Proteins can exhibit dynamic structural flexibility as they carry out their functions, especially in binding regions that interact with other molecules. For the key SARS-CoV-2 spike protein that facilitates COVID-19 infection, studies have previously identified several such highly flexible regions with therapeutic importance. However, protein structures available from the Protein Data Bank are presented as static snapshots that may not adequately depict this flexibility, and furthermore these cannot keep pace with new mutations and variants. In this paper we present a sequential Monte Carlo method for broadly sampling the 3-D conformational space of protein structure, according to the Boltzmann distribution of a given energy function. Our approach is distinct from previous sampling methods that focus on finding the lowest-energy conformation for predicting a single stable structure. We exemplify our method on the SARS-CoV-2 omicron variant as an application of timely interest. Our results identify sequence positions 495-508 as a key region where omicron mutations have the most impact on the space of possible conformations, which coincides with the findings of other preliminary studies on the binding properties of the omicron variant.
翻译:蛋白质数据库提供的蛋白质结构作为静态快照,可能无法充分反映这种灵活性,而且无法跟上新的变异和变异的步伐。在本文件中,我们根据布尔茨曼对特定能源功能的分配情况,提出一个连续的蒙特卡洛方法,广泛抽样蛋白结构的三维匹配空间。我们的方法不同于以往的抽样方法,即侧重于寻找最低能源符合度以预测单一稳定结构。我们将我们的方法举例为SARS-COV-2 微缩变异,作为及时感兴趣的应用。我们的结果确定,在关键区域,微小变异对可能的异异异空间影响最大,其序列位置为495-508。