CATALYSTS friendly nature of biocatalysis meets the growing demand for sustainable pharmaceutical manufacturing processes .
Enzyme engineering plays a crucial role in optimising biocatalysts for ncAA synthesis . Advances in computational biology , machine learning and artificial intelligence ( AI ), as well as next-generation sequencing and ultra-high throughput screening , have significantly improved our ability to design and predict enzyme structures and functions . Techniques such as ancestral sequence reconstruction and deep learning have been employed to develop enzymes with enhanced stability and activity .
These engineered enzymes can exhibit superior catalytic properties , enabling more efficient ncAA synthesis under industrially relevant conditions . The Nobel Prizes in Chemistry in 2018 and 2024 , awarded for advances in enzyme engineering , structure prediction and enzyme design , highlight the critical importance of these developments in the field of biocatalysis .
BioEngine & BioNavigator
Recent advances in ncAA production at Enzymaster include the synthesis of derivatives of tryptophan , tyrosine and phenylalanine . These accomplishments were achieved through the application of directed evolution , facilitated by Enzymaster ’ s proprietary BioEngine ** platform and supported by the BioNavigator ** bioinformatics toolkit for intelligent enzyme library design .
BioEngine combines directed evolution with computational enzyme engineering . Computational predictions are employed to generate efficient and focused libraries of enzyme variants , which are systematically screened using techniques such as HPLC , GC or MS .
The process evaluates a wide range of properties in enzyme variants under process conditions , enabling the achievement of biocatalytic process targets in just three to six iterative rounds of combined in silico and experimental optimisation . This typically corresponds to a development timeline of four to eight months .
BioNavigator offers advanced computational methods for enzyme discovery and optimisation , including in silico predictions of activity , selectivity and stability under non-natural process conditions . It leverages bioinformaticsdriven identification of key mutation sites and rational combinatorial library design for efficient enzyme engineering . Advanced AI algorithms and structure-based recombination techniques are used for fast virtual pre-screening .
These approaches significantly expand the diversity of combinatorial sequence libraries beyond conventional methods such as random and site-saturation mutagenesis .
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Figure 2 - Relative activity of TrpB wild type ( WT ) & evolved variants ( V1-V6 )
Figure 3 - Reaction catalysed by TPL ( a ) & sub-strate scope ( b )
By covering an extensive sequence space ( up to one million variants / day ), BioNavigator identifies crucial sequence regions for enhanced enzyme performance in biocatalytic processes .
Tryptophan synthase : From zero to hero
In nature , the β-sub-unit of tryptophan synthase ( TrpB ) catalyses the condensation of indole ( Figure 1a , 1 ) with L-serine ( 2 ) to produce L-tryptophan ( 3 ), utilising pyridoxal 5 ' -phosphate ( PLP ) as a coenzyme . In this case study , the objective was to synthesise an L-tryptophan derivative ( Figure 1b , 5 ) using the corresponding indole derivative ( 4 ) as the substrate .
While the initial TrpB variant ( WT ) showed zero activity towards the target indole derivative , screening
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