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Evaluating Molecular Modeling Tools for Thermal Stability Using an Independently Generated Dataset

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Evaluating Molecular Modeling Tools for Thermal Stability Using an Independently Generated Dataset bioRxiv preprint doi: https://doi.org/10.1101/856732; this version posted November 28, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Evaluating molecular modeling tools for thermal stability using an independently generated dataset Peishan Huang, 1 Simon K. S. Chu, 1 Henrique N. Frizzo,2 Morgan P. Connolly,3 Ryan W. Caster,2 Justin B. Siegel2,4,5,* 1Biophysics Graduate Group, University of California, Davis, California, United States of America 2Genome Center, University of California, Davis, California, United States of America 3Microbiology Graduate Group, University of California, Davis, California, United States of America 4Department of Biochemistry & Molecular Medicine, University of California, Davis, California, United States of America, 5Department of Chemistry, University of California, Davis, California, United States of America KEYWORDS Thermal stability, Rosetta Molecular Modeling Suite, molecular modeling, Protein Engineering ABSTRACT: Engineering proteins to enhance thermal stability is a widely utilized approach for creating industrially relevant biocatalysts. Computational tools that guide these engineering efforts remain an active area of research with new data sets and develop algorithms. To aid in these efforts, we are reporting an expansion of our previously published data set of mutants for a -glucosidase to include both measures of TM and G, to complement the previously reported measures of T50 and kinetic constants (kcat and KM). For a set of 51 mutants, we found that T50 and TM are moderately correlated with a Pearson correlation coefficient (PCC) of 0.58, indicated the two methods capture different physical features. The performance of predicted stability using five computational tools are also evaluated on the 51 mutants dataset, none of which are found to be strong predictors of the observed changes in T50, TM, or G. Furthermore, the ability of the five algorithms to predict the production of isolatable soluble protein is examined, which revealed that Rosetta ΔΔG, ELASPIC, and DeepDDG are capable of predicting if a mutant could be produced and isolated as a soluble protein. These results further highlight the need for new algorithms for predicting modest, yet important, changes in thermal stability as well as a new utility for current algorithms for prescreening designs for the production of soluble mutants. INTRODUCTION reduced by 50% after a heat-challenge over a defined time.4,12,13 When analyzing this set of mutants using two A common goal of enzyme engineering is the enhancement of established computational programs (Rosetta ΔΔG and FoldX thermal stability.1 For industrial applications improving a PSSM) for predicting thermal stability, we found that there proteins’ robustness to thermal challenges or half-life at was no significant correlation between the predictions and the elevated temperature can often be the deciding factor for the observed T50.14 commercialization of a biocatalyst.2–5 Currently, the most common approach for improving thermal stability is through One hypothesis explaining the poor predictive performance of directed evolution methodologies,6,7 which, can be time- the algorithms with the BglB dataset is that the algorithms are consuming, costly, and limited in the ability to search sequence trained on TM, a direct measure of structural thermal stability, space. Computational design tools to predictably identify but being used to predict T50, an indirect measure of the mutations, single and combinatorial that enhance thermal protein’s thermal stability.12 Alternatively, the poor stability are rapidly developing and growing in popularity.8–11 performance could have come from the narrow T50 range However, accurate predictions using computational tools to (extreme variants are +6.06 °C and -5.02 °C from wild type), as guide protein stability design remains an active area of research the algorithms are generally trained on larger changes in and is not always successful. thermal stability and ± 5 °C may be within the error of the currently developed algorithms. In this study, we evaluated both The use of large data sets on the mutational effect on protein hypotheses. To assess if there is a significant difference in TM stability, such as ProTherm, is often used to train and T50 we developed a data set of 51 BglB mutants (Figure 1) computational methods for predicting thermal stability. The in which both thermal stability measurements, T50 and TM, are data sets utilized generally composed of the equilibrium measured. constant of unfolding (KU) or the melting temperature of an enzyme (TM).12 In our previous study, we determined the Interestingly, for the set of 51 measurements, there