论文标题
Nrgten Python包装：一种可扩展的工具包
The NRGTEN Python package: an extensible toolkit for coarse-grained normal mode analysis of proteins, nucleic acids, small molecules and their complexes
论文作者
论文摘要
摘要：粗粒正常模式分析（NMA）是研究生物分子动力学的快速计算技术。在这里，我们介绍了用于弹性网络的Najmanovich研究小组工具包（NRGTEN）。 NRGTEN是一个Python工具包，除了流行和新颖的指标外，还实现了四种不同的NMA模型，从这些模型中进行基准测试和测量属性。此外，该工具包可作为公共Python软件包可用，并且很容易扩展用于开发或实施其他NMA模型。值得注意的是，我们组在NRGTEN内开发的encom模型（弹性网络接触模型）值得注意，这是由于其原子相互作用的特定化学性质所致。这使得突变影响的某些独特预测，例如对稳定性（通过振动熵差的变化），对不同构象状态之间的过渡概率或整个大分子/复合物（研究变构和信号传导）的柔韧性曲线的过渡概率。此外，所有NMA模型均可用来从起始结构中生成构象合团，以帮助应用应用之间蛋白质蛋白质，蛋白质 - 配体或其他对接研究。 NRGTEN可以通过公共Python软件包免费获得，该软件包可以轻松安装在任何现代机器上，并包含在线托管的详细用户指南。可用性和实施：https：//github.com/gregorpatof/nrgten_package/联系：[email protected]
Summary: Coarse-grained normal mode analysis (NMA) is a fast computational technique to study the dynamics of biomolecules. Here we present the Najmanovich Research Group Toolkit for Elastic Networks (NRGTEN). NRGTEN is a Python toolkit that implements four different NMA models in addition to popular and novel metrics to benchmark and measure properties from these models. Furthermore, the toolkit is available as a public Python package and is easily extensible for the development or implementation of additional NMA models. The inclusion of the ENCoM model (Elastic Network Contact Model) developed in our group within NRGTEN is noteworthy, owing to its account for the specific chemical nature of atomic interactions. This makes possible some unique predictions of the effect of mutations, such as on stability (via changes in vibrational entropy differences), on the transition probability between different conformational states or on the flexibility profile of the whole macromolecule/complex (to study allostery and signalling). In addition, all NMA models can be used to generate conformational ensembles from a starting structure to aid in protein-protein, protein-ligand or other docking studies among applications. NRGTEN is freely available via a public Python package which can be easily installed on any modern machine and includes a detailed user guide hosted online. Availability and implementation: https://github.com/gregorpatof/nrgten_package/ Contact: [email protected]
