Robertson Timothy A, Varani Gabriele
Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA.
Proteins. 2007 Feb 1;66(2):359-74. doi: 10.1002/prot.21162.
We have developed an all-atom statistical potential function for the prediction of protein-DNA interactions from their structures, and show that this method outperforms similar, lower-resolution statistical potentials in a series of decoy discrimination experiments. The all-atom formalism appears to capture details of atomic interactions that are missed by the lower-resolution methods, with the majority of the discriminatory power arising from its description of short-range atomic contacts. We show that, on average, the method is able to identify 90% of near-native docking decoys within the best-scoring 10% of structures in a given decoy set, and it compares favorably with an optimized physical potential function in a test of structure-based identification of DNA binding-sequences. These results demonstrate that all-atom statistical functions specific to protein-DNA interactions can achieve great discriminatory power despite the limited size of the structural database. They also suggest that the statistical scores may soon be able to achieve accuracy on par with more complex, physical potential functions.
我们开发了一种全原子统计势函数，用于从蛋白质-DNA复合物的结构预测其相互作用，并表明在一系列诱饵鉴别实验中，该方法优于类似的低分辨率统计势。全原子形式似乎捕捉到了低分辨率方法所遗漏的原子相互作用细节，其大部分鉴别能力来自于对短程原子接触的描述。我们表明，平均而言，该方法能够在给定诱饵集中得分最高的10%的结构中识别出90%的近天然对接诱饵，并且在基于结构的DNA结合序列识别测试中，与优化的物理势函数相比具有优势。这些结果表明，尽管结构数据库规模有限，但特定于蛋白质-DNA相互作用的全原子统计函数仍可实现强大的鉴别能力。它们还表明，统计得分可能很快就能达到与更复杂的物理势函数相当的准确性。