is only a thermal stability of 79 β -glucosidase B (BglB) variants by modest correlation between T50 and TM, with a PCC of 0.58. finding T50 – a type of kinetic stability that is determined by This highlights the difference in the physical properties being the temperature at which a mutant’s residual activity is measured using these two techniques, TM being the thermal 1 bioRxiv preprint doi: https://doi.org/10.1101/856732; this version posted November 28, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. stability of the protein’s structural elements and T50 reporting breathable seal and incubated while shaking at 250 rpm at 18 C on the thermal stability to irreversible denaturation. However, for 24 hours. Then the cells were then spun down, lysed, and similar to the previous study,14 the relationship between the purified samples using immobilized metal ion affinity predicted stability with the experimental TM only results in a chromatography as previously described.14 The purity of the weak correlation not only with the previous algorithms protein samples was analyzed using 12-14% SDS-PAGE (SI 1- evaluated (Rosetta ΔΔG and FoldX PSSM) but also three other 1), the yield was assessed based on the A280 for proteins that commonly used methods: ELASPIC, DeepDDG, and appeared >75% pure in the SDS-PAGE analysis. PoPMuSiC. This result suggests that while the two measurements are reporting on different physical properties, Melting Temperature assay. The melting temperature (TM) of this is not the key factor that led to the low predictive accuracy BglB was determined using the Protein Thermal Shift (PTS) ™ of established algorithms on this data set. kit (Applied BioSystem ®, from Thermo Fisher). Standard protocols provided by the manufacturer were used. Protein concentrations ranged from 0.1- 0.5 mg/mL and fluorescence reading was monitored with a QuantaStudio™ 3 System from 20 °C to 90 °C. The temperature melting curve was first smoothened with a 20-step sliding window average (SI 4). TM was determined from the average of 4 replicates at which the derivative was largest, and all melting curves can be found in SI 2. ΔΔG Calculations from TM. Calculations were carried out in a similar manner as previously described.15 Briefly, to derive ΔG°unfolding the fluorescence intensity was first translated into fraction of folded (Pf) and unfolded (Pu) proteins at different temperatures starting from the minimum fluorescence (퐹푚푖푛) to the maximum fluorescence (퐹푚푎푥) shown in eq 1. 퐹−퐹푚푖푛 푃푓 = 1 − (1) Figure 1. Structure of BglB (PDB ID: 2JIE) from bacterium 퐹푚푎푥−퐹푚푖푛 Paenibacillus polymyxa. PyMOL rendering15 of BglB showing the 28 sequence-positions (teal spheres) of the 51 mutants By taking a two-state folding-unfolding model, the equilibrium randomly selected for TM analysis. The reaction scheme of the constant of unfolding (Ku) at different temperatures is then hydrolysis of 4-nitrophenyl β-D-glucopyranoside by BglB used given by: in the T50 study.14 퐾 = 푃푢 (2) 푢 푃 To evaluate the second hypothesis, that the changes in thermal 푓 stability of the BglB data set are too small for current By plotting ln Ku against 1 ⁄ 푇 using Van’t Hoff method shown algorithms, we investigated the ability of the algorithms to ∆퐻 ∆푆 predict soluble protein production. Mutations that reduce in eq 3 (SI 2), the − ⁄푅 is defined by the slope, ⁄푅 is the thermal stability (TM) by > 18 °C also fail to yield soluble, y-intercept, T is temperature, and R is the ideal gas constant. isolatable protein. Analysis of computational algorithms ln 퐾 = − ∆퐻 + ∆푆 (3) enriched for insoluble mutants based on the predicted energetics 푢 푅푇 푅 showed a significant enrichment was observed for Rosetta ΔΔG. This supports the hypothesis that the lack of performance The Gibbs free energy of protein-unfolding (ΔG°unfolding) can on the BglB data set is due to the narrow range in thermal then be determined using eq 4, where ΔG°unfolding is the stability changes observed. This highlights the need for new unfolding energy at 298K as T. All calculations can be found algorithms for predicting modest, yet important, changes in under SI 4. thermal stability as well as a new utility for current algorithms for prescreening designs for the production of soluble protein. ∆퐺°푢푛푓표푙푑푖푛푔 = ∆퐻 − 푇∆푆 (4) METHODS Molecular modeling. Five popular, readily accessible, and recently developed force-field and machine-learning-based Mutant Selection, protein expression, and purification. Out molecular modeling methods were evaluated for their ability to of 79 mutants of Beta-glucosidase B (BglB) that were recapitulate the experimental data: Rosetta ΔΔG16, FoldX17, previously characterized with T50 data,14 51 variants with ELASPIC18, DeepDDG19, and PoPMuSiC.20 The crystal plasmid readily available were transformed into chemically structure of BglB (PDB ID: 2JIE) was used across five different competent E. coli BLR(DE3) cells. The variants were produced algorithms.
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