Haddad Yazan, Heger Zbyněk, Adam Vojtech
Department of Chemistry and Biochemistry, Mendel University in BrnoBrno, Czechia; Central European Institute of Technology, Brno University of TechnologyBrno, Czechia.
Front Mol Neurosci. 2017 Jan 20;10:7. doi: 10.3389/fnmol.2017.00007. eCollection 2017.
Targeted therapy is a promising approach for treatment of neuroblastoma as evident from the large number of targeting agents employed in clinical practice today. In the absence of known crystal structures, researchers rely on homology modeling to construct template-based theoretical structures for drug design and testing. Here, we discuss three candidate cell surface proteins that are suitable for homology modeling: human norepinephrine transporter (hNET), anaplastic lymphoma kinase (ALK), and neurotrophic tyrosine kinase receptor 2 (NTRK2 or TrkB). When choosing templates, both sequence identity and structure quality are important for homology modeling and pose the first of many challenges in the modeling process. Homology modeling of hNET can be improved using template models of dopamine and serotonin transporters instead of the leucine transporter (LeuT). The extracellular domains of ALK and TrkB are yet to be exploited by homology modeling. There are several idiosyncrasies that require direct attention throughout the process of model construction, evaluation and refinement. Shifts/gaps in the alignment between the template and target, backbone outliers and side-chain rotamer outliers are among the main sources of physical errors in the structures. Low-conserved regions can be refined with loop modeling method. Residue hydrophobicity, accessibility to bound metals or glycosylation can aid in model refinement. We recommend resolving these idiosyncrasies as part of "good modeling practice" to obtain highest quality model. Decreasing physical errors in protein structures plays major role in the development of targeting agents and understanding of chemical interactions at the molecular level.
靶向治疗是一种很有前景的神经母细胞瘤治疗方法,从当今临床实践中使用的大量靶向药物就可见一斑。在缺乏已知晶体结构的情况下,研究人员依靠同源建模来构建基于模板的理论结构,用于药物设计和测试。在此,我们讨论三种适合进行同源建模的候选细胞表面蛋白:人类去甲肾上腺素转运体(hNET)、间变性淋巴瘤激酶(ALK)和神经营养性酪氨酸激酶受体2(NTRK2或TrkB)。选择模板时,序列同一性和结构质量对于同源建模都很重要,并且是建模过程中众多挑战中的第一个。使用多巴胺和5-羟色胺转运体的模板模型而非亮氨酸转运体(LeuT)的模板模型,可以改进hNET的同源建模。ALK和TrkB的细胞外结构域尚未通过同源建模进行研究。在模型构建、评估和优化的整个过程中,有几个特殊情况需要直接关注。模板与靶标之间比对中的移位/缺口、主链异常值和侧链旋转异构体异常值是结构中物理误差的主要来源。低保守区域可以用环建模方法进行优化。残基疏水性、与结合金属的可及性或糖基化有助于模型优化。我们建议将解决这些特殊情况作为“良好建模实践”的一部分,以获得最高质量的模型。减少蛋白质结构中的物理误差在靶向药物的开发以及在分子水平上理解化学相互作用方面起着重要作用。