Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
J Comput Aided Mol Des. 2022 Sep;36(9):677-686. doi: 10.1007/s10822-022-00471-4. Epub 2022 Aug 26.
Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot .
分子可视化是结构生物学的基石，为理解生物分子的形态和功能提供了独特的视角，而这些是其他方法难以实现的。科学分析、发表、教育和宣传工作通常受益于使用 Maya、3ds Max 和 Blender 等高级计算机图形程序渲染的逼真的分子描述。然而，即使对于专家用户来说，在这些程序中设置分子场景也很繁琐，渲染通常需要大量的时间和计算机资源。我们创建了一个名为 Prot2Prot 的深度学习模型，它可以快速模仿逼真的可视化风格，而只需使用更简单、易于生成的分子表示形式。生成的图像通常与使用行业标准 3D 图形程序渲染的图像难以区分，但它们可以在更短的时间内创建，即使在网络浏览器中运行也是如此。据我们所知，Prot2Prot 是首次将图像到图像的转换应用于大分子可视化的示例。Prot2Prot 可免费使用，并根据 Apache License, Version 2.0 发布。用户可以在无需注册的情况下访问 Prot2Prot 支持的网络应用程序，网址为 http://durrantlab.com/prot2prot 。
