Goldschmidt Lukasz, Cooper David R, Derewenda Zygmunt S, Eisenberg David
Howard Hughes Medical Institute, University of California, Los Angeles-DOE Institute of Genomics and Proteomics, Los Angeles, California 90095-1570, USA.
Protein Sci. 2007 Aug;16(8):1569-76. doi: 10.1110/ps.072914007.
Growing well-diffracting crystals constitutes a serious bottleneck in structural biology. A recently proposed crystallization methodology for "stubborn crystallizers" is to engineer surface sequence variants designed to form intermolecular contacts that could support a crystal lattice. This approach relies on the concept of surface entropy reduction (SER), i.e., the replacement of clusters of flexible, solvent-exposed residues with residues with lower conformational entropy. This strategy minimizes the loss of conformational entropy upon crystallization and renders crystallization thermodynamically favorable. The method has been successfully used to crystallize more than 15 novel proteins, all stubborn crystallizers. But the choice of suitable sites for mutagenesis is not trivial. Herein, we announce a Web server, the surface entropy reduction prediction server (SERp server), designed to identify mutations that may facilitate crystallization. Suggested mutations are predicted based on an algorithm incorporating a conformational entropy profile, a secondary structure prediction, and sequence conservation. Minor considerations include the nature of flanking residues and gaps between mutation candidates. While designed to be used with default values, the server has many user-controlled parameters allowing for considerable flexibility. Within, we discuss (1) the methodology of the server, (2) how to interpret the results, and (3) factors that must be considered when selecting mutations. We also attempt to benchmark the server by comparing the server's predictions with successful SER structures. In most cases, the structure yielding mutations were easily identified by the SERp server. The server can be accessed at http://www.doe-mbi.ucla.edu/Services/SER.
生长出衍射良好的晶体是结构生物学中的一个严重瓶颈。最近针对“顽固结晶蛋白”提出的一种结晶方法是设计表面序列变体，使其形成能够支撑晶格的分子间接触。这种方法依赖于表面熵降低（SER）的概念，即用构象熵较低的残基取代柔性的、暴露于溶剂中的残基簇。该策略可使结晶过程中构象熵的损失最小化，并使结晶在热力学上变得有利。该方法已成功用于使超过15种新型蛋白质结晶，这些都是顽固结晶蛋白。但是选择合适的诱变位点并非易事。在此，我们宣布推出一个网络服务器——表面熵降低预测服务器（SERp服务器），旨在识别可能促进结晶的突变。基于一种结合构象熵图谱、二级结构预测和序列保守性的算法来预测建议的突变。次要考虑因素包括侧翼残基的性质以及突变候选位点之间的间隔。虽然该服务器设计为使用默认值，但它有许多用户可控制的参数，具有相当大的灵活性。在此，我们讨论（1）服务器的方法，（2）如何解释结果，以及（3）选择突变时必须考虑的因素。我们还试图通过将服务器的预测与成功的SER结构进行比较来对服务器进行基准测试。在大多数情况下，产生突变的结构很容易被SERp服务器识别。可通过http://www.doe-mbi.ucla.edu/Services/SER访问该服务器。
