Schlessinger Avner, Rost Burkhard
CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, USA.
Proteins. 2005 Oct 1;61(1):115-26. doi: 10.1002/prot.20587.
Structural flexibility has been associated with various biological processes such as molecular recognition and catalytic activity. In silico studies of protein flexibility have attempted to characterize and predict flexible regions based on simple principles. B-values derived from experimental data are widely used to measure residue flexibility. Here, we present the most comprehensive large-scale analysis of B-values. We used this analysis to develop a neural network-based method that predicts flexible-rigid residues from amino acid sequence. The system uses both global and local information (i.e., features from the entire protein such as secondary structure composition, protein length, and fraction of surface residues, and features from a local window of sequence-consecutive residues). The most important local feature was the evolutionary exchange profile reflecting sequence conservation in a family of related proteins. To illustrate its potential, we applied our method to 4 different case studies, each of which related our predictions to aspects of function. The first 2 were the prediction of regions that undergo conformational switches upon environmental changes (switch II region in Ras) and the prediction of surface regions, the rigidity of which is crucial for their function (tunnel in propeller folds). Both were correctly captured by our method. The third study established that residues in active sites of enzymes are predicted by our method to have unexpectedly low B-values. The final study demonstrated how well our predictions correlated with NMR order parameters to reflect motion. Our method had not been set up to address any of the tasks in those 4 case studies. Therefore, we expect that this method will assist in many attempts at inferring aspects of function.
结构灵活性与多种生物过程相关，如分子识别和催化活性。蛋白质灵活性的计算机模拟研究试图基于简单原理对灵活区域进行表征和预测。从实验数据得出的B值被广泛用于测量残基灵活性。在此，我们展示了对B值最全面的大规模分析。我们利用这一分析开发了一种基于神经网络的方法，可从氨基酸序列预测灵活和刚性残基。该系统使用全局和局部信息（即来自整个蛋白质的特征，如二级结构组成、蛋白质长度和表面残基比例，以及来自序列连续残基局部窗口的特征）。最重要的局部特征是反映相关蛋白质家族中序列保守性的进化交换概况。为说明其潜力，我们将方法应用于4个不同的案例研究，每个案例都将我们的预测与功能方面相关联。前两个案例是预测环境变化时发生构象转换的区域（Ras中的开关II区域）以及预测表面区域，其刚性对功能至关重要（螺旋桨折叠中的通道）。我们的方法都正确地捕捉到了这些区域。第三个研究表明，我们的方法预测酶活性位点中的残基具有出乎意料的低B值。最后一个研究展示了我们的预测与NMR序参数反映运动的相关性有多好。我们的方法并非为解决这4个案例研究中的任何任务而设置。因此，我们期望该方法将有助于许多推断功能方面的尝试。
