Afpdb - an efficient structure manipulation package for AI protein design.

Bioinformatics (Oxford, England) Pub Date : 2024-11-05 DOI:10.1093/bioinformatics/btae654

Yingyao Zhou, Jiayi Cox, Bin Zhou, Steven Zhu, Yang Zhong, Glen Spraggon

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Abstract

Motivation: The advent of AlphaFold and other protein Artificial Intelligence (AI) models has transformed protein design, necessitating efficient handling of large-scale data and complex workflows. Using existing programming packages that predate recent AI advancements often leads to inefficiencies in human coding and slow code execution. To address this gap, we developed the Afpdb package.

Results: Afpdb, built on AlphaFold's NumPy architecture, offers a high-performance core. It uses RFDiffusion's contig syntax to streamline residue and atom selection, making coding simpler and more readable. Integrating PyMOL's visualization capabilities, Afpdb allows automatic visual quality control. With over 180 methods commonly used in protein AI design, which are otherwise hard to find, Afpdb enhances productivity in structural biology by supporting the development of concise, high-performance code.

Availability: Code and documentation are available on GitHub (https://github.com/data2code/afpdb) and PyPI (https://pypi.org/project/afpdb). An interactive tutorial is accessible through Google Colab.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Afpdb - 用于人工智能蛋白质设计的高效结构操作软件包。

动机AlphaFold 和其他蛋白质人工智能（AI）模型的出现改变了蛋白质设计，要求高效处理大规模数据和复杂的工作流程。使用现有的编程软件包往往会导致人工编码效率低下和代码执行缓慢。为了弥补这一不足，我们开发了 Afpdb 程序包：Afpdb 基于 AlphaFold 的 NumPy 架构，提供了一个高性能核心。它使用 RFDiffusion 的 contig 语法来简化残基和原子选择，使编码更简单、更易读。Afpdb 集成了 PyMOL 的可视化功能，可自动进行可视化质量控制。Afpdb 拥有 180 多种蛋白质 AI 设计中常用的方法，这些方法在其他地方很难找到，Afpdb 通过支持开发简洁、高性能的代码，提高了结构生物学的生产率：代码和文档可在 GitHub (https://github.com/data2code/afpdb) 和 PyPI (https://pypi.org/project/afpdb) 上获取。可通过 Google Colab 获取互动教程：补充数据可在 Bioinformatics online 上获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bioinformatics (Oxford, England)

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