In silico Enzyme engineering Framework (eEF) – a good companion for enzyme engineeringBy Pravin Kumar
Enzymes are exquisitely evolving catalytic machineries that can speed up a chemical reaction up to 1017 folds (Table 1). In retrospect, the enzyme tends to confront when taken-off from its home ground and placed in a fermentor. However, engineering enzymes by incorporating amino acid substitutions both rationally and irrationally has enabled the evolution of a new class of enzymes that can withstand extreme conditions and perform as desired.
To speed up and to decipher the molecular details of enzyme engineering process, we have developed an in silico enzyme engineering framework (eEF). The framework has been built using state-of-the-art computational techniques and improved theoretical tools for describing enzyme-substrate (E-S) reaction. Apart from speeding the engineering process, eEF provides valuable information about the principles that control enzyme mechanisms and offers guidance for engineering enzymes.
The complexity of even the most common reactions like hydrolysis and synthesis catalyzed by enzymes makes it difficult to identify the rate-determining step/s (RDS) of the reactions. The proton transfers (PT) from the lytic water molecule in any ATPases, proton abstraction from acetyl-coenzyme A in citrate synthase etc, are some of the examples. The intervention of the computational methods has to a large extent unveiled the complexity of enzymatic reactions. Methods like QM/MM, REMD, Transition state path sampling, etc., provides accurate atomistic picture and reasonable information on the thermodynamics of enzymatic reactions. Processing the output of these techniques and visualizing using high end visualizations tools depicts the nuclei-dynamics between the catalytic residues of the enzyme and the substrates. Other than the actual catalytic process that takes place in the enzymes active site, the path taken by the substrate to enter the catalytic site also affects the rate of reaction. Therefore, transition path sampling simulations envision the energy landscape of such a system helping to select the best spot for enzyme modification, to attain activity towards a specific substrate or to show increased activity for an existing substrate. For example, trimming some of the side chains by substituting bulkier amino acids like Methonine with Glycine would facilitate the movement of the substrate by clearing some of the steric hindrances.
eEF predicts the path or multiple paths taken by the substrate to enter the active site and searches for high energy regions in the enzymes during the reaction that would affect the reaction rate. Simulations conducted using eEF help in identifying residues within and around the catalytic site in what is called as the proximity effect.
Thus, changing such identified residues could be crucial during a synthesis process, which aids in bringing two or more substrates closer to each other by attaining the lowest possible free energy of the system (Figure 1). In a not-so-well-understood enzymatic reaction, eEF was able to predict the thermodynamics and the consequent path taken & appropriate orientation of two substrates in the catalytic region of Penicillin G acylase (PGA) (Figure 2). eEF further predicted the different transition states of the E-S
reaction catalyzed by PGA and exploring these transition states of both the substrates (Figure 1) and the induced conformational changes of the enzyme (Figure 3) revealed the differences between a slow or an uncatalyzed reaction and a fast catalyzed reaction. Consequently, changes made by eEF should enhance the enzymatic reaction and it is far reliable than the blind evolutionary techniques such as error prone PCR that has no rationale for any of the enzyme modifications it produces. Experiments conducted using eEF are carried out in different solvents, varying temperature & pH, pressure etc., mimicking an in vitro experiment.
This exhaustive and systematic approach of eEF used for identifying Hotspots produces several (~200,000) modified 3D enzyme structures that are the permutation combinations of different amino acid substitutions. The 3D structures are filtered using intramolecular energy of the enzyme and the intermolecular energies calculated between the enzyme and the substrate. To compute the intermolecular energies, eEF uses a unified scoring function that considers the physical atomic contacts and atomic energies such as Van der Waals forces, electrostatic interactions, hydrogen bonds energies, pi-pi and cat-pi interactions. In addition, transformations of desolvation parameters and intra hydrogen bond energies are analyzed to compute the stability of the enzymes. At this stage eEF filters 30 potential enzyme modifications out of ~ 200,000 modifications. The filtered modifications enter the next level in the framework called “transition state analysis”, which generates reaction coordinates of different stages of transition states like E-S, E-S->E-P and the E-P. Transition state analysis is carried out using quantum mechanics/molecular mechanics (QM/MM) simulations, steered molecular dynamics and transition path sampling. Finally, the transition state reaction coordinates are used to predict the kinetic properties, activation energy and rate limiting step/s of the modified enzymes. The overall steps involved for engineering a given enzyme using eEF:
- Modeling of the catalytic binding mode of the substrate/s in the enzyme
- identification using different experiments as described above
- Knowledge based substitutions in the hotspots and production of ~200,000 3D enzyme modifications
- Filtering and ranking the top 30 potential modifications from ~200,000 modifications using filters based on the intra & inter molecular energies of the E-S complex
- Finally, predicting the kinetic properties like Kcat, Km for the top 30 modifications
Polyclone in News
- Polyclone signs MoU with JSS University to expedite stem cell R&D
- TATAA Biocenter announced a reseller agreement with Polyclone
Articles of this issue
Click here for a free trial