Hofmann Tommy, Fischer Axel W, Meiler Jens, Kalkhof Stefan
Department of Proteomics, Helmholtz-Centre for Environmental Research - UFZ, Leipzig D-04318, Germany.
Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA.
Methods. 2015 Nov 1;89:79-90. doi: 10.1016/j.ymeth.2015.05.014. Epub 2015 May 15.
Recent development of high-resolution mass spectrometry (MS) instruments enables chemical crosslinking (XL) to become a high-throughput method for obtaining structural information about proteins. Restraints derived from XL-MS experiments have been used successfully for structure refinement and protein-protein docking. However, one formidable question is under which circumstances XL-MS data might be sufficient to determine a protein's tertiary structure de novo? Answering this question will not only include understanding the impact of XL-MS data on sampling and scoring within a de novo protein structure prediction algorithm, it must also determine an optimal crosslinker type and length for protein structure determination. While a longer crosslinker will yield more restraints, the value of each restraint for protein structure prediction decreases as the restraint is consistent with a larger conformational space. In this study, the number of crosslinks and their discriminative power was systematically analyzed in silico on a set of 2055 non-redundant protein folds considering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactive crosslinkers between 1 and 60Å. Depending on the protein size a heuristic was developed that determines the optimal crosslinker length. Next, simulated restraints of variable length were used to de novo predict the tertiary structure of fifteen proteins using the BCL::Fold algorithm. The results demonstrate that a distinct crosslinker length exists for which information content for de novo protein structure prediction is maximized. The sampling accuracy improves on average by 1.0 Å and up to 2.2 Å in the most prominent example. XL-MS restraints enable consistently an improved selection of native-like models with an average enrichment of 2.1.
高分辨率质谱(MS)仪器的最新发展使化学交联(XL)成为一种获取蛋白质结构信息的高通量方法。来自XL-MS实验的约束条件已成功用于结构优化和蛋白质-蛋白质对接。然而,一个严峻的问题是,在哪些情况下XL-MS数据足以从头确定蛋白质的三级结构?回答这个问题不仅需要理解XL-MS数据对从头蛋白质结构预测算法中的采样和评分的影响,还必须确定用于蛋白质结构测定的最佳交联剂类型和长度。虽然较长的交联剂会产生更多的约束条件,但随着约束条件与更大的构象空间一致,每个约束条件对蛋白质结构预测的价值会降低。在这项研究中,考虑了1至60埃之间的赖氨酸-赖氨酸、赖氨酸-天冬氨酸、赖氨酸-谷氨酸、半胱氨酸-半胱氨酸和精氨酸-精氨酸反应性交联剂,在一组2055个非冗余蛋白质折叠上通过计算机系统分析了交联的数量及其区分能力。根据蛋白质大小,开发了一种启发式方法来确定最佳交联剂长度。接下来,使用可变长度的模拟约束条件,通过BCL::Fold算法从头预测了15种蛋白质的三级结构。结果表明,存在一个独特的交联剂长度,对于该长度,从头蛋白质结构预测的信息含量最大化。采样精度平均提高了1.0埃,在最突出的例子中提高了2.2埃。XL-MS约束条件始终能够改进对类似天然模型的选择,平均富集度为2.1。