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，通过使用具有与目标蛋白质相似的氨基酸序列的模板结构，可以容易地预测相当准确的蛋白质结构。然而，在靶和模板之间的氨基酸序列不同的区域中，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，我们探讨了分子动力学为基础的方法的可能性，使用人类SAP Src同源2（SH 2）域作为建模目标。采用多重正则化方法解决多重极小问题，采用广义Born/表面积模型降低计算量。此外，对可靠区域内的原子施加位置限制，以避免不必要的构象采样。我们发现，人口最多的构象集群在合奏的模型一致，以及从晶体结构的参考模拟合奏中的两个主要集群之一。这表明，目前的细化方法可以显着提高比较模型中的不可靠区域的准确性。在本研究中，我们也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质量的结构（候选）。有必要从候选者中选择一些预期具有天然结构的模型。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。该方法基于其与结构环境的相关程度对序列中每个位置处的每个氨基酸进行评分。结构环境的定义是基于蛋白质的局部二级结构，埋在蛋白质中的残基的面积和不可接近的溶剂，和侧链面积的分数，被极性原子，如水分子覆盖。

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.