Coarse-Grained Simulation of Biosurfactants for Risk Assessment

用于风险评估的生物表面活性剂的粗粒度模拟

• 批准号: 2889387
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889387
• 关键词: Coarse; Grained; Simulation; Biosurfactants; Risk

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)

材料与生物膜相互作用的知识可用于环境风险评估，但对于新型生物基表面活性剂，计算机预测方法尚未得到验证。特别是，目前几乎没有公布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，例如它们引起膜弯曲的倾向[1]。据报道，鼠李糖脂对产生它们的细胞具有抗菌作用。与传统的合成表面活性剂相比，生物表面活性剂的主要优点是可以从可再生来源生产并且毒性低。联合利华和杜伦大学之间的持续合作产生了新的模拟方法，可以在 Martini 粗粒度框架 [2,3]（包括新的 Martini 3 版本 [4]）中进行映射、参数化和运行模拟，以预测膜-水分配（log KMW） 对于有机小分子。这种物理特性对于生物蓄积性和基线毒性的风险评估非常重要。对于此类评估，Log KMW 是比更成熟但更粗略的辛醇-水分配系数 (log KOW) 更准确、更相关的物理量，后者不直接解释生物膜的磷脂双层。达勒姆与联合利华的合作目前正在扩大 log KMW 预测的化学范围，以包括某些类别的传统表面活性剂。这个新项目将扩展和验证这种方法来预测生物表面活性剂的物理性质。目标：(a) 开发模拟能力，以更好地了解生物系统中生物表面活性剂的相互作用。 (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 和 R Winter； BBA 生物膜 1862, 183431 (2020)[2] TD Potter、EL Barrett 和 MA Miller； J Chem Theor Comput 17 5777 (2021)[3] TD Potter、N Haywod、A Teixeira、G Hodges、EL Barrett 和 MA Miller； Env Sci Proc Impacts 25 1082 (2023)[4] PCT Souza 等人；自然方法 18 382 (2021)