Thermostabilizing mutations preferentially occur at structural weak spots with a high mutation ratio

• Published in: Journal of biotechnology 2012-06, Vol.159 (3), p.135-144
• Main Authors: Rathi, Prakash C., Radestock, Sebastian, Gohlke, Holger
• Format: Article
• Language: English
• Subjects: Amino acids; Animals; Archaeal Proteins - chemistry; Archaeal Proteins - genetics; Archaeal Proteins - metabolism; biotechnology; citrate (si)-synthase; Citrate (si)-Synthase - chemistry; Citrate (si)-Synthase - genetics; Citrate (si)-Synthase - metabolism; Citrate synthase; Constraint Network Analysis; Databases, Protein; Enzyme Stability; Flexibility; Hot Temperature; hydrogen bonding; Hydrophobic and Hydrophilic Interactions; hydrophobic bonding; hydrophobicity; Mathematical models; Molecular Dynamics Simulation; Mutation; Mutations; Networks; Protein engineering; Protein Engineering - methods; Protein Unfolding; Proteins; Proteins - chemistry; Proteins - genetics; Reinforcement; Reproducibility of Results; Rigidity theory; Spots; Swine; temperature; Thermal stability; Thermostability
• ISSN: 0168-1656; 1873-4863
• DOI: 10.1016/j.jbiotec.2012.01.027

[Display omitted] ► Constraint Network Analysis for predicting protein thermostability and weak spots. ► Improved analysis by considering structure ensembles and new network representation. ► Very good correlation between predicted and experimental thermostabilities for 5 CS. ► Weak spots predicted on less stable CS are preferentially mutated in more stable CS. ► Method works as pre-filter in data-driven engineering for thermostabilization. We apply Constraint Network Analysis (CNA) to investigate the relationship between structural rigidity and thermostability of five citrate synthase (CS) structures over a temperature range from 37°C to 100°C. For the first time, we introduce an ensemble-based variant of CNA and model the temperature-dependence of hydrophobic interactions in the constraint network. A very good correlation between the predicted thermostabilities of CS and optimal growth temperatures of their source organisms (R2=0.88, p=0.017) is obtained, which validates that CNA is able to quantitatively discriminate between less and more thermostable proteins even within a series of orthologs. Structural weak spots on a less thermostable CS, predicted by CNA to be in the top 5% with respect to the frequency of occurrence over an ensemble, have a higher mutation ratio in a more thermostable CS than other sequence positions. Furthermore, highly ranked weak spots that are also highly conserved with respect to the amino acid type found at that sequence position are nevertheless found to be mutated in the more stable CS. As for mechanisms at an atomic level that lead to a reinforcement of weak spots in more stable CS, we observe that the thermophilic CS achieve a higher thermostability by better hydrogen bonding networks whereas hyperthermophilic CS incorporate more hydrophobic contacts to reach the same goal. Overall, these findings suggest that CNA can be applied as a pre-filter in data-driven protein engineering to focus on residues that are highly likely to improve thermostability upon mutation.