Most monogenic disorders are caused by mutations altering protein folding free energy

Abstract

Revealing the molecular effect that pathogenic missense mutations cause on the corresponding protein is crucial for developing therapeutic solutions. This is especially important for monogenic diseases since, for most of them, there is no treatment available, while typically, the treatment should be provided in the early development stages. This requires fast, targeted drug development at a low cost. Here, we report a database of monogenic disorders (MOGEDO), which includes 768 proteins, the corresponding 2559 pathogenic and 1763 benign mutations, along with the functional classification of the corresponding proteins. Using the database and various computational tools that predict folding free energy change (ΔΔG), we demonstrate that, on average, 70% of pathogenic cases result in decreased protein stability. Such a large fraction indicates that one should aim at in-silico screening for small molecules stabilizing the structure of the mutant protein. We emphasize that knowledge of ΔΔG is essential because one wants to develop stabilizers that compensate for ΔΔG but not to make protein over-stable since over-stable protein may be dysfunctional. We demonstrate that using ΔΔG and predicted solvent exposure of the mutation site; one can develop a predictive method that distinguishes pathogenic from benign mutation with a success rate even better than some of the leading pathogenicity predictors. Furthermore, hydrophobic-hydrophobic mutations have stronger correlations between folding free energy change and pathogenicity compared with others. Also, mutations involving Cys, Gly, Arg, Trp and Tyr amino acids being replaced by any other amino acid are more likely to be pathogenic. To facilitate further detection of pathogenic mutations, the wild type of amino acids in the 768 proteins mentioned above was mutated to other 19 residues (14,847,817 mutations), and the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic.

Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations

“…Different studies [1][2][3][4] demonstrate that mutations can significantly hinder a protein's ability to effectively bind and interact with key biomolecules such as DNA, RNA, and other proteins. Consequently, alterations in protein stability can initiate a chain reaction, thereby potentially leading to disease development.…”