Galeazzi Flavia Maria, Monteiro da Silva Gabriel, Arantes Pablo, Varghese Iz, Shukla Ananya, Rubenstein Brenda M
Brown University, Providence, Rhode Island 02912, USA.
Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, Rhode Island 02912, USA.
bioRxiv. 2025 May 14:2025.05.09.653048. doi: 10.1101/2025.05.09.653048.
Deep learning approaches like AlphaFold 2 (AF2) have revolutionized structural biology by accurately predicting the ground state structures of proteins. Recently, clustering and subsampling techniques that manipulate multiple sequence alignment (MSA) inputs into AlphaFold to generate conformational ensembles of proteins have also been proposed. Although many of these techniques have been made open source, they often require integrating multiple packages and can be challenging for researchers who have a limited programming background to employ. This is especially true when researchers are interested in subsampling to produce predictions of protein conformational ensembles, which require multiple computational steps. This manuscript introduces FastConformation, a Python-based application that integrates MSA generation, structure prediction via AF2, and interactive analysis of protein conformations and their distributions, all in one place. FastConformation is accessible through a user-friendly GUI suitable for non-programmers, allowing users to iteratively refine subsampling parameters based on their analyses to achieve diverse conformational ensembles. Starting from an amino acid sequence, users can make protein conformation predictions and analyze results in just a few hours on their local machines, which is significantly faster than traditional molecular dynamics (MD) simulations. Uniquely, by leveraging the subsampling of MSAs, our tool enables the generation of alternative protein conformations. We demonstrate the utility of FastConformation on proteins including the Abl1 kinase, LAT1 transporter, and CCR5 receptor, showcasing its ability to predict and analyze the protein conformational ensembles and effects of mutations on a variety of proteins. This tool enables a wide range of high-throughput applications in protein biochemistry, drug discovery, and protein engineering.
像AlphaFold 2（AF2）这样的深度学习方法通过准确预测蛋白质的基态结构，给结构生物学带来了革命性的变化。最近，也有人提出了聚类和子采样技术，这些技术将多序列比对（MSA）输入到AlphaFold中，以生成蛋白质的构象集合。尽管这些技术中的许多已经开源，但它们通常需要集成多个软件包，对于编程背景有限的研究人员来说，使用起来可能具有挑战性。当研究人员对通过子采样来生成蛋白质构象集合的预测感兴趣时尤其如此，因为这需要多个计算步骤。本文介绍了FastConformation，这是一个基于Python的应用程序，它将MSA生成、通过AF2进行结构预测以及蛋白质构象及其分布的交互式分析集成在一个地方。FastConformation可以通过一个适合非程序员的用户友好型图形用户界面（GUI）访问，允许用户根据自己的分析迭代优化子采样参数，以获得多样化的构象集合。从氨基酸序列开始，用户可以在本地机器上只需几个小时就能进行蛋白质构象预测并分析结果，这比传统的分子动力学（MD）模拟要快得多。独特的是，通过利用MSA的子采样，我们的工具能够生成替代的蛋白质构象。我们展示了FastConformation在包括Abl1激酶、LAT1转运蛋白和CCR5受体等蛋白质上的实用性，展示了其预测和分析蛋白质构象集合以及突变对各种蛋白质影响的能力。这个工具能够在蛋白质生物化学、药物发现和蛋白质工程中实现广泛的高通量应用。
