Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

Abstract

To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO2 or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.

Molecular Evolution of an Aminotransferase Based on Substrate–Enzyme Binding Energy Analysis for Efficient Valienamine Synthesis

“…Defining hotspots for mutagenesis precisely is crucial for obtaining ideal results with the least effort when constructing mutation libraries, that is, making the libraries small but smart; this approach can accelerate the evolution process greatly. , In the present case, nine variable residues with statistically significant predicted destabilizing effects on the undesired binding of valienone–PMP to RffA_Kpn were identified from among the 62 contacting residues so that relatively small, focused libraries could be constructed to accelerate the identification of desirable enzyme mutations.…”

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering

“…For this reason enzyme engineers have started to regularly use those methods to improve the outcome of directed evolution and enzyme engineering campaigns. 87–97 Machine learning can improve the efficiency of downstream experimental studies thereby adding value and complimenting purely experimental bioengineering approaches. Moreover, the exponential increase in DNA sequencing throughput presents a significant opportunity to combine state-of-the-art computation and machine learning with large biological datasets in an attempt to learn sequence-function maps in protein sequence space.…”

In silico protein engineering shows that novel mutations affecting NAD+ binding sites may improve phosphite dehydrogenase stability and activity

“…Point mutations in protein structure can lead to novel dynamical features and conformational modifications 9 that affect protein activity and stability 10 . The mechanism of such alterations behind a mutation is being explored to better understand their effect on PTDH structure, stability, and activity 11 , 12 . Recently, we investigated how a series of mutations in P. stutzeri applied in the laboratory and deposited in the Brenda database ( https://www.brenda-enzymes.org/ ) affected PTDH-NAD + interaction and structural stability 13 .…”

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to better understand their effect on PTDH structure, stability, and activity 11,12 . Recently, we investigated how a series of mutations in P. stutzeri applied in the laboratory and deposited in the Brenda database (https:// www.

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