The complex cellular milieu can spontaneously de-mix in a process controlled in part by proteins that are intrinsically disordered (ID). A protein’s propensity to de-mix is thought to be driven by the preference for protein-protein rather than protein-solvent interactions. The hydrodynamic size of monomeric proteins, as quantified by the polymer scaling exponent (v), is driven by a similar balance. We hypothesize that mean v, as predicted by the protein sequence, will be smaller for proteins with a strong propensity to de-mix. To test this hypothesis, we analyzed protein databases containing subsets that are either folded, disordered, or disordered and known to spontaneously phase separate. We find that the phase separating disordered proteins, on average, have lower calculated values of v compared to their non-phase separating counterparts. Moreover, these proteins have a higher sequence-predicted propensity for β-turns. Using a simple, surface areabased model, we propose a physical mechanism for this difference: transient β-turn structures reduce the desolvation penalty of forming a protein-rich phase and increase exposure of atoms involved in π/sp² electronic interactions. By this mechanism, β-turns act as energetically favored nucleation points, which may explain the increased propensity for turns in ID regions (IDRs) that are utilized biologically for phase separation. Phase separating IDRs, non-phase separating IDRs, and folded regions could be distinguished by combining v and β-turn propensity, and we propose a new algorithm, ParSe (partition sequence), for predicting phase separating protein regions. ParSe is able to accurately identify folded, disordered, and phase-separating protein regions from the primary sequence.

中文翻译：

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Beta 转向倾向和模型聚合物缩放指数识别相分离的无序蛋白质

复杂的细胞环境可以在部分由内在无序 (ID) 蛋白质控制的过程中自发地分解。蛋白质的分离倾向被认为是由对蛋白质-蛋白质而不是蛋白质-溶剂相互作用的偏好驱动的。单体蛋白质的流体动力学大小，由聚合物缩放指数 ( v )量化，由类似的平衡驱动。我们假设这意味着v正如蛋白质序列所预测的那样，对于具有强烈分离倾向的蛋白质来说， 会更小。为了验证这一假设，我们分析了包含折叠、无序或无序且已知自发相分离的子集的蛋白质数据库。我们发现相分离的无序蛋白质与非相分离的蛋白质相比，平均而言具有较低的v计算值。此外，这些蛋白质对β-转角具有更高的序列预测倾向。使用简单的基于表面积的模型，我们提出了这种差异的物理机制：瞬态 β-转角结构减少了形成富含蛋白质的相的去溶剂化惩罚，并增加了参与 π/sp ²的原子的暴露电子交互。通过这种机制，β-转角作为能量上有利的成核点，这可以解释生物学上用于相分离的 ID 区域 (IDR) 中转角的增加倾向。相分离违约评级，非相分离发行人违约评级和折叠的区域可以通过合并来区分v和β-转的倾向，并且提出了一种新的算法，解析（票面天信SE quence），用于预测相分离蛋白区域。ParSe 能够从一级序列中准确识别折叠、无序和相分离的蛋白质区域。