Drug Discovery Science and Technology, AbbVie Bioresearch Center, Worcester, MA, US.
Drug Discovery Science and Technology, AbbVie Inc, North Chicago, IL, US.
MAbs. 2022 Jan-Dec;14(1):2044977. doi: 10.1080/19420862.2022.2044977.
N-terminal heterogeneity resulting from non-uniform signal peptide (SP) cleavage can potentially affect biologics property attributes and result in extended product development timelines. Few studies are available on engineering SPs systematically to address miscleavage issues. Herein, we developed a novel high throughput computational pipeline capable of generating millions of SP mutant sequences that uses the SignalP 5.0 deep learning model to predict which of these mutants are likely to alleviate the N-terminal miscleavage in antibodies. We optimized the parameters to target mutating one or two amino acids at the C-terminus of 84 unique SPs, exhausting all theoretically possible combinations and resulting in a library of 296,077 unique wildtype and mutant signal peptides for in silico screening of each antibody. We applied this method to five antibodies against different targets, with various extent of miscleavage (2.3% to 100%) on their Lambda light chains. In each case, multiple SP mutants were generated, with miscleavage reduced to a non-detectable level and titers comparable with or better than that of the original SPs. Pairwise mutational analysis using an in silico library enriched with high-scoring mutants revealed patterns of amino acids at the C-terminus of SPs, providing insights beyond the "Heijne rule". To our knowledge, no similar approach that combines high throughput in silico mutagenesis and screening with SP cleavage prediction has been reported in the literature. This method can be applied to both the light chain and heavy chain of antibodies, regardless of their initial extent of miscleavage, provides optimized solutions for individual cases, and facilitates the development of antibody therapeutics. Aa, amino acids; CHO, Chinese hamster ovary; CNN, convolutional neural network; CSscore, cleavage site score; CSV, comma-separated values; HC, heavy chain; HEK, human embryonic kidney; HPLC, high-performance liquid chromatography; IgG, immunoglobulin G; IGLV, immunoglobulin G Lambda variable; LC, light chain; LCMS, liquid chromatography-mass spectrometry; MS, mass spectrometry; PCR, polymerase chain reaction; PBS, phosphate-buffered saline; PEI, polyethylenimine; SP, signal peptide; SPase, signal peptidase; TCEP, tris(2-carboxyethyl) phosphine; TOF, time-of-flight.
N 端的不均一性来源于非均匀的信号肽（SP）切割，可能会影响生物制剂的属性，并导致产品开发时间延长。目前，关于系统地设计 SP 以解决错误切割问题的研究较少。在此，我们开发了一种新的高通量计算管道，能够生成数百万个 SP 突变序列，该方法使用 SignalP 5.0 深度学习模型来预测这些突变体中有哪些可能会减轻抗体的 N 端错误切割。我们优化了参数，靶向在 84 个独特 SP 的 C 端突变一个或两个氨基酸，穷尽所有理论上可能的组合，从而产生了 296077 个独特的野生型和突变信号肽库，用于每个抗体的计算机筛选。我们将这种方法应用于五种针对不同靶点的抗体，它们的 Lambda 轻链上的错误切割程度（2.3%到 100%）各不相同。在每种情况下，都生成了多个 SP 突变体，错误切割减少到不可检测水平，滴度与原始 SP 相当或更好。使用富含高分突变体的计算机文库进行的成对突变分析揭示了 SP 末端的氨基酸模式，提供了超越“Heijne 规则”的见解。据我们所知，文献中没有报道过将高通量计算机诱变和筛选与 SP 切割预测相结合的类似方法。该方法可应用于抗体的轻链和重链，无论其初始错误切割程度如何，为每个案例提供优化的解决方案，并促进抗体治疗药物的开发。AA，氨基酸；CHO，中国仓鼠卵巢；CNN，卷积神经网络；CSscore，切割位点评分；CSV，逗号分隔值；HC，重链；HEK，人胚肾；HPLC，高效液相色谱法；IgG，免疫球蛋白 G；IGLV，免疫球蛋白 G Lambda 可变区；LC，轻链；LCMS，液相色谱-质谱法；MS，质谱法；PCR，聚合酶链式反应；PBS，磷酸盐缓冲盐水；PEI，聚乙烯亚胺；SP，信号肽；SPase，信号肽酶；TCEP，三(2-羧乙基)膦；TOF，飞行时间。
