Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains

Antibodies are a popular and powerful class of therapeutic due to their ability to exhibit high affinity and specificity to target proteins. However, the majority of antibody therapeutics are not genetically human, with initial therapeutic designs typically obtained from animal models. Humanisation of these precursors is essential to reduce immunogenic risks when administered to humans. To aid humanisation we developed Humatch, a computational tool designed to offer experimental-like joint humanisation of heavy and light chains in seconds. Humatch consists of three lightweight Convolutional Neural Networks (CNNs) trained to identify human heavy V-genes, light V-genes, and well-paired antibody sequences with near-perfect accuracy. We show that these CNNs, alongside germline similarity, can be used for fast humanisation that aligns well with known experimental data. Throughout the humanisation process, a sequence is guided towards a specific target gene and away from others via multiclass CNN outputs and gene-specific germline data. This guidance ensures final humanised designs do not sit `between' genes, a trait that is not naturally observed. Humatch's optimisation towards specific genes and good VH/VL pairing increases the chances that final designs will be stable and express well and reduces the chances of immunogenic epitopes forming between the two chains. Here we share the data used to train and evaluate Humatch and the CNN weights and germline likeness lookup arrays that guide its humanisation.抗体是一类应用广泛且效力卓越的治疗性制剂，因其可与靶蛋白展现出极高的亲和力与特异性。然而，绝大多数抗体类治疗药物并非基因层面的人源化序列，其初始治疗性设计通常来自动物模型。将这类前体序列进行人源化改造，对于降低其应用于人体时的免疫原性风险至关重要。为助力抗体人源化工作，我们开发了Humatch——一款可在数秒内实现类实验级别的重链与轻链联合人源化的计算工具。Humatch由三个轻量化卷积神经网络（Convolutional Neural Networks, CNNs）构成，这些模型经训练后可近乎完美地识别人源重链V基因、轻链V基因以及配对良好的抗体序列。本研究证实，结合种系相似性，这些卷积神经网络可用于快速实现抗体人源化，且所得结果与已知实验数据高度契合。在整个人源化改造流程中，模型通过多分类卷积神经网络输出结果与基因特异性种系数据，引导目标序列向特定靶基因靠拢，并远离其他非靶基因。该引导机制可确保最终的人源化设计不会处于“基因间”区域——这一特征在自然状态下并不存在。Humatch针对特定基因的优化以及良好的VH/VL配对策略，可提升最终设计序列的稳定性与表达效率，同时降低两条链之间形成免疫原性表位的概率。本次公开的数据集包含用于训练与评估Humatch的相关数据，以及指导其实现人源化的卷积神经网络权重文件与种系相似性查询数组。