Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri, USA.
Medical Scientist Training Program, Washington University in St. Louis, St. Louis, Missouri, USA.
Protein Sci. 2024 Mar;33(3):e4902. doi: 10.1002/pro.4902.
The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.
精准医学的目标是利用我们对疾病分子病因的了解,更好地诊断和治疗患者。然而,与精准医学的需求相比,食品和药物管理局(FDA)批准的药物和注释编码变体的数量存在着实质性的不匹配。这篇综述介绍了基于物理的精准医学的概念,这是一个可扩展的框架,有望提高我们对序列-功能关系的理解,并加速药物发现。我们表明,通过计算机模拟来解释蛋白质在溶液中采用的结构组合,可以克服许多假设单一蛋白质结构所带来的限制。我们强调了蛋白质动力学的研究和最近用于分析结构组合的方法。这些研究表明,构象分布的差异可以预测蛋白质家族内和变体之间的功能差异。由于新的计算工具为我们提供了前所未有的获取蛋白质结构组合的途径,这种洞察力可能使整个变体库的变体致病性的准确预测成为可能。我们进一步表明,通过明确考虑蛋白质组合,例如通过自由能计算或对接马尔可夫状态模型,可以发现新的先导化合物。总之,我们证明了隐藏口袋或实验结构中不存在的腔隙为靶向目前被认为不可成药的蛋白质提供了一种途径。总的来说,我们的综述为蛋白质科学领域提供了一条加速精准医学的路线图。