Most Monogenic Disorders Are Caused by Mutations Altering Protein Folding Free Energy

Abstract

Revealing the molecular effect that pathogenic missense mutations have 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 an updated database of monogenic disorders (MOGEDO), which includes 768 proteins and 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 do not make protein over-stable, since over-stable protein may be dysfunctional. We demonstrate that, by using ΔΔG and predicted solvent exposure of the mutation site, one can develop a predictive method that distinguishes pathogenic from benign mutations 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), the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic.

Keywords: folding free energy; monogenic disorders; mutation; pathogenicity; protein stability.

Figure 1

Distribution of change in folding free energy using different predictors for monogenic disorder dataset 1 (no likely cases).

Figure 2

ROC curve for monogenic disorder dataset 1 (no likely cases) for different categories of amino acid mutations. According to their chemical properties, the amino acids are categorized as follows: Ala, Cys, Gly, Ile, Leu, Met, Phe, Pro, Trp, and Val are categorized as hydrophobic; Asp, Glu, Lys, Arg, His, Asn, Gln, Ser, Thr, and Tyr are categorized as polar; His, Phe, Trp, and Tyr are aromatic; Ala, ILe, Lys, Leu, Met, Pro, and Val are aliphatic; His, Lys, and Arg are positive; Asp and Glu are negative; Ala, Cys, Gly, Ser, Asn, Asp, Pro, Thr, and Val are small; and Arg, Gln, Glu, His, ILe, Leu, Lys, Met, Phe, Trp, and Tyr are large amino acids.

Figure 2

ROC curve for monogenic disorder dataset 1 (no likely cases) for different categories of amino acid mutations. According to their chemical properties, the amino acids are categorized as follows: Ala, Cys, Gly, Ile, Leu, Met, Phe, Pro, Trp, and Val are categorized as hydrophobic; Asp, Glu, Lys, Arg, His, Asn, Gln, Ser, Thr, and Tyr are categorized as polar; His, Phe, Trp, and Tyr are aromatic; Ala, ILe, Lys, Leu, Met, Pro, and Val are aliphatic; His, Lys, and Arg are positive; Asp and Glu are negative; Ala, Cys, Gly, Ser, Asn, Asp, Pro, Thr, and Val are small; and Arg, Gln, Glu, His, ILe, Leu, Lys, Met, Phe, Trp, and Tyr are large amino acids.

Figure 3

ROC curve for Monogenic Disorder Dataset 1 (no likely cases) for different functional categories.

Figure 3

ROC curve for Monogenic Disorder Dataset 1 (no likely cases) for different functional categories.