Rapid structure-based software to enhance antibody affinity and developability for high-throughput screening

治疗性单克隆抗体与称为表位的蛋白质的特定区域结合，从而引发细胞 治疗或治愈疾病的反应。发现治疗性抗体传统上需要费力和 昂贵的筛选实验，因此选择哪些抗体结合表位的计算方法 最好的并具有最理想的药物特性的需求量很大。基于结构的抗体 设计对于现代药物发现和开发过程也很重要。这种方法需要高 分辨率四级 (3D) 蛋白质复杂结构，其实验测定通常是一个缓慢的过程 但这并不总是成功。蛋白质结构和结合界面预测算法即将产生影响 加快构建高置信度的药物靶点结构模型，为人类健康提供保障 生物制药，这将有助于确定新的治疗策略。然而，目前的算法是 它们区分强结合抗体和弱结合抗体的能力有限，这阻碍了 发现广泛的治疗方法。此外，还需要技术来预测候选抗体是否 在开发过程中会尽早失败。随着模拟去除分子的改进 不损害功能的负债，计算机辅助抗体设计可用于降低药物开发 成本并将实验重点放在最有前途的候选药物上。 在这里，我们建议通过开发基于以下内容的高精度软件工具来推进抗体发现： DNASTAR 的 NovaFold Antibody 程序成功用于抗体结构预测，NovaDock 实现灵活 蛋白质-蛋白质对接，以及用于蛋白质工程的 Lasergene Protein Design。项目重点目标1) 开发更准确和有效的免疫复合物（相互作用的抗体和抗原）结构 通过更好地对具有挑战性的互补决定区（CDR）进行建模来进行预测，这些区域发挥着 在抗体亲和力和选择性中发挥关键作用； 2) 预测减少化学物质的抗体序列 以及被证明对抗体的制造过程或治疗效果有害的能量负担 病人。特别是，通过结合计算加速，整体预测能力将得到提高 技术支持数以万计的抗体序列的虚拟筛选。最后，也是第一个 届时，该项目将开发一种“虚拟免疫系统”来接近人类抗体的发现，其中抗体 将从种系序列建模并选择最好地识别感兴趣的抗原。整体 项目目标是提供先进的抗体筛选流程，该流程功能强大、准确且生产速度快 结果，这将通过实现详细而准确的免疫复合物结构来加速抗体的发现 高通量规模的预测和基于结构的责任检测。

Therapeutic monoclonal antibodies bind to specific regions of proteins called epitopes, which elicit cellular responses that treat or cure disease. Discovering therapeutic antibodies traditionally requires laborious and expensive screening experiments, so computational approaches that select which antibodies bind an epitope best and have the most desirable pharmaceutical properties are in high demand. Structure-based antibody design is also important to the modern drug discovery and development process. This approach requires a high- resolution quaternary (3D) protein complex structure, whose experimental determination is often a slow process that is not always successful. Protein structure and binding interface prediction algorithms are poised to impact human health by accelerating the construction of high-confidence structural models of drug targets and biopharmaceuticals, which will help identify new therapeutic strategies. However, the current algorithms are limited in their ability to distinguish stronger-binding antibodies from weaker ones, which is preventing the discovery of broad classes of therapeutics. In addition, technologies are needed to predict if a candidate antibody will fail as early as possible in the development process. With improvements in simulating removal of molecular liabilities without damaging function, computer-aided antibody design can be used to lower drug development costs and focus experiments on the most promising drug candidates. Here we propose to advance antibody discovery by developing highly accurate software tools built on the success of DNASTAR’s NovaFold Antibody program for antibody structure prediction, NovaDock for flexible protein-protein docking, and Lasergene Protein Design for protein engineering. The aims of the project focus 1) on developing more accurate and effective immune complex (an interacting antibody and antigen) structure predictions through better modeling of the challenging complementarity-determining regions (CDR), which play a critical role in antibody affinity and selectivity; and 2) on predicting antibody sequences that reduce chemical and energetic liabilities that prove detrimental to an antibody’s manufacturing process or therapeutic effect in a patient. In particular, overall predictive capability will be improved by incorporating computational acceleration techniques to support the virtual screening of tens of thousands of antibody sequences. Finally, and for the first time, this project will develop a “virtual immune system” to approach human antibody discovery, where antibodies will be modeled from germline sequences and selected for best recognizing an antigen of interest. The overall project goal is to deliver an advanced antibody screening pipeline that is powerful, accurate, and produces fast results, which will accelerate antibody discovery by enabling detailed and accurate immune complex structure predictions and structure-based liability detection at a high-throughput scale.