Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA.
Int J Mol Sci. 2024 Feb 6;25(4):1963. doi: 10.3390/ijms25041963.
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.
揭示致病错义突变对相应蛋白质的分子影响对于开发治疗方法至关重要。对于大多数单基因疾病来说,这一点尤为重要,因为这些疾病大多数都没有有效的治疗方法,而通常情况下,治疗应该在早期发育阶段进行。这就需要以较低的成本快速进行靶向药物开发。在这里,我们报告了一个更新的单基因疾病数据库(MOGEDO),其中包含 768 种蛋白质和相应的 2559 种致病性和 1763 种良性突变,以及相应蛋白质的功能分类。我们利用该数据库和各种预测折叠自由能变化(ΔΔG)的计算工具,证明平均有 70%的致病性病例会导致蛋白质稳定性降低。如此大的比例表明,人们应该通过计算机筛选来寻找稳定突变蛋白结构的小分子。我们强调,ΔΔG 的知识是必不可少的,因为人们希望开发能够补偿 ΔΔG 的稳定剂,但又不会使蛋白质过度稳定的稳定剂,因为过度稳定的蛋白质可能会失去功能。我们证明,通过使用 ΔΔG 和预测突变部位的溶剂暴露,可以开发一种预测方法,该方法区分致病性和良性突变的成功率甚至优于一些领先的致病性预测方法。此外,与其他突变相比,疏水-疏水突变与折叠自由能变化和致病性之间的相关性更强。另外,涉及 Cys、Gly、Arg、Trp 和 Tyr 氨基酸被任何其他氨基酸取代的突变更有可能是致病性的。为了方便进一步检测致病性突变,我们将上述 768 种蛋白质中的野生型氨基酸突变为其他 19 种残基(14847817 种突变),用 SAAFEC-SEQ 计算 ΔΔG,并预测 5506051 种突变是致病性的。