Department of Biophysics, Howard Hughes Medical Institute, UT Southwestern Medical Center, Dallas, TX, USA.
Department of Chemistry, University of Utah, Salt Lake City, UT, USA.
Nat Chem. 2024 Nov;16(11):1794-1802. doi: 10.1038/s41557-024-01630-w. Epub 2024 Sep 13.
Biomolecular condensates regulate cellular function by compartmentalizing molecules without a surrounding membrane. Condensate function arises from the specific exclusion or enrichment of molecules. Thus, understanding condensate composition is critical to characterizing condensate function. Whereas principles defining macromolecular composition have been described, understanding of small-molecule composition remains limited. Here we quantified the partitioning of ~1,700 biologically relevant small molecules into condensates composed of different macromolecules. Partitioning varied nearly a million-fold across compounds but was correlated among condensates, indicating that disparate condensates are physically similar. For one system, the enriched compounds did not generally bind macromolecules with high affinity under conditions where condensates do not form, suggesting that partitioning is not governed by site-specific interactions. Correspondingly, a machine learning model accurately predicts partitioning using only computed physicochemical features of the compounds, chiefly those related to solubility and hydrophobicity. These results suggest that a hydrophobic environment emerges upon condensate formation, driving the enrichment and exclusion of small molecules.
生物分子凝聚物通过无膜分隔来调节细胞功能，使分子分区化。凝聚物的功能源于分子的特异性排除或富集。因此，了解凝聚物的组成对于表征凝聚物的功能至关重要。虽然已经描述了定义大分子组成的原则，但对小分子组成的理解仍然有限。在这里，我们定量了约 1700 种具有生物学相关性的小分子在由不同大分子组成的凝聚物中的分配。化合物的分配差异近百万倍，但在凝聚物之间存在相关性，这表明不同的凝聚物在物理上是相似的。对于一个系统，在不形成凝聚物的条件下，富集的化合物通常不会与大分子以高亲和力结合，这表明分配不是由特定位置的相互作用决定的。相应地，机器学习模型仅使用化合物的计算物理化学特征（主要与溶解度和疏水性有关）就能准确预测分配。这些结果表明，疏水环境在凝聚物形成时出现，驱动小分子的富集和排除。
