Join theenzymerevolution

No more games of chance.Just smart, predictive work

If there's an enzyme, we will find it

Learn how we can discover the enzyme you were looking for in a short time and with high precision.

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How we're improving enzymes in record-time

Find out how we use computer simulations to optimize enzymes that fit perfectly into your project.

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How does enzymesearch work

Explore the entire enzyme landscape with BIOMATCHMAKER®

Thanks to the use of realistic, computer simulations, we can quickly find the best candidates for any target chemical application.

Taking to the lab only the most promising enzymes reduces uncertainty and saves significant amounts of time and resources.


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How does enzymeoptimization work

Identify the best mutations to boost enzyme performance with ZYMEVOLVER

Our solution relies on highly realistic molecular design techniques, mimicking the enzyme’s real surrounding conditions.

Thanks to this feature, we can determine with accuracy what region of the sequence space is crucial for the final enzyme’s performance.

Plus, ZYMEVOLVER uses additional ZYMSPOT technology to pinpoint distal enzyme hotspots with the potential to enhance enzyme properties.

No need to waste time in the lab.


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ZYMVOL provides ALL important features for simulation of biocatalytic reactions


Enzymes are made of amino acids whose identity and position shape function. Bioinformatics provides insights about sequence-function relationships by analyzing in detail protein sequence databases and activity data. It allows to focus the search for improved variants in the portion of sequence space that actually matters.

Side chain Flexibility

Enzymes are dynamic molecules and all parts should be free to move. Side chain flexibility deals with amino acid side chain movement. These days, while many companies do include a degree of side chain flexibility, this is normally associated with rigid backbone structures.

Backbone flexibility

In addition to the side chains, the protein backbone is another important part of the system. Including backbone flexibility is computationally very expensive and time-consuming, so most companies omit it. However, a realistic representation of protein-substrate dynamics requires a fully flexible system. That’s why we have devised methodologies that account for this effect while remaining time- and cost-effective (Gil, 2016).

Distant mutations

Most computer-guided engineering centers only on amino acids at the active site since, in general, this is the most important area of the protein when looking to improve activity, selectivity or specificity. Nevertheless, experimental directed evolution has shown that this is true only to a certain extent, and amino acids in other parts of the protein are often required for effective improvements. Therefore, we are currently implementing this extremely challenging feature to make our simulations even more powerful and be able to compete with experimental techniques in this area at a fraction of their cost.

Explicit Solvent

Most of our competitors use implicit solvent representation via dielectric constant to simulate solvent effect (typically for water, ε=78) The alternative is to use explicit water molecules (3 atom particles) that permit a far more realistic representation of the solvent but are computationally much more expensive. We use a multi-scale protocol that allows us to screen a large number of variants in a first phase with implicit water or another solvent before increasing the level of theory in the second filter to include explicit solvent over a smaller number of selected variants.

Quantum mechanics

Includes electronic-based simulations that can account for the fundamental enzyme processes of bond breaking/forming and electron transfer. Most companies rely exclusively on “cheaper” classical force field-based methods or on bioinformatics alone. ZYMEVOLVER uses a hybrid method called QM/MM (quantum mechanics/molecular mechanics, awarded the Nobel Prize in Chemistry, 2013) that allows us to study reaction chemistry with electronic structure methods like Density Functional Theory, and the rest of the system (protein and solvent) with classical MM. Our team has developed highly efficient methods—very economical from the computational point of view and very quick—that allow us to study the effect of point mutations in a time frame that can be used at the industrial level (Monza, 2015).

Our Success Stories

Dive into some of our top performing enzyme projects.

Take a look at how we work at ZYMVOL and learn how we apply our technology and expertise to overcome different challenges.

See our case studies