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A macromolecular structure building toolkit for machine learning and cloud applications

用于机器学习和云应用的大分子结构构建工具包

基本信息

批准号：
BB/X006492/1
负责人：
Kevin Cowtan
金额：
$ 42.86万
依托单位：
University of York
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FX006492%2F1
关键词：
macromolecular structure building toolkit machine

项目摘要

Scientists are interested in the atomic structure of biological molecules: in other words, what the molecules look like. Knowing in detail what a molecule looks like provides important clues to how it might work. If we can go further and capture molecules in the process of interacting with other biological molecules, or artificial compounds such as drugs, we get a clearer picture of how they work.Most of our knowledge of the structure of biological molecules comes from experimental techniques including X-ray crystallography and electron microscopy (EM). These experimental techniques give us pictures of the real molecules in which we can see an outline of the molecular structure, but we can't usually see the individual atoms or tell them apart. So we need to interpret the map in terms of what we know about the molecule from the genetic code which was used to build it. We address this in two ways: through software which allows the user to place atoms using 3D graphics to see the shapes, or by software which tries to do the same process automatically.The automatic process involves lots of steps, from recognizing groups of atoms to linking them up and matching them to the genetic code. Recent advances in computer vision have created huge opportunities to improve automatic interpretation, and scientists working in these areas have produced revolutionary improvements in some of the steps. However these breakthroughs are only useful in combination with the rest of the steps. So we want to break up our automated interpretation software into the individual steps and make those steps very easy for other groups to use. They can then try replacing the step they are interested in with their new code and distribute the resulting method as a complete package.Another interesting element of this work is that it is structured so that the primary benefit of science is to others. Science works by scientists building on the work of others. We have observed that some of the ways in which science is done discourages this - science is done by groups led by senior scientists who are in competition with one another for funds and recognition, which disincentives the sharing of methods and results. We want to test if there is a better way to do science and achieve more progress with less funding by working primarily to benefit others. If we are right, then over the course of 5-10 years we should be able to identify projects which have been enabled by our work, even if we did not initiate or participate in those projects. We will aim to build a qualitative picture of how our approach has impacted practice in the field by comparing project building on our work to projects building on other components or built from scratch.A final strand of this project is to make the tools that we write work in web browsers, so that users do not need to install special software. This will link our work with developments in cloud computing, and we will also adapt our methods to help with advances in predicting the shape of molecules which have come from Google's DeepMind project. This will make the steps of determining molecular structures more accessible to new participants in the field, including students, schools, participants with more limited computing resources such as Chromebooks and mobile devices. Barriers to participation often serve to confine the practice of science to existing privileged groups, so making these methods more widely available will reduce inequalities of opportunity and encourage diversity in the scientific community.

科学家们对生物分子的原子结构感兴趣：换句话说，分子看起来像什么。详细了解一个分子的样子可以为它如何工作提供重要的线索。如果我们能更进一步，在与其他生物分子或人工化合物（如药物）相互作用的过程中捕获分子，我们就能更清楚地了解它们是如何工作的。我们对生物分子结构的大部分知识来自实验技术，包括X射线晶体学和电子显微镜（EM）。这些实验技术为我们提供了真实的分子的图像，我们可以看到分子结构的轮廓，但我们通常看不到单个原子或区分它们。因此，我们需要根据我们从用于构建分子的遗传密码中所了解的分子来解释该图谱。我们通过两种方式解决这个问题：通过允许用户使用3D图形来放置原子以查看形状的软件，或者通过试图自动执行相同过程的软件。自动过程包括许多步骤，从识别原子群到将它们连接起来并将它们与遗传密码相匹配。计算机视觉的最新进展为改进自动判读创造了巨大的机会，在这些领域工作的科学家们在某些步骤上取得了革命性的进步。然而，这些突破只有在与其他步骤相结合时才有用。因此，我们希望将我们的自动解释软件分解为各个步骤，并使这些步骤非常容易为其他组使用。然后，他们可以尝试用新代码替换他们感兴趣的步骤，并将生成的方法作为一个完整的包分发。这项工作的另一个有趣的元素是，它的结构使科学的主要利益是他人。科学工作是科学家在他人工作的基础上进行的。我们注意到，从事科学研究的某些方式阻碍了这一点-科学研究是由资深科学家领导的小组进行的，他们相互竞争资金和认可，这阻碍了方法和成果的分享。我们想测试是否有一种更好的方式来做科学，并通过主要为造福他人而工作，以更少的资金取得更大的进展。如果我们是对的，那么在5-10年的时间里，我们应该能够确定我们的工作所促成的项目，即使我们没有发起或参与这些项目。我们的目标是通过比较基于我们工作的项目构建与基于其他组件或从头开始构建的项目，来构建一个定性的图片，说明我们的方法如何影响该领域的实践。这个项目的最后一个环节是让我们编写的工具在Web浏览器中工作，这样用户就不需要安装特殊的软件。这将把我们的工作与云计算的发展联系起来，我们还将调整我们的方法，以帮助预测来自谷歌DeepMind项目的分子形状。这将使该领域的新参与者更容易获得确定分子结构的步骤，包括学生、学校、计算资源更有限的参与者，如Chromebook和移动的设备。参与的障碍往往使科学实践局限于现有的特权群体，因此，更广泛地提供这些方法将减少机会不平等，鼓励科学界的多样性。