School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB United Kingdom.
School of Biological Sciences, University of Edinburgh, Roger Land Building, Edinburgh EH9 3FF, United Kingdom.
Protein Eng Des Sel. 2024 Jan 29;37. doi: 10.1093/protein/gzae002.
Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for the end user. Deep learning-based methods offer an attractive alternative, outperforming physics-based methods at a significantly lower computational cost. In this paper, we explore the application of Convolutional Neural Networks (CNNs) for sequence design. We describe the development and benchmarking of a range of networks, as well as reimplementations of previously described CNNs. We demonstrate the flexibility of representing proteins in a three-dimensional voxel grid by encoding additional design constraints into the input data. Finally, we describe TIMED-Design, a web application and command line tool for exploring and applying the models described in this paper. The user interface will be available at the URL: https://pragmaticproteindesign.bio.ed.ac.uk/timed. The source code for TIMED-Design is available at https://github.com/wells-wood-research/timed-design.
序列设计是设计或工程蛋白质过程中的关键步骤。传统上，基于物理的方法被用于求解最优序列，其主要缺点是对最终用户来说计算量很大。基于深度学习的方法提供了一种有吸引力的替代方法，以显著较低的计算成本超过基于物理的方法。在本文中，我们探讨了卷积神经网络（CNN）在序列设计中的应用。我们描述了一系列网络的开发和基准测试，以及之前描述的 CNN 的重新实现。我们通过将额外的设计约束编码到输入数据中，展示了在三维体素网格中表示蛋白质的灵活性。最后，我们描述了 TIMED-Design，这是一个用于探索和应用本文中描述的模型的 Web 应用程序和命令行工具。用户界面将在以下 URL 可用：https://pragmaticproteindesign.bio.ed.ac.uk/timed。TIMED-Design 的源代码可在以下网址获得：https://github.com/wells-wood-research/timed-design。
