Department of Biochemical Engineering , University College London , Gordon Street , London WC1E 7JE , U.K.
Mol Pharm. 2018 Aug 6;15(8):3079-3092. doi: 10.1021/acs.molpharmaceut.8b00186. Epub 2018 Jun 22.
Computationally guided semirational design has significant potential for improving the aggregation kinetics of protein biopharmaceuticals. While improvement in the global conformational stability can stabilize proteins to aggregation under some conditions, previous studies suggest that such an approach is limited, because thermal transition temperatures ( T) and the fraction of protein unfolded ( f) tend to only correlate with aggregation kinetics where the protein is incubated at temperatures approaching the T. This is because under these conditions, aggregation from globally unfolded protein becomes dominant. However, under native conditions, the aggregation kinetics are presumed to be dependent on local structural fluctuations or partial unfolding of the native state, which reveal regions of high propensity to form protein-protein interactions that lead to aggregation. In this work, we have targeted the design of stabilizing mutations to regions of the A33 Fab surface structure, which were predicted to be more flexible. This Fab already has high global stability, and global unfolding is not the main cause of aggregation under most conditions. Therefore, the aim was to reduce the conformational flexibility and entropy of the native protein at various locations and thus identify which of those regions has the greatest influence on the aggregation kinetics. Highly dynamic regions of structure were identified through both molecular dynamics simulation and B-factor analysis of related X-ray crystal structures. The most flexible residues were mutated into more stable variants, as predicted by Rosetta, which evaluates the ΔΔ G for each potential point mutation. Additional destabilizing variants were prepared as controls to evaluate the prediction accuracy and also to assess the general influence of conformational stability on aggregation kinetics. The thermal conformational stability, and aggregation rates of 18 variants at 65 °C, were each examined at pH 4, 200 mM ionic strength, under which conditions the initial wild-type protein was <5% unfolded. Variants with decreased T values led to more rapid aggregation due to an increase in the fraction of protein unfolded under the conditions studied. As expected, no significant improvements were observed in the global conformational stability as measured by T. However, 6 of the 12 stable variants led to an increase in the cooperativity of unfolding, consistent with lower conformational flexibility and entropy in the native ensemble. Three of these had 5-11% lower aggregation rates, and their structural clustering indicated that the local dynamics of the C-terminus of the heavy chain had a role in influencing the aggregation rate.
计算指导的半理性设计在改善蛋白生物药物的聚集动力学方面具有重要潜力。虽然全局构象稳定性的提高可以在某些条件下稳定蛋白质的聚集,但先前的研究表明,这种方法是有限的,因为热转变温度(T)和未折叠蛋白的分数(f)往往仅与在接近 T 的温度下孵育的蛋白质的聚集动力学相关。这是因为在这些条件下,来自全局展开的蛋白质的聚集变得占主导地位。然而,在天然条件下,聚集动力学被认为取决于局部结构波动或天然状态的部分展开,这揭示了导致聚集的高倾向形成蛋白质-蛋白质相互作用的区域。在这项工作中,我们的目标是设计稳定突变,以 A33 Fab 表面结构的预测更灵活的区域。该 Fab 已经具有很高的整体稳定性,并且在大多数条件下,全局展开不是聚集的主要原因。因此,目标是降低各种位置天然蛋白质的构象灵活性和熵,从而确定哪些区域对聚集动力学的影响最大。通过分子动力学模拟和相关 X 射线晶体结构的 B 因子分析,确定了结构的高动态区域。最灵活的残基突变为罗塞塔(Rosetta)预测的更稳定的变体,罗塞塔(Rosetta)评估每个潜在点突变的 ΔΔG。作为对照,制备了额外的去稳定变体,以评估预测的准确性,并评估构象稳定性对聚集动力学的一般影响。在 pH 4、200 mM 离子强度下,在 65°C 下检查了 18 种变体的热构象稳定性和聚集率,在这些条件下,初始野生型蛋白质的未折叠部分<5%。由于在研究条件下未折叠蛋白分数的增加,T 值降低的变体导致聚集更快。不出所料,T 值测量的全局构象稳定性没有显著提高。然而,在 12 个稳定变体中有 6 个导致解折叠的协同性增加,这与天然集合中较低的构象灵活性和熵一致。其中有 3 个的聚集率降低了 5-11%,它们的结构聚类表明重链 C 末端的局部动力学在影响聚集率方面起作用。