Generalized biomolecular modeling and design with RoseTTAFold all-atom

Although AlphaFold2 (AF2) and RoseTTAFold (RF) have transformed structural biology by enabling high-accuracy protein structure modeling, they are unable to model covalent modifications or interactions with small molecules and other non-protein molecules that can play key roles in biological function. Here, we describe RoseTTAFold All-Atom (RFAA), a deep network capable of modeling full biological assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications given the sequences of the polymers and the atomic bonded geometry of the small molecules and covalent modifications. Following training on structures of full biological assemblies in the Protein Data Bank (PDB), RFAA has comparable protein structure prediction accuracy to AF2, excellent performance in CAMEO for flexible backbone small molecule docking, and reasonable prediction accuracy for protein covalent modifications and assemblies of proteins with multiple nucleic acid chains and small molec..., , , # Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom

[https://doi.org/10.5061/dryad.mcvdnck6v](https://doi.org/10.5061/dryad.mcvdnck6v)

## Description of the data and file structure

This is a deposition of data for the manuscript \"Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom which describes two methods:

1. A structure prediction method that can accept, proteins, nucleic acids, small molecules and covalent modifications. (RoseTTAFold All-Atom)
2. A generative model that generates protein structures around small molecules. (RFDiffusion All-Atom)

In this data deposition, there are a set of folders.

A. fig2_structures, which contain pymol sessions used to make images in figure 2 of the manuscript. These are examples of predicted protein small molecule complexes.

B. fig3_structures, which contain pymol sessions used to make images in figure 3 of the manuscript. These are examples of predicted covalent modifications to proteins.

C. poseb...

尽管AlphaFold2（AlphaFold2，简称AF2）与RoseTTAFold（RoseTTAFold，简称RF）凭借高精度蛋白质结构建模能力革新了结构生物学领域，但二者均无法对共价修饰，或是与小分子、其他非蛋白质分子间的相互作用进行建模——而这些要素在生物功能中往往发挥关键作用。本文介绍RoseTTAFold全原子模型（RoseTTAFold All-Atom，简称RFAA），一款深度学习网络，可基于聚合物序列与小分子、共价修饰的原子键合几何结构，对包含蛋白质、核酸、小分子、金属离子以及共价修饰的完整生物组装体进行建模。在基于蛋白质数据银行（Protein Data Bank，PDB）收录的完整生物组装体结构完成训练后，RFAA的蛋白质结构预测精度可与AF2媲美，在针对柔性骨架小分子对接的CAMEO评测中展现出优异性能，同时对蛋白质共价修饰以及包含多条核酸链与小分子的蛋白质组装体的预测精度亦较为合理。# 《基于RoseTTAFold全原子模型的通用生物分子建模与设计》（Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom）

https://doi.org/10.5061/dryad.mcvdnck6v

## 数据与文件结构说明
本数据集为上述手稿的配套数据存档，该论文阐述了两种方法：
1. RoseTTAFold全原子模型（RoseTTAFold All-Atom）：一种可接收蛋白质、核酸、小分子与共价修饰作为输入的结构预测方法；
2. RFDiffusion全原子模型（RFDiffusion All-Atom）：一种可围绕小分子生成蛋白质结构的生成式模型。

本次数据存档包含以下文件夹：
A. fig2_structures：存储用于制作论文图2图像的PyMol会话文件，为预测得到的蛋白质-小分子复合物示例；
B. fig3_structures：存储用于制作论文图3图像的PyMol会话文件，为预测得到的蛋白质共价修饰示例；
C. poseb...