Ab initio protein modelling for automated X-ray crystal structure solution

用于自动 X 射线晶体结构解决方案的从头算蛋白质建模

• 批准号: BB/H01330X/1
• 负责人: Daniel Rigden
• 金额: $ 22.65万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FH01330X%2F1
• 关键词: Ab; initio; protein; modelling; automated

Proteins make up the functional machinery of all living beings. Their particular roles depend on their 3-dimensional structures which allow given proteins to interact specifically with other molecules in their environment. Some proteins - enzymes - go further and can transform certain compounds into others. To understand better how proteins work and be able to use them in industry and medicine, scientists are greatly interested in figuring out their 3-dimensional structures. There are various ways to do this, but the dominant technique is X-ray crystallography. In this, an intense beam of X-rays is fired at a protein crystal. The X-rays are diffracted when passing through the crystal, producing a pattern of rays that is characteristic of the protein under study. In order to elucidate the structure of the protein, information derived from multiple diffraction patterns obtained from the same protein but under different conditions must be drawn together. The acquisition of such extra diffraction patterns can be time consuming, expensive, and commonly involves hazardous chemicals. A technique exists, however, where computers substitute the additional experiments by estimating equivalent information from available structures of proteins similar to that under study. In this way, protein structures can be solved from one single diffraction pattern. This technique - called Molecular Replacement (MR) - is fast, economical, clean and often uncomplicated. However, since MR relies on pre-existing structures, it is not applicable to many proteins of interest, for which similar structures are simply not available. For many years, scientists have tried to develop computer methods to predict the structure of proteins, purely based on their sequences. These methods are generally called ab initio modelling methods. Over the past decade, these efforts have started to bear fruit. These predicted models are unlikely to substitute for crystal structures any time soon since they typically contain errors, but recent work has shown that they are sometimes close enough to the real structure for them to be used in the MR process. This is the main idea behind this proposal - to adapt current ab initio modelling procedures to the specific needs of MR. With ab initio modelling, it is generally the case that the more detailed (i.e. the longer) the computer calculation, the better the model you can make. Unfortunately, achieving the best models is so demanding that it often requires extensive calculation times or access to supercomputers or other vast computer resources. Few crystallographers have access to these facilities, making the modelling method impractical. We therefore propose a different approach, making efficient use of simpler models that can be easily obtained on typical computers. In our preliminary work, we have already proven that this approach can work successfully for MR. What we want to do now is find the best way to produce optimal models and to do this automatically. This effectively means adapting the method to meet the demands of modern X-ray crystallography, making it fast so that it can be used as a routine approach and accessible to other crystallographers without specialist knowledge of ab initio modelling. We then want to include the method in the MrBUMP program, which is a well-established package allowing for easy, automated MR. MrBUMP can be added to a software package called CCP4i that is widely used by crystallographers. By incorporating our processing method in a familiar program, we expect it to become widely used across the world. We expect that by extending the MR computational approach we will enable protein structures to be determined more quickly and cheaply. In this way, research in all sorts of areas that depend on protein structure information, like drug design, will proceed faster.

蛋白质构成了所有生物的功能机制。它们的特殊作用取决于它们的三维结构，这些结构允许给定的蛋白质与其环境中的其他分子特异性地相互作用。一些蛋白质-酶-更进一步，可以将某些化合物转化为其他化合物。为了更好地了解蛋白质的工作原理，并能够将其用于工业和医学，科学家们对弄清楚它们的三维结构非常感兴趣。有各种方法可以做到这一点，但占主导地位的技术是X射线晶体学。在这个实验中，一束强烈的X射线射向蛋白质晶体。X射线在穿过晶体时发生衍射，产生一种射线图案，这是所研究的蛋白质的特征。为了阐明蛋白质的结构，必须将来自同一蛋白质但在不同条件下获得的多个衍射图案的信息放在一起。这种额外衍射图案的获取可能是耗时的、昂贵的，并且通常涉及危险化学品。然而，存在一种技术，即计算机通过从与所研究的蛋白质类似的可用结构中估计等效信息来替代额外的实验。通过这种方式，蛋白质结构可以从一个单一的衍射图案中解析出来。这种技术-称为分子置换（MR）-是快速，经济，清洁，往往不复杂。然而，由于MR依赖于预先存在的结构，因此它不适用于许多感兴趣的蛋白质，对于这些蛋白质，类似的结构根本不可用。多年来，科学家们一直试图开发计算机方法来预测蛋白质的结构，纯粹基于它们的序列。这些方法通常被称为从头建模方法。在过去十年中，这些努力已开始取得成果。这些预测的模型不太可能很快取代晶体结构，因为它们通常包含错误，但最近的工作表明，它们有时足够接近真实的结构，可以用于MR过程。这是本提案背后的主要思想-使当前的从头计算建模程序适应MR的具体需求。使用从头计算建模，通常情况下，计算机计算越详细（即越长），模型就越好。不幸的是，实现最佳模型的要求非常高，通常需要大量的计算时间或访问超级计算机或其他庞大的计算机资源。很少有晶体学家能够使用这些设备，使得建模方法不切实际。因此，我们提出了一种不同的方法，有效地利用更简单的模型，可以很容易地获得典型的计算机。在我们的初步工作中，我们已经证明这种方法可以成功地用于MR。我们现在要做的是找到产生最佳模型的最佳方法，并自动完成。这实际上意味着调整方法以满足现代X射线晶体学的需求，使其快速，以便它可以用作常规方法，并且可以被其他晶体学家使用，而无需从头算建模的专业知识。然后，我们希望将该方法包含在MrBUMP程序中，该程序是一个成熟的软件包，可以轻松实现自动MR。MrBUMP可以添加到晶体学家广泛使用的名为CCP 4 i的软件包中。通过将我们的处理方法融入到一个熟悉的程序中，我们希望它能在世界范围内得到广泛应用。我们希望通过扩展MR计算方法，我们将能够更快，更便宜地确定蛋白质结构。通过这种方式，依赖于蛋白质结构信息的各种领域的研究，如药物设计，将更快地进行。

项目成果

Exploring the speed and performance of molecular replacement with AMPLE using QUARK ab initio protein models.

• DOI: 10.1107/s1399004714025784
• 发表时间: 2015-02
• 期刊: Acta crystallographica. Section D, Biological crystallography
• 作者: Keegan RM;Bibby J;Thomas J;Xu D;Zhang Y;Mayans O;Winn MD;Rigden DJ
• 通讯作者: Rigden DJ

Routine phasing of coiled-coil protein crystal structures with AMPLE.

• DOI: 10.1107/s2052252515002080
• 发表时间: 2015-03-01
• 期刊: IUCrJ
• 影响因子: 3.9
• 作者: Thomas JM;Keegan RM;Bibby J;Winn MD;Mayans O;Rigden DJ
• 通讯作者: Rigden DJ

Application of the AMPLE cluster-and-truncate approach to NMR structures for molecular replacement.

• DOI: 10.1107/s0907444913018453
• 发表时间: 2013-11
• 期刊: Acta crystallographica. Section D, Biological crystallography
• 作者: Bibby J;Keegan RM;Mayans O;Winn MD;Rigden DJ
• 通讯作者: Rigden DJ

Structural and mechanistic insights into an archaeal DNA-guided Argonaute protein

• DOI: 10.1038/nmicrobiol.2017.35
• 发表时间: 2017-03
• 期刊: Nature Microbiology
• 影响因子: 28.3
• 作者: S. Willkomm;C. A. Oellig;Adrian Zander;T. Restle;R. Keegan;Dina Grohmann;S. Schneider