Hot-spot analysis for drug discovery targeting protein-protein interactions

被引:81
作者
Rosell, Mireia [1 ]
Fernandez-Recio, Juan [1 ,2 ]
机构
[1] BSC, Dept Life Sci, Barcelona, Spain
[2] CSIC, IBMB, Struct Biol Unit, Barcelona, Spain
关键词
Protein-protein interactions; drug discovery; computational docking; interface hot-spot residues; SMALL-MOLECULE INHIBITORS; SINGLE NUCLEOTIDE POLYMORPHISMS; DRUGGABLE BINDING-SITES; TRANSIENT POCKETS; WEB SERVER; SCALE MAP; IPPI-DB; PREDICTION; DOCKING; INTERFACES;
D O I
10.1080/17460441.2018.1430763
中图分类号
R9 [药学];
学科分类号
100702 [药剂学];
摘要
Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.
引用
收藏
页码:327 / 338
页数:12
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