CISI2.0:更好的抗体交叉或自身相互作用预测工具

CISI2.0:A Better Tool for Predicting Cross-Interaction or Self-Interaction of Antibodies Based on Sequences

抗体广泛用于各类疾病的预防、诊断与治疗。然而,治疗性抗体研发的成功率还不尽人意。不少抗体因为稳定性差,溶解度低,存在交叉或自身相互作用等可开发性缺陷而最终开发失败。候选单克隆抗体能否开发成功,与其理化性质息息相关。虽然已有多种实验方法测定抗体交叉或自身相互作用相关的多种理化特性,但实验测试费力费时费钱。现有的抗体可开发性计算方法,或者依赖于结构,速度慢,通量低;或者未提供可用的软件或在线服务;或者提供的计算服务或软件费用过高;或者预测器的性能与健壮性有待提高。该文仅基于抗体序列,采用二肽期望均值偏差为特征,构建预测抗体交叉或自身相互作用的支持向量机模型。评估结果显示,该集成模型敏感性为100%,准确率为96.18%,可望用于抗体交叉或自身相互作用的高通量评估,加速治疗性抗体研发进程,降低研发成本。

Antibodies are widely used in the prevention,diagnosis and treatment of various diseases.However,the success rate of therapeutic antibody development is far from satisfying.Many antibodies failed due to developability problems such as poor stability,low solubility,and cross-interactions or self-interactions.Whether a candidate monoclonal antibody is developable is closely related to its physicochemical properties.Although a few experimental assays are available to detect several types of physicochemical properties of antibody relevant to cross-interactions or self-interactions,they are laborious,time-consuming and expensive.Some computational methods for antibody developability evaluation have been reported.However,these methods are slow,low throughput,not available,too expensive,or not robust enough.In this paper,a support vector machine(SVM)model for predicting cross-interaction or self-interaction of antibodies is constructed by using dipeptide deviation from expected mean derived from antibody sequences as features.The ensemble model achieves 100%sensitivity and 96.18%accuracy in cross-validation.The model can be used for high-throughput assessment of cross-interaction or self-interaction of antibodies,speeding therapeutic antibodies development,and reducing cost.Based on the model,a free web server called CISI2.0 is built,which is available at http://i.uestc.edu.cn/CISI2.