论文标题
内部坐标之间的学习相关性，以改善蛋白质的3D笛卡尔坐标
Learning Correlations between Internal Coordinates to improve 3D Cartesian Coordinates for Proteins
论文作者
论文摘要
我们考虑了内部坐标（键长，价角和二面角）的通用表示问题，并将其转化为生物分子的三维笛卡尔坐标。我们表明，内部对 - 牙犯的过程依赖于正确预测内部坐标本身之间化学微妙的相关性，并且学习这些相关性增加了笛卡尔代表的忠诚度。该蛋白质的机器学习解决了这个一般问题，但是对于任何类型的链生物分子，包括RNA，DNA和脂质的任何类型的链生物分子都可以扩展数据。我们表明，内部对 - 牙犯的过程依赖于正确预测内部坐标本身之间化学微妙的相关性，并且学习这些相关性增加了笛卡尔代表的忠诚度。我们开发了一种机器学习算法INT2CART，以预测蛋白质的骨架扭转角度和残基类型的键长和键角，并允许比使用固定键长和键角或静态库方法更好地重建蛋白质结构，该方法依赖于单个残基上的骨架扭转角度和残基类型。 INT2CART算法已在https://github.com/thglab/int2cart上作为单个Python软件包实现。
We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordinates of a biomolecule. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. This general problem has been solved with machine learning for proteins, but with appropriately formulated data is extensible to any type of chain biomolecule including RNA, DNA, and lipids. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. We developed a machine learning algorithm, Int2Cart, to predict bond lengths and bond angles from backbone torsion angles and residue types of a protein, and allows reconstruction of protein structures better than using fixed bond lengths and bond angles, or a static library method that relies on backbone torsion angles and residue types on a single residue. The Int2Cart algorithm has been implemented as an individual python package at https://github.com/THGLab/int2cart.
