Development of methods for high-resolution protein structure modeling and for model structure assessment

比较建模是一种强大的方法，它通过使用与目标蛋白质具有相似氨基酸序列的模板结构来容易地预测相当准确的蛋白质结构。然而，在靶标和模板的氨基酸序列不同的区域，预测的结构仍然不可靠。在这种情况下，模型必须进行改进。在这项研究中，我们探索了一种基于分子动力学的方法的可能性，以人SAP Src同源2(SH2)结构域为建模目标。采用多正则方法处理多极小值问题，并采用推广的Born/表面积模型降低计算量。此外，为了避免不必要的构象采样，对可靠区域中的原子施加了位置限制。我们发现，从晶体结构出发，模型系综中布居最多的构象团簇与参考模拟系综中的两个主要团簇之一吻合得很好。这表明，当前的精化方法可以显著提高比较模型中不可靠区域的精度。在这项研究中，我们还发展了蛋白质结构评估的方法。大多数蛋白质结构预测程序生成一组不同性质的结构(候选)。有必要从候选模型中选择一些预期具有固有结构的模型。我们的方法从蛋白质结构的多个方面来评估蛋白质结构的质量。这种方法根据氨基酸与结构环境的相关性程度，对序列中每个位置的每个氨基酸进行评分。结构环境是基于蛋白质的局部二级结构、蛋白质中的残基埋藏在蛋白质中且不能被溶剂接触到的区域以及被水分子等极性原子覆盖的侧链区域的比例来定义的。

Comparative modeling is a powerful method that easily predicts a considerably accurate structure of a protein by using a template structure having a similar amino-acid sequence to the target protein. However, in the region where the amino acid sequence is different between the target and the template, the predicted structure remains unreliable. In such a case, the model has to be refined. In this study, we explored the possibility of a molecular dynamics-based method, using the human SAP Src Homology 2 (SH2) domain as the modeling target. The multicanonical method was used to alleviate the multiple-minima problem and the generalised Born/surface area model was used to reduce the computational cost. In addition, position restraints were imposed on the atoms in the reliable regions to avoid unnecessary conformational sampling. We found that the most populated conformational clusters in the ensemble of the model agreed well with one of the two major clusters in the ensemble of the reference simulation starting from the crystal structure. This demonstrates that the current refinement method can significantly improve the accuracy of an unreliable region in a comparative model. In this study, we also developed the method for protein structure assessment. Most protein structure prediction programs generate a set of structures of various qualities (candidates). It is necessary to select some models that are expected to have native structures from the candidates. Our method evaluates quality of a protein structure from many aspects of protein structures. This method scores each amino acid at each position in the sequences based on its degree of correlation to structural environment. Structural environment is defined based on protein local secondary structure, the area of the residue buried in the protein and inaccessible to solvent, and the fraction of side-chain area that is covered by polar atoms like water molecule.