The computer-aided investigation of protein folding has greatly benefited from coarse-grained models, that is, simplified representations at a resolution level lower than atomistic, providing access to qualitative and quantitative details of the folding process that would be hardly attainable, via all-atom descriptions, for medium to long molecules. Nonetheless, the effectiveness of low-resolution models is itself hampered by the presence, in a small but significant number of proteins, of nontrivial topological self-entanglements. Features such as native state knots or slipknots introduce conformational bottlenecks, affecting the probability to fold into the correct conformation; this limitation is particularly severe in the context of coarse-grained models. In this work, we tackle the relationship between folding probability, protein folding pathway, and protein topology in a set of proteins with a nontrivial degree of topological complexity. To avoid or mitigate the risk of incurring in kinetic traps, we make use of the elastic folder model, a coarse-grained model based on angular potentials optimized toward successful folding via a genetic procedure. This light-weight representation allows us to estimate in silico folding probabilities, which we find to anti-correlate with a measure of topological complexity as well as to correlate remarkably well with experimental measurements of the folding rate. These results strengthen the hypothesis that the topological complexity of the native state decreases the folding probability and that the force-field optimization mimics the evolutionary process these proteins have undergone to avoid kinetic traps.

中文翻译：

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蛋白质自缠结调节成功折叠到天然状态：多尺度建模研究

蛋白质折叠的计算机辅助研究极大地受益于粗粒度模型，即在低于原子的分辨率级别的简化表示，提供了对折叠过程的定性和定量细节的访问，这些细节通过所有 -原子描述，用于中长分子。尽管如此，低分辨率模型的有效性本身受到在少量但大量蛋白质中存在的非平凡拓扑自纠缠的阻碍。自然状态结或活结等特征会引入构象瓶颈，影响折叠成正确构象的概率；这种限制在粗粒度模型的背景下尤为严重。在这项工作中，我们解决了折叠概率之间的关系，蛋白质折叠途径，以及具有非平凡拓扑复杂度的一组蛋白质中的蛋白质拓扑。为了避免或减轻发生动力学陷阱的风险，我们使用了弹性折叠模型，这是一种基于角势的粗粒度模型，通过遗传程序优化成功折叠。这种轻量级表示允许我们估计in silico折叠概率，我们发现它与拓扑复杂性的度量反相关，并且与折叠率的实验测量非常相关。这些结果加强了以下假设：天然状态的拓扑复杂性降低了折叠概率，并且力场优化模拟了这些蛋白质为避免动力学陷阱而经历的进化过程。