Enzymes are nature’s catalysts that can help industry, as they aid to making industrial processes and relevant products such as those in food, agricultural, cosmetic, and pharmaceutical sectors, faster, cleaner, safer, cheaper, and more sustainable. Their use contributes to solving global challenges such as overpopulation, diminishing natural resources, pollution and human health challenges, which in turn demands new enzymes with novel and/or improved properties, structures and activities. Finding new enzymes for academic and industrial settings is a long and complex process, but due to extensive efforts of the scientific community, this time frame has been significantly reduced: nowadays, it takes less than three years to provide industry with new enzyme solutions. Most enzymes are isolated from microbes and recent technological advances mean that it is relatively easy to produce new enzymes. The real problem is that only a very small percentage of new enzymes are useful in industrial processes. This was this Thesis’s challenge, to improve the value of new enzymes and to help understanding, predicting and engineering their properties, to find candidates of interest for industry. First, this Thesis set out to find new enzymes from phylogenetically diverse uncultured microbial communities as well as cultured microbes, that develop in multiple environments, including some of the most extreme habitats on the planet. This guarantees high enzymatic diversity and that the recovered enzymes may be already adapted to work in multiple environmental conditions of pH, temperature, salinity, and nutrient diversity and concentration, to cite some. Second, this Thesis focuses its attention on finding and improving the value of new enzymes for industry, by targeting so-called “substrate promiscuous” enzymes, that are capable of accepting many substrates and therefore are usable in more than one industrial process. By focusing on serine ester-hydrolases and class III ω-transaminases, and by applying and designing a number of screening tools and high throughput technologies, we have created one of the largest collection of such enzymes in a laboratory, accounting for a total of 155 (145 and 10, respectively). By their extensive characterization with circa 130 different substrates, the substrate specificity and selectivity of each enzyme candidate was evaluated, and those with prominent features identified. With the help of crystal structures and computational tools, markers of “substrate promiscuity” and selectivity were identified, through which this Thesis was able to fast-track the identification of industrially versatile ester-hydrolases and class III ω-transaminases; they have better properties, namely, higher and uncommon substrate specificity and selectivity, compared to best commercial enzymes. Further, we demonstrated that it is possible to transform a “substrate promiscuous” but non stereo-specific enzyme into a biocatalyst with broad substrate range while showing chiral selectivity through supramolecular and protein engineering tools. Major outcomes of the present Thesis include: I) the generation of robust meta-data demonstrating that biodiversity in the environment provides an invaluable source of novel enzymes; II) the accumulation of data demonstrating that “substrate promiscuous” enzymes accepting a wide range of different substrates can be identified, and are more abundant than expected; III) the demonstration that the “substrate promiscuity” level of certain classes of enzymes can be predicted from the analysis of their sequence, 3D model and/or structure, without the need of previous cloning, expression and characterization; IV) reinforcing the idea that broad substrate range is associated with low enantioselectivity, but that this can be solved by supramolecular engineering and by applying to an enzyme flexibility constraints through different immobilization strategies; V) the demonstration that through discovering novel binding sites where extra active sites can be introduced, it is possible to generate an enzyme with two functional reactive groups, to improve the competitiveness and catalytic opportunities of enzymatic catalysts

Calificación: Sobresaliente Cum Laude por unanimidad. Premio extraordinario por la Universidad Autónoma de Madrid.

Peer reviewed