Gani Osman A, Thakkar Balmukund, Narayanan Dilip, Alam Kazi A, Kyomuhendo Peter, Rothweiler Ulli, Tello-Franco Veronica, Engh Richard A
The Norwegian Structural Biology Centre, Department of Chemistry, University of Tromsø, Tromsø, Norway.
The Norwegian Structural Biology Centre, Department of Chemistry, University of Tromsø, Tromsø, Norway.
Biochim Biophys Acta. 2015 Oct;1854(10 Pt B):1605-16. doi: 10.1016/j.bbapap.2015.05.004. Epub 2015 May 19.
In just over two decades, structure based protein kinase inhibitor discovery has grown from trial and error approaches, using individual target structures, to structure and data driven approaches that may aim to optimize inhibition properties across several targets. This is increasingly enabled by the growing availability of potent compounds and kinome-wide binding data. Assessing the prospects for adapting known compounds to new therapeutic uses is thus a key priority for current drug discovery efforts. Tools that can successfully link the diverse information regarding target sequence, structure, and ligand binding properties now accompany a transformation of protein kinase inhibitor research, away from single, block-buster drug models, and toward "personalized medicine" with niche applications and highly specialized research groups. Major hurdles for the transformation to data driven drug discovery include mismatches in data types, and disparities of methods and molecules used; at the core remains the problem that ligand binding energies cannot be predicted precisely from individual structures. However, there is a growing body of experimental data for increasingly successful focussing of efforts: focussed chemical libraries, drug repurposing, polypharmacological design, to name a few. Protein kinase target similarity is easily quantified by sequence, and its relevance to ligand design includes broad classification by key binding sites, evaluation of resistance mutations, and the use of surrogate proteins. Although structural evaluation offers more information, the flexibility of protein kinases, and differences between the crystal and physiological environments may make the use of crystal structures misleading when structures are considered individually. Cheminformatics may enable the "calibration" of sequence and crystal structure information, with statistical methods able to identify key correlates to activity but also here, "the devil is in the details." Examples from specific repurposing and polypharmacology applications illustrate these points. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.
在短短二十多年间,基于结构的蛋白激酶抑制剂研发已从利用单个靶点结构的试错方法,发展到以结构和数据驱动的方法,这些方法旨在优化对多个靶点的抑制特性。强效化合物和激酶组范围的结合数据越来越容易获得,这使得上述发展成为可能。因此,评估将已知化合物用于新治疗用途的前景,是当前药物研发工作的关键优先事项。能够成功关联有关靶点序列、结构和配体结合特性的各种信息的工具,如今伴随着蛋白激酶抑制剂研究的转变而来,这种转变正从单一的重磅药物模式,转向具有细分应用和高度专业化研究团队的“个性化医疗”。向数据驱动的药物研发转型的主要障碍包括数据类型不匹配,以及所使用方法和分子的差异;核心问题仍然是无法从单个结构精确预测配体结合能。然而,越来越多的实验数据使得研究工作日益成功地聚焦:聚焦化学文库、药物再利用、多药理学设计等等。蛋白激酶靶点的相似性可通过序列轻松量化,其与配体设计的相关性包括通过关键结合位点进行广泛分类、评估耐药性突变以及使用替代蛋白。尽管结构评估能提供更多信息,但蛋白激酶的灵活性以及晶体环境与生理环境之间的差异,可能导致单独考虑结构时,晶体结构的使用具有误导性。化学信息学或许能够对序列和晶体结构信息进行“校准”,统计学方法能够识别与活性相关联的关键因素,但同样在这里,“细节决定成败”。来自特定再利用和多药理学应用的实例说明了这些要点。本文是名为《蛋白激酶抑制剂》的特刊的一部分。