论文标题
Dynasig-Ml Python软件包：生物分子动力学关系关系的自动化学习
The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships
论文作者
论文摘要
摘要：Dynasig-Ml（动力学签名 - 机器学习）Python软件包允许使用大量序列变体的实验度量数据集对生物分子中的3D动态功能 - 功能关系的有效探索。 DynAsig-ML软件包是围绕弹性网络触点模型（ENCOM）构建的，这是第一个也是唯一对序列敏感的粗粒NMA模型，该模型用于生成输入动态签名。从硅突变结构开始，整个管道只能使用几行Python和适度的计算资源运行。在大型生物分子或大量序列变体的情况下，计算密集型步骤也很容易平行。作为示例应用程序，我们使用Dynasig-ML软件包来预测深度突变扫描数据中细菌酶VIM-2乳糖酶的进化适应性。可用性和实施：Dynasig-Ml是开源软件，网址为https：//github.com/gregorpatof/dynasigml软件包联系人：[email protected]
Summary: The DynaSig-ML (Dynamical Signatures - Machine Learning) Python package allows the efficient, user-friendly exploration of 3D dynamics-function relationships in biomolecules, using datasets of experimental measures from large numbers of sequence variants. The DynaSig-ML package is built around the Elastic Network Contact Model (ENCoM), the first and only sequence-sensitive coarse-grained NMA model, which is used to generate the input Dynamical Signatures. Starting from in silico mutated structures, the whole pipeline can be run with just a few lines of Python and modest computational resources. The compute-intensive steps can also easily be parallelized in the case of either large biomolecules or vast amounts of sequence variants. As an example application, we use the DynaSig-ML package to predict the evolutionary fitness of the bacterial enzyme VIM-2 lactamase from deep mutational scan data. Availability and implementation: DynaSig-ML is open source software available at https: //github.com/gregorpatof/dynasigml package Contact: [email protected]
