A software tool for optimizing the solubility of therapeutic proteins.

描述(由申请人提供)：任何蛋白质的治疗价值都很容易受到低溶解度的影响，这往往会对纯化、产量、活性、保质期和交付产生不利影响。因此，对从蛋白质模型衍生的药物化合物的开发和随后FDA的批准来说，溶解性问题是经常遇到的障碍。这些事实使得开发可量化的蛋白质溶解度描述以及可用于合理和高效地重新设计具有更高溶解度的治疗性蛋白质的设计平台成为当务之急。先前对瘦素突变体数据库的研究表明，当相似突变的信息已经在训练数据集中时，基于序列的瘦素溶解度分析可以为其他突变体提供高预测性(0.96相关性)。在该分析中，氨基酸属性(例如，疏水性、电荷和溶剂化自由能)在蛋白质序列上求和，然后与实验的溶解度测量相关联。然而，对于训练集中没有发现的突变类型，预测失败。相反，当从单个突变体的结构模型中得到的系综参数与实验的溶解度相关时，可预测性很容易扩展到训练集未知的取代，并显示出蛋白质溶解度的明显结构热力学成分。在另一个突变数据集上对该模型进行的盲法测试中，基于集成的方法预测突变是否会增加或减少瘦素的溶解度，准确率为86%，与实际实验值的总体相关性为0.80。初步测试还表明，基于系综的瘦素溶解度的参数化很容易转移到非瘦素结构上。这一阶段SBIR的目标是通过将瘦素上使用的相同参数化例程应用于第二种医学相关化合物，即称为人表皮生长因子(EGF)的小有丝分裂蛋白，来提供基于系综的蛋白质溶解度模型的一般性的原理证明。EGF是抗癌药物的目标，使为最佳溶液特性而设计的类似物可能对制药业有价值。在随后的第二阶段项目应用中，将使用人促红细胞生成素和人粒细胞集落刺激因子作为测试系统，开发一种治疗蛋白质的通用和自动化优化策略。

DESCRIPTION (provided by applicant): The therapeutic value of any protein can be easily compromised by low solubility, which often adversely affects purification, yield, activity, shelf-life, and delivery. Solubility concerns are thus frequent obstacles to the development and subsequent FDA approval of pharmaceutical compounds derived from protein models. These facts make the development of a quantifiable description of protein solubility, as well as a design platform that can be used to rationally and efficiently re-engineer therapeutic proteins with increased solubility, a high priority. Previous studies on a database of leptin mutants have shown that a sequence-based analysis of leptin solubility, in which amino acid properties (e.g., hydrophobicity, charge and solvation free energy) are summed over a protein sequence and then correlated to experimental solubility measurements, can provide high predictability (0.96 correlation) for additional mutants when information for similar mutations is already in the training dataset. However, predictability fails for mutation types not found in the training set. In contrast, when ensemble-based parameters derived from structural models of the individual mutants are correlated to the experimental solubilities, predictability readily extends to substitutions unknown to the training set, and shows an apparent structural-thermodynamic component to the solubility of proteins. In a blind test of this model on an additional mutant dataset, the ensemble- based approach predicted whether or not a mutation will increase or decrease leptin solubility with 86% accuracy, with an overall correlation of 0.80 with the actual experimental values. Initial tests also indicate that the ensemble-based parameterization of leptin solubility is readily transferable to non- leptin structures. The goal of this Phase I SBIR is to provide a proof-of-principal of the generality of the ensemble-based model of protein solubility by applying the same parameterization routine used on leptin to a second medically relevant compound, the small mitogenic protein called human epidermal growth factor (EGF). EGF is a target for cancer inhibitor drugs, making analogs designed for optimal solution properties likely to be valuable to the pharmaceutical industry. In a subsequent Phase II project application, a general and automated optimization strategy for therapeutic proteins will be developed using human erythropoietin and human granulocyte-colony stimulating factor as the test systems.