Department of Colloid and Lipid Science, Faculty of Chemistry, Gdańsk University of Technology, 11/12 G. Narutowicza St., 80-233, Gdańsk, Poland.
Department of Chemistry, Technology and Biotechnology of Food, Faculty of Chemistry, Gdańsk University of Technology, 11/12 G. Narutowicza St., 80-233, Gdańsk, Poland.
Sci Rep. 2024 Apr 10;14(1):8362. doi: 10.1038/s41598-024-59021-7.
In the growing landscape of interest in natural surfactants, selecting the appropriate one for specific applications remains challenging. The extensive, yet often unsystematized, knowledge of microbial surfactants, predominantly represented by rhamnolipids (RLs), typically does not translate beyond the conditions presented in scientific publications. This limitation stems from the numerous variables and their interdependencies that characterize microbial surfactant production. We hypothesized that a computational recipe for biosynthesizing RLs with targeted applicational properties could be developed from existing literature and experimental data. We amassed literature data on RL biosynthesis and micellar solubilization and augmented it with our experimental results on the solubilization of triglycerides (TGs), a topic underrepresented in current literature. Utilizing this data, we constructed mathematical models that can predict RL characteristics and solubilization efficiency, represented as logP = f(carbon and nitrogen source, parameters of biosynthesis) and logMSR = f(solubilizate, rhamnolipid (e.g. logP), parameters of solubilization), respectively. The models, characterized by robust R values of respectively 0.581-0.997 and 0.804, enabled the ranking of descriptors based on their significance and impact-positive or negative-on the predicted values. These models have been translated into ready-to-use calculators, tools designed to streamline the selection process for identifying a biosurfactant optimally suited for intended applications.
在对天然表面活性剂日益增长的兴趣中，为特定应用选择合适的表面活性剂仍然具有挑战性。微生物表面活性剂（主要以鼠李糖脂 (RL) 为代表）的广泛但往往没有系统的知识通常无法超出科学出版物中呈现的条件。这种局限性源于微生物表面活性剂生产中众多变量及其相互依赖性。我们假设可以从现有文献和实验数据中为具有靶向应用特性的 RL 生物合成开发计算配方。我们收集了关于 RL 生物合成和胶束增溶的文献数据，并将我们关于甘油三酯 (TG) 增溶的实验结果（当前文献中代表性不足的主题）扩充到其中。利用这些数据，我们构建了可以预测 RL 特性和增溶效率的数学模型，分别表示为 logP=f(碳氮源、生物合成参数)和 logMSR=f(增溶剂、鼠李糖脂 (例如 logP)、增溶参数)。这些模型的 R 值分别为 0.581-0.997 和 0.804，具有很强的稳健性，能够根据描述符的重要性和对预测值的积极或消极影响对其进行排名。这些模型已经被转化为可直接使用的计算器，旨在简化选择最佳生物表面活性剂的过程，以适应预期的应用。
