Nature’s rules of evolution.

Work performed

NetMoDEzyme is organized in four subprojects. First, Markov State network models are applied to characterize the enzyme structure and dynamics thus identifying the enzyme’s most populated conformational states (S1). Second, network community analysis is performed on these states to determine the enzyme regions correlated to active site residues and substrate binding (S2). Third, the rules of operation of the laboratory-based DE are analyzed in terms of occurrence frequencies and evolutionary conservation, and a Sequence-Activity relationship are developed (S3). Finally, the new protocol has to be established (from the outcome of S1-3), applied, and validated in a superfamily of enzymes relevant for the potential applications in the synthesis of enantiomerically pure β-blockers (S4). The first three subprojects were originally thought to be applied in the industrially relevant enzymes engineered using ProSAR-based Directed Evolution (training set): Sitagliptin (Januvia®, wild-type and 4 variants), LovD (Zocor®, wild-type and 8 variants), and Atrovastatin (Lipitor®, wild-type and 4 variants). The first calculations performed on the wild-type (least active) and most evolved variants of Sitagliptin enzyme did not provide any reasonable explanation for the enhancement of catalytic activity, thus Sitagliptin was removed from the training set and monoamine oxidase (MAO) enzymes were considered instead. Of note is that both enzymes are important catalysts for chiral amine formation.

Hereafter, a summary of the main research achievements is detailed for each subproject:

Subproject S1. Markov State Models for characterizing the enzyme structure and dynamics
In this subproject, a new MSM-based approach has been developed for elucidating the enhancement in catalytic activity along Directed Evolution in the case of: LovD enzyme, Atrovastatin, and the new MAO-N enzyme replacing Sitagliptin. Since the start of the project, we have encountered many problems related to the functioning of the GPU-cluster purchased with this grant, which have been translated into a delay in some of the detailed tasks.

LovD training enzymes: We are now finishing the construction of the MSM based on the accumulated simulation time of 100 microseconds for each variant: WT, LovD1, LovD3, LovD6, and LovD9 (500 microseconds of accumulated simulation time). The MSM clearly shows how along the evolutionary process those conformational states presenting the catalytic triad in a proper conformation for catalysis are progressively stabilized (i.e. higher population of the catalytically active metastable states). All these simulations have been performed in the apo state (absence of substrate), and the catalytic activity of each state has been estimated by computing the mean distances between the catalytic residues on each metastable state of the MSM (as explained in the proposal). Additionally, and to further prove the enhancement in catalytic activity QM/MM-MD simulations are being performed to estimate the activation barrier corresponding to the first and second acylation step of the overall process. We expect to finish the MSM for LovD by the start of next year, and submit the results for publication in a high-profile journal.

Results presented in the following conferences:

Osuna, S. Molecular Dynamics Simulations of Designed and Evolved Enzymes, Invited lecture at Gordon Research Conference in Biocatalysis, University of New England, Biddeford, USA 2016.



Atrovastatin training enzymes: We generated the different variants produced along the experimental evolution with Rosetta based on the wild-type structure. Different X-ray structures are available for wild-type Halohydrin dehalogenase C (HheC) either as dimer or tetramer. Previous studies by the Janssen lab suggested that the enzyme is likely to be tetrameric in solution, however we did not have the experimental confirmation for wild-type and all variants generated along the evolutionary pathway. It sh

Final results

Our studies included in S1 and S2 provide evidence that Molecular Dynamics simulations, coupled to correlation-based tools similar to those used to investigate processes such as allosteric regulation and molecular recognition, can be successfully applied in the enzyme (re)design field. We have developed the Shortest Path Map (SPM), which analyzes the different conformational sub-states sampled along the MD trajectory and identifies which residues are important for the sub-state inter-conversion. Therefore, if catalytically competent states are sampled in the MD simulation, the new tool facilitates identification of residues that contribute to the inactive-to-active inter-conversion. SPM thus identifies both active site but also distal residues that could lead to a population shift toward the catalytically competent conformation for novel activity. This is totally unprecedented, and thus opens the door to new computational paradigms that are not restricted to active site alterations. We are currently developing additional strategies based on the analysis of possible hydrogen bond and non-covalent interactions of the identified SPM position and surrounding residues to propose specific aminoacid changes for novel function.

In the last period of this project, more focus will be put on the following subprojects:

Subproject S3. Statistical Structure-Activity Analysis for automatizing the protocol. This subproject aims to decipher DE rules of operation and develop a chemoinformatic model for ranking the generated mutants. It involves the following subprojects:
S3.1 Unveiling Directed Evolution guidelines of operation for enhanced enzymatic activity: The amino acid changes introduced via DE will be analyzed and compared to the natural occurrence frequency of amino acids in proteins. The enzyme regions found to be correlated to the active site residues will be mutated following this criterion.
S3.2 Elucidating the relationship between enzyme proficiency and computational scores: A chemoinformatic model to predict enzyme activities from the computational scores developed in S1 will be developed. With this approach, a predicted activity will be assigned to each mutant and thus will reduce the number steps by identifying beneficial mutations.


Subproject S4. Application of the NetMoDEzyme protocol towards industrially relevant pharmaceutical targets. The successful development of subprojects S1-S3 and the combination of the state-of-the-art technologies established are expected to blossom into the genuinely new NetMoDEzyme protocol. This subproject will validate the new developed protocol and will identify the weak and strong points of the methodology. The following subprojects will be targeted:
S4.1 Engineering epoxide hydrolases for the asymmetric synthesis of β-blockers: One of the most interesting targets in the α/β-hydrolase fold is the development of an epoxide hydrolase for the production of chiral precursors of β-adrenergenic receptor blocking drugs used to treat cardiovascular problems. The protocol will be applied to design new epoxide hydrolases accepting the bulky precursors of (S)-propranolol, and (S)-alprenolol for producing enantiomerically pure β-blockers at a reduced cost.
S4.2 Experimental evaluation of the computationally designed epoxide hydrolases: The TOP10 ranked mutants will be tested experimentally by one of the hired PhD students at the Department of Biology of the University of Girona (group led by Dr. M. Ribó and Dr. M. Vilanova). These experimental tests (see B2) will demonstrate the efficiency of the new developed protocol and will therefore validate the complete approach.


Milestones (M): The milestones of the proposal that will be targeted in the last period of the project are: (M3) determination of DE rules of operation and buildup of a Sequence-Activity relationship for assigning a predicted activity to each computational design, and (M4) validate the proposed protocol by designing active enz

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