Studying the Protein Thermostabilities and Folding Rates by the Interaction Energy Network in Solvent

Abstract

Residue interaction networks determine various characteristics of proteins, such as the folding rate, thermostability, and allosteric process. The interactions between residues can be described by distances or energies. The former is simple but less rigorous. The latter is complicated but more precise, especially when considering the solvent effect. In this work, we apply an existing energy decomposition method based on the Poisson–Boltzmann equation solver. The calculation is especially accelerated on GPU for higher performance. In four formal applications, the constructed interaction energy (IE) network shows good results. First, it is found that the protein folding rate has a stronger correlation with the energy-based contact order than the distance-based contact order. The Pearson correlation coefficient (PCC) is 0.839 versus 0.784 on a dataset of non-two-state proteins. Second, we find that most thermophilic proteins have lower IEs than mesophilic proteins. The IE in solvent acts as an indicator to evaluate the thermostabilities of proteins. Third, we use the IE network to predict the key residues in the formation of the insulin dimer. Most key residues are in agreement with the findings in previous alanine-scanning experiments. Lastly, we propose a novel method (called APFN) to predict the allosteric pathway based on the IE network. The method gives the same allosteric pathway for CheY protein as in previous nuclear magnetic resonance spectroscopy experiments. On the whole, the IE network in the solvent has been demonstrated to be reliable in describing the characteristics embedded in protein structures.