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，通过使用与目标蛋白具有相似氨基酸序列的模板结构，可以轻松预测相当准确的蛋白质结构。然而，在靶标和模板之间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，必须对模型进行改进。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，以人类SAP Src Homology 2 （SH2）结构域作为建模目标。采用多规范方法解决多极小问题，采用广义Born/表面积模型减少计算量。此外，在可靠区域对原子施加了位置限制，以避免不必要的构象采样。我们发现，从晶体结构出发，模型的综上最密集的构象团簇与参考模拟的综上两个主要团簇中的一个很好地吻合。这表明，目前的改进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，我们还开发了蛋白质结构评估方法。大多数蛋白质结构预测程序生成一组不同质量（候选）的结构。有必要从候选模型中选择一些预计具有本地结构的模型。我们的方法从蛋白质结构的许多方面来评估蛋白质结构的质量。该方法根据氨基酸与结构环境的相关程度对序列中每个位置的氨基酸进行评分。结构环境的定义是基于蛋白质的局部二级结构，蛋白质中被掩埋且无法被溶剂接触的残基面积，以及被极性原子（如水分子）覆盖的侧链面积的比例。

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.