Rational Design of Pepsin for Enhanced Thermostability via Exploiting the Guide of Structural Weakness on Stability

Enzyme thermostability is an important parameter for estimating its industrial value. However, most naturally produced enzymes are incapable of meeting the industrial thermostability requirements. Software programs can be utilized to predict protein thermostability. Despite the fast-growing number of programs designed for this purpose; few provide reliable applicability because they do not account for thermodynamic weaknesses. Aspartic proteases are widely used in industrial processing; however, their thermostability is not able to meet the large-scale production requirements. In this study, through analyzing structural characteristics and modifying thermostability using prediction software programs, we improved the thermostability of pepsin, a representative aspartic protease. Based on the structural characteristics of pepsin and the experimental results of mutations predicted by several energy-based prediction software programs, it was found that the majority of pepsin’s thermodynamic weaknesses lie on its flexible regions on the surface. Using computational design, mutations were made based on the predicted sites of thermodynamic weakness. As a result, the half-lives of mutants D52N and S129A at 70°C were increased by 200.0 and 66.3%, respectively. Our work demonstrated that in the effort of improving protein thermostability, identification of structural weaknesses with the help of computational design, could efficiently improve the accuracy of protein rational design.

Predicting protein domain temperature adaptation across the prokaryote-eukaryote divide

Protein thermostability is important for fitness but difficult to measure across the proteome. Fortunately, protein thermostability is correlated with prokaryote optimal growth temperatures (OGTs), which can be predicted from genome features. Models that can predict temperature sensitivity across the prokaryote-eukaryote divide would help inform how eukaryotes adapt to elevated temperatures, such as those predicted by climate change models. In this study we test whether prediction models can cross the prokaryote-eukaryote divide to predict protein stability in both prokaryotes and eukaryotes. We compare models built using a) the whole proteome, b) Pfam domains, and c) individual amino acid residues. Proteome-wide models accurately predict prokaryote optimal growth temperatures (r2 up to 0.93), while site-specific models demonstrate that nearly half of the proteome is associated with optimal growth temperature in both Archaea and Bacteria. Comparisons with the small number of eukaryotes with temperature sensitivity data suggest that site-specific models are the most transferable across the prokaryote-eukaryote divide. Using the site-specific models, we evaluated temperature sensitivity for 323,850 amino acid residues in 2,088 Pfam domain clusters in Archaea and Bacteria species separately. 59.0% of tested residues are significantly associated with OGT in Archaea and 75.2% of tested residues are significantly associated with OGT in Bacteria species at a 5% false discovery rate. These models make it possible to identify which Pfam domains and amino acid residues are involved in temperature adaptation and facilitate future research questions about how species will fare in the face of increasing environmental temperatures.

Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning

We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).

Universal constraints on protein evolution in the long-term evolution experiment with Escherichia coli

Although it is well known that highly expressed and highly interacting proteins evolve slowly across the tree of life, there is little consensus for why this is true. Here, I report that highly abundant and highly interacting proteins evolve slowly in the hypermutator populations of Lenski’s long-term evolution experiment with E. coli (LTEE). Specifically, the density of observed mutations per gene, as measured in metagenomic time series covering 60,000 generations of the LTEE, strongly anti-correlates with mRNA abundance, protein abundance, and degree of protein-protein interaction. Weaker positive correlations between protein thermostability and mutation density are observed in the hypermutator populations, counterbalanced by negative correlations between protein thermostability and mRNA and protein abundance. These results show that universal constraints on protein evolution are visible in data spanning three decades of experimental evolution. Therefore, it should be possible to design experiments to answer why highly expressed and highly interacting proteins evolve slowly.