Quality assessment of V_HH models

Heavy Chain Only Antibodies are specific to Camelid species. Despite the lack of the light chain variable domain, their heavy chain variable domain (VH) domain, named V_HH or nanobody, has promising potential applications in research and therapeutic fields. The structural study of V_HH is therefore of great interest. Unfortunately, considering the huge amount of sequences that might be produced, only about one thousand of V_HH experimental structures are publicly available in the Protein Data Bank, implying that structural model <i>prediction</i> of V_HH is a necessary alternative to obtaining 3D information besides its sequence. The present study aims to assess and compare the quality of predictions from different modelling methodologies. Established comparative &amp; homology modelling approaches to recent Deep Learning-based modelling strategies were applied, i.e. Modeller using single or multiple structural templates, ModWeb, SwissModel (with two evaluation schema), RoseTTAfold, AlphaFold 2 and NanoNet. The prediction accuracy was evaluated using RMSD, TM-score, GDT-TS, GDT-HA and Protein Blocks distance metrics. Besides the global structure assessment, we performed specific analyses of Frameworks and CDRs structures. We observed that AlphaFold 2 and especially NanoNet performed better than the other evaluated softwares. Importantly, we performed molecular dynamics simulations of an experimental structure and a NanoNet predicted model of a V_HH in order to compare the global structural flexibility and local conformations using Protein Blocks. Despite rather similar structures, substantial differences in dynamical properties were observed, which underlies the complexity of the task of model evaluation. Communicated by Ramaswamy H. Sarma

仅重链抗体（Heavy Chain Only Antibodies）为骆驼科物种所特有。尽管缺失轻链可变结构域，但其重链可变结构域（VH），即V_HH或纳米抗体（nanobody），在研究与治疗领域拥有极具前景的应用潜力。因此，对V_HH的结构研究具有重要意义。
遗憾的是，考虑到可能产生的海量序列数据，目前蛋白质数据库（Protein Data Bank）中公开可用的V_HH实验结构仅约一千个，这意味着除了获取序列信息外，V_HH的结构模型预测是获取其三维结构信息的必要替代方案。
本研究旨在评估并对比不同建模方法所得预测结果的质量。研究采用了经典的比较建模与同源建模方法，以及近年来基于深度学习的建模策略，具体包括：使用单个或多个结构模板的Modeller、ModWeb、SwissModel（含两种评估方案）、RoseTTAfold、AlphaFold 2以及NanoNet。
本研究使用均方根偏差（RMSD）、TM评分（TM-score）、GDT-TS、GDT-HA及蛋白质块距离指标对预测精度进行评估。除了整体结构评估之外，我们还对框架区与互补决定区（Complementarity Determining Regions, CDRs）的结构进行了专项分析。研究结果显示，AlphaFold 2，尤其是NanoNet，的预测表现优于其余参评建模软件。
尤为重要的是，我们对一个V_HH的实验结构以及NanoNet预测所得的模型开展了分子动力学模拟，借助蛋白质块指标对比二者的整体结构柔性与局部构象。尽管二者整体结构较为相似，但研究观察到其动力学特性存在显著差异，这也凸显了模型评估任务的复杂性。
由Ramaswamy H. Sarma转交。