Computational Design of Stable and Soluble Biocatalysts

Abstract

Natural enzymes are delicate biomolecules possessing only marginal thermodynamic stability. Poorly stable, misfolded, and aggregated proteins lead to huge economic losses in the biotechnology and biopharmaceutical industries. Consequently, there is a need to design optimized protein sequences that maximize stability, solubility, and activity over a wide range of temperatures and pH values in buffers of different composition and in the presence of organic cosolvents. This has created great interest in using computational methods to enhance biocatalysts' robustness and solubility. Suitable methods include (i) energy calculations, (ii) machine learning, (iii) phylogenetic analyses, and (iv) combinations of these approaches. We have witnessed impressive progress in the design of stable enzymes over the last two decades, but predictions of protein solubility and expressibility are scarce. Stabilizing mutations can be predicted accurately using available force fields, and the number of sequences available for phylogenetic analyses is growing. In addition, complex computational workflows are being implemented in intuitive web tools, enhancing the quality of protein stability predictions. Conversely, solubility predictors are limited by the lack of robust and balanced experimental data, an inadequate understanding of fundamental principles of protein aggregation, and a dearth of structural information on folding intermediates. Here we summarize recent progress in the development of computational tools for predicting protein stability and solubility, critically assess their strengths and weaknesses, and identify apparent gaps in data and knowledge. We also present perspectives on the computational design of stable and soluble biocatalysts.

Computational redesign of a PETase for plastic biodegradation by the GRAPE strategy

“…Over the last 20 years, in silico design based on energy calculations has taken a long way from fairly simple to more accurate and versatile methods [42][43] , particularly with a positive impact in the area of protein stabilization. However, the accuracies based on energy functions are still suboptimal because of several factors, including the insufficient conformational sampling of the static structure, imbalances in the force fields, and the intrinsic problems with existing data sets 24 . Although the drawback can be mitigated by using hybrid methods that incorporate complementary statistical-based approaches such as ABACUS, most stability strategies are focusing on single-pointmutation or simple stepwise combination process, resulting in higher prediction errors upon application to multiple-point mutants.…”

“…There is usually a pathway whereby some new functions could be acquired by individually beneficial mutations, however, when the desired function is beyond what a single mutation or double mutations can accomplish, possible paths grow exponentially as the mutations accumulate and most paths result in downhill or even unfolded proteins 23 . Since a majority of protein engineering studies involves simple uphill walks, the main demand lies in identifying an efficient path of accumulated mutations to achieve the desired protein performance 24 .…”

A benchmark of protein solubility prediction methods on UDP-dependent glycosyltransferases

“…We have compared the performance of 11 sequence based solubility prediction methods on plant UGT proteins. We have only included the tools that predict the solubility of different proteins expressed in E. coli [12,13]. The list of the tools and their description can be found in the Methods section.…”

“…Solubility prediction software tools can have a significant impact on recombinant protein production by excluding insoluble proteins from expression trials and thereby preventing extra costs and dead-end experiments. Overall, solubility prediction tools can be grouped into 3 classes based on their applications [12]: 1) methods that predict the overall solubility of proteins upon expression (usually in E. coli), 2) approaches for predicting the aggregation propensity of different regions in a protein sequence, and 3) tools that predict the impact of mutations on solubility of proteins. Among these groups, the former is studied here.…”

Evaluating molecular modeling tools for thermal stability using an independently generated dataset

“…TM is defined by the temperature at which half the enzyme is found in the unfolded state over folded state12,22 and is often evaluated through denaturation assays, from which the thermodynamic measurements (ΔGunfolding) can be obtained. 22 This method is generally a lower throughput method as purified protein is required to get an accurate measurement for the structural properties for the mutant being evaluated. T50 measures the temperature of half-inactivation that leads to irreversible unfolding11,23, and it is determined by the reduction of half of the enzymatic activity due heat-challenges.12 This is a very common assay for protein engineering due to its compatibility with high throughput assays and the ability to use cell lysates to evaluate function.…”