Leem Jinwoo, Georges Guy, Shi Jiye, Deane Charlotte M
Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, United Kingdom.
Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, Penzberg, 82377, Germany.
Proteins. 2018 Apr;86(4):383-392. doi: 10.1002/prot.25453. Epub 2018 Jan 25.
Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone-dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody-specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position-dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ and χ1+2 accuracy (78.7% and 64.8%) compared to three leading backbone-dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain-side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears.
侧链预测是计算抗体设计和结构预测的一个重要组成部分。当前的抗体建模工具使用依赖于主链的旋转异构体库，其构象取自普通蛋白质。在此，我们展示了我们的抗体特异性旋转异构体库，其中旋转异构体是根据其免疫遗传学（IMGT）位置进行分类的，而不是根据其局部主链几何结构。我们发现，对于某些位置的一些氨基酸类型，仅观察到数量有限的侧链构象。利用这些信息，我们能够缩小旋转异构体采样空间的范围。基于我们的旋转异构体库，我们构建了一个侧链预测器，即位置依赖的抗体旋转异构体交换器（PEARS）。在一个由95个抗体模型结构组成的盲测集上，与三个领先的依赖于主链的侧链预测器相比，PEARS具有最高的平均χ和χ1 + 2准确率（分别为78.7%和64.8%）。我们使用IMGT位置而非主链的ϕ/ψ，这意味着PEARS对模型结构主链中的错误更具鲁棒性。PEARS还实现了最低数量的侧链 - 侧链冲突。PEARS可作为一个网络应用程序免费获取，网址为http://opig.stats.ox.ac.uk/webapps/pears。
