Web-based tools for computational enzyme design

Abstract

Enzymes are on high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users.These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

Bacterial α-Glucan and Branching Sucrases from GH70 Family: Discovery, Structure–Function Relationship Studies and Engineering

“…The extension of such approaches would also need setting-up very high throughput screening assays, which remains challenging. A support from computational protein design definitely makes sense in this context [ 203 , 204 , 205 ].…”

DE-STRESS: a user-friendly web application for the evaluation of protein designs

“…Finally, we use Aggrescan3D ( Kuriata et al. , 2019 ) to calculate an aggregation propensity score for protein structures, as aggregation is a common failure mode for designed and engineered proteins ( Marques et al. , 2021 ).…”

Exploiting enzyme evolution for computational protein design

“…While the earliest attempts at computational enzyme design focused primarily on side-chain positioning [1][2][3][4] or on focusing the search space for in vitro directed evolution (see Glossary) studies [5], subsequent work broadly expanded the scope of the field, including the fully de novo design of new enzymes [6] (typically followed by optimization using directed evolution) and the repurposing of existing enzymes to catalyze ever more complex chemical reactions [7,8]. In addition, computational design approaches are becoming ever-more streamlined, such that there now exists a range of powerful web servers that can assist in the design process [9].…”

“…We note that related methodological aspects have been recently reviewed in, for example, [40], and a summary of selected methods is presented in Table 1. In addition, the field is growing continuously, and the number of user-friendly tools is growing accordingly [9].…”

“…While the earliest attempts at computational enzyme design focused primarily on side-chain positioning [1][2][3][4] or on focusing the search space for in vitro directed evolution (see Glossary) studies [5], subsequent work broadly expanded the scope of the field, including the fully de novo design of new enzymes [6] (typically followed by optimization using directed evolution) and the repurposing of existing enzymes to catalyze ever more complex chemical reactions [7,8]. In addition, computational design approaches are becoming ever-more streamlined, such that there now exists a range of powerful web servers that can assist in the design process [9].In principle, computational design approaches can take two very loosely defined directions: structure-based design approaches that require some level of knowledge of the system of interest, including information about the chemical mechanisms, transition states, and key catalytic residues involved; and sequence-based design approaches that can, for example, draw on evolutionary information to predict potential hotspots for protein engineering as well as new variants with desired physicochemical properties, something that is in particular increasingly being achieved using machine-learning approaches [10].Computational approaches that require minimal knowledge of the molecular details of the chemical processes involved are attractive for their speed and efficiency, as exploring the underlying mechanisms and transition states typically requires significant experimental and/or computational effort. However, much like their experimental counterparts, such approaches are likely to hit optimization plateaus [11,12] where further improvement in activity becomes extremely challenging, and without knowledge of the underlying chemistry it can be difficult-to-impossible to overcome such plateaus.…”