Methods to accelerate protein structure determination by solution NMR

NMR化学位移为蛋白质提供了重要的局部结构信息。对于具有高达约100-130个残基的大小的蛋白质，从NMR化学位移数据产生一致的结构已经变得可行，并且这样的结构具有与用标准NMR方案获得的那些结构相当的质量。 在与大卫贝克博士及其团队的合作中，我们开发了一种化学位移引导的方法，可以成功准确地根据化学位移确定结构，但在实践中，该方法仅限于相对较小的蛋白质。 新的工作重点是扩展这种方法，以允许纳入易于访问的实验信息，并更广泛地利用现有的数据库，以前解决的结构。 通过优化的神经网络算法斯巴达+，我们能够估计已知结构的蛋白质的化学位移。 使用这个程序，我们能够分配近似的化学位移值晶体学确定的结构。 标准生物信息学工具最初设计用于搜索具有远程同源序列的蛋白质，现在可以适用于在数据库中搜索具有同源化学位移的序列，从而找到具有相似结构的蛋白质链，而不需要任何序列同源性。 该方法被证明是非常强大的，并且能够使用现有的算法来处理序列中的间隙时，搜索结构同源物。 该方法的效率随着蛋白质的大小而增加，因此代表了我们与Baker小组合作开发的CS-Rosetta方法的理想补充。 然而，它确实需要在数据库中存在先前解决的蛋白质的类似折叠，并且如果没有好的结构模板，则不会达到收敛。该方法在计算上相当苛刻，并且需要访问集群计算。

NMR chemical shifts provide important local structural information for proteins. Consistent structure generation from NMR chemical shift data has become feasible for proteins with sizes of up to about 100-130 residues, and such structures are of a quality comparable to those obtained with the standard NMR protocol. In collaboration with Dr. David Baker and his group, we developed a chemical-shift-guided approach to successfully and accurately determine structures on the basis of chemical shifts, but in practice the approach was limited to relatively small proteins. New work focuses on extending this approach to allow incorporation of easily accessible experimental information and more extensively exploit the available database of previously solved structures. By means of an optimized neural network algorithm, SPARTA+, we are able to estimate chemical shifts for proteins of known structure. Using this program, we are able to assign approximate chemical shift values to crystallographically determined structures. Standard bioinformatics tools, that have been designed originally for searching for proteins of remotely homologous sequence, can be adapted to now search the database for sequences with homologous chemical shifts and thereby find protein chains of similar structure, without requiring any sequence homology. The approach is proving to be very robust, and is able to use existing algorithms to deal with gaps in the sequence when searching for structural homologs. The method increases in efficiency with the size of the protein and therefore represents an ideal complement to the CS-Rosetta approach developed earlier by us in collaboration with the Baker group. It does require, however, the presence of prior solved protein of a similar fold in the database and will not reach convergence if no good structural templates can be found. The approach is computationally rather demanding, and requires access to cluster computing.