Saeed Ammaar A, Klureza Margaret A, Hekstra Doeke R
Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA 02138.
Department of Chemistry & Chemical Biology, Harvard University, Cambridge, MA 02138.
bioRxiv. 2024 Jul 30:2024.07.29.605395. doi: 10.1101/2024.07.29.605395.
Proteins are dynamic macromolecules. Knowledge of a protein's thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein's structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COLAV (COnformational LAndscape Visualization), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP-1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens also enable detailed mapping of proteins' conformational landscapes.
蛋白质是动态大分子。了解蛋白质的热可及构象对于确定重要转变和设计治疗方法至关重要。可及构象受到蛋白质结构的高度限制，以至于由于外部扰动引起的协同结构变化可能跟踪内在构象转变。这些转变可以被认为是通过构象景观的路径。晶体学药物片段筛选是高通量扰动实验，其中数千个药物靶点晶体用小分子药物前体（片段）浸泡，并检查片段结合情况，在靶蛋白上绘制潜在药物结合位点。在这里，我们描述了一个开源的Python包COLAV（构象景观可视化），用于从此类大规模晶体学扰动研究中推断构象景观。我们将COLAV应用于两个医学上重要系统的药物片段筛选：调节胰岛素信号传导的蛋白酪氨酸磷酸酶1B（PTP-1B）和严重急性呼吸综合征冠状病毒2主蛋白酶（MPro）。有了足够的片段结合结构，我们发现这种药物筛选还能够详细绘制蛋白质的构象景观。
