使用 Galaxy 实现蛋白质-蛋白质相互作用的快速准确全基因组预测和结构建模。
Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy.
机构信息
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Department of Biochemistry and Molecular Biology, Penn State University, College Park, PA, USA.
出版信息
BMC Bioinformatics. 2023 Jun 23;24(1):263. doi: 10.1186/s12859-023-05389-8.
BACKGROUND
Protein-protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein-protein interactions and produce high-quality multimeric structural models.
RESULTS
Application of our method to the Human and Yeast genomes yield protein-protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2's non-structural protein 3. We also produced models of SARS-CoV2's spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4.
CONCLUSIONS
The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.
背景
蛋白质-蛋白质相互作用在几乎所有细胞过程中都起着至关重要的作用。鉴定相互作用的蛋白质可以深入了解生物，并为疾病治疗提供新的药物靶点。在这里，我们提出了一个公开的、自动化的流水线，用于预测全基因组蛋白质-蛋白质相互作用，并生成高质量的多聚体结构模型。
结果
将我们的方法应用于人类和酵母基因组，得到的蛋白质-蛋白质相互作用网络的质量与常见的实验方法相似。我们鉴定并建模了可能与 SARS-CoV2 的非结构蛋白 3 的木瓜蛋白酶样蛋白酶相互作用的人类蛋白质。我们还生成了 SARS-CoV2 的刺突蛋白（S）与髓鞘少突胶质细胞糖蛋白受体和二肽基肽酶-4 相互作用的模型。
结论
所提出的方法能够自信地识别相互作用，同时为实验验证提供高质量的多聚体结构模型。互作组建模流水线可在 usegalaxy.org 和 usegalaxy.eu 上使用。
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