解析分子电荷各向异性：抗体溶液的案例
Deciphering Molecular Charge Anisotropy: the Case of Antibody Solutions
AI总结：
本文提出一种多尺度框架，通过胶体启发的粗粒化建模与神经网络优化，解析蛋白质溶液中电荷分布对结构和动力学的影响，揭示电荷斑点的空间排列对溶液结构的调控作用。
摘要：
静电相互作用根本上决定了带电（生物）物质的结构、稳定性和动力学，但异质性和各向异性电荷分布对蛋白质溶液行为的影响仍不清楚。本文引入了一种多功能多尺度框架，通过胶体启发的粗粒化建模结合神经网络辅助优化，直接连接分子级静电学与集体性质。以单克隆抗体为模型系统，我们的逆向设计方法识别出能可靠重现实验结构因子、渗透压缩性和集体扩散系数的电荷模式。对数据的仔细检查进一步揭示了特定物理特征和局部电荷斑点的空间排列如何显著影响溶液结构。这种可转移的策略提供了一条预测路径，以解码和控制复杂生物分子中电荷驱动的相互作用，并更广泛地应用于异质电荷软物质系统，对蛋白质配方和生物材料工程具有即时相关性。
英文摘要：
Electrostatic interactions fundamentally govern the structure, stability, and dynamics of charged (bio)matter, yet the impact of heterogeneous and anisotropic charge distributions on the behavior of protein solutions remains elusive. Here, we introduce a versatile multiscale framework that directly connects molecular-level electrostatics to collective properties via a colloid-inspired coarse-grained modeling combined with neural network-assisted optimization. Using monoclonal antibodies as model system, our inverse design approach identifies charge patterns capable of reliably reproducing experimental structure factors, osmotic compressibility and collective diffusion coefficients in a wide region of protein concentrations. Close inspection of our data further uncovers how specific physical features and spatial arrangements of localized charge patches significantly influence the solution structure. This transferable strategy provides a predictive pathway to decode and control charge-driven interactions in complex biomolecules and, more generally, in heterogeneously-charged soft matter systems, with immediate relevance to protein formulation and biomaterials engineering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
