Coarse-Grained Simulation of Biosurfactants for Risk Assessment

材料与生物膜相互作用的知识用于环境风险评估，但对于新型生物基表面活性剂，计算机预测方法未经验证。特别是，目前几乎没有发表的鼠李糖脂的粗粒度模拟。这类脂质含有一个或两个鼠李糖部分（一种脱氧糖），由某些细菌合成。实验研究已经开始探索鼠李糖脂对磷脂膜物理性质的影响，例如它们诱导膜弯曲的趋势[1]。据报道，鼠李糖脂对产生它们的细胞具有抗菌益处。与传统的合成表面活性剂相比，生物表面活性剂具有可再生资源生产和低毒性的主要优势。联合利华和达勒姆大学之间正在进行的合作产生了新的模拟方法，可以在Martini粗粒度框架中映射，参数化和运行模拟[2，3]（包括新的Martini 3版本[4]）来预测小有机分子的膜-水分配（log KMW）。这一物理特性对于与生物累积性和基线毒性有关的风险评估十分重要。对于此类评估，Log KMW是一个比更成熟但更粗糙的辛醇-水分配系数（log KOW）更准确和相关的物理量，后者并不直接说明生物膜的磷脂双层。Durham-Unilever合作目前正在扩大log KMW预测的化学范围，以包括某些类别的常规表面活性剂。这个新项目将扩展和验证这一方法，以预测生物表面活性剂的物理性质。(b)了解并量化生物表面活性剂类别内的变化如何影响其物理化学性质。(c)表征生物表面活性剂结构和膜组成对膜形状效应的影响。[1]M Herzog，T Tiso，LM Blank and R Winter; BBA Biomembranes 1862，183431（2020）[2] TD Potter，EL Barrett and MA米勒; J Chem Theor Comput 17 5777（2021）[3] TD Potter，N Haywod，A特谢拉，G Hodges，EL Barrett and MA米勒; Env Sci Proc Impacts 25 1082（2023）[4] PCT Souza et al.;自然方法18 382（2021）

Knowledge of a material's interactions with biological membranes is used in environmental risk assessment, but in silico predictive methods are unvalidated for novel, bio-based surfactants. In particular, there are currently there are almost no published coarse-grained simulations of rhamnolipids. This class of lipids contain one or two rhamnose moeties (a deoxy sugar) and are synthesised by certain bacteria. Experimental studies have begun to explore the effects of rhamnolipids on the physical properties of phospholipid membranes, such as their tendency to induce membrane curvature [1]. Rhamnolipids have also been reported to have antimicrobial benefits for the cells that produce them. Compared to conventional synthetic surfactants, biosurfactants have the major advantages of being possible to produce from renewable sources and showing low toxicity.An ongoing collaboration between Unilever and Durham University has yielded new simulation methodology to map, parametrise and run simulations in the Martini coarse-grained framework [2,3] (including the new Martini 3 version [4]) to predict membrane-water partitioning (log KMW) for small organic molecules. This physical property is important for risk assessment in connection with bioaccumulation and baseline toxicity. Log KMW is a more accurate and pertinent physical quantity for such assessments than the more established but cruder octanol-water partitioning coefficient (log KOW), which does not directly account for the phospholipid bilayer of biological membranes. The Durham-Unilever collaboration is currently expanding the chemical scope of log KMW predictions to include some classes of conventional surfactants. This new project will extend and validate this methodology to predict physical properties of biosurfactants.Goals: (a) Develop simulation capability for better understanding of the interactions of biosurfactants in biological systems. (b) Understand and quantify how variation within biosurfactant classes impacts their physico-chemical properties. (c) Characterise the influence of biosurfactant structure and membrane composition on membrane shape effects.[1] M Herzog, T Tiso, LM Blank and R Winter; BBA Biomembranes 1862, 183431 (2020)[2] TD Potter, EL Barrett and MA Miller; J Chem Theor Comput 17 5777 (2021)[3] TD Potter, N Haywod, A Teixeira, G Hodges, EL Barrett and MA Miller; Env Sci Proc Impacts 25 1082 (2023)[4] PCT Souza et al.; Nature Methods 18 382 (2021)