Melagraki Georgia, Ntougkos Evangelos, Rinotas Vagelis, Papaneophytou Christos, Leonis Georgios, Mavromoustakos Thomas, Kontopidis George, Douni Eleni, Afantitis Antreas, Kollias George
Division of Immunology, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece.
NovaMechanics Ltd, Nicosia, Cyprus.
PLoS Comput Biol. 2017 Apr 20;13(4):e1005372. doi: 10.1371/journal.pcbi.1005372. eCollection 2017 Apr.
We present an in silico drug discovery pipeline developed and applied for the identification and virtual screening of small-molecule Protein-Protein Interaction (PPI) compounds that act as dual inhibitors of TNF and RANKL through the trimerization interface. The cheminformatics part of the pipeline was developed by combining structure-based with ligand-based modeling using the largest available set of known TNF inhibitors in the literature (2481 small molecules). To facilitate virtual screening, the consensus predictive model was made freely available at: http://enalos.insilicotox.com/TNFPubChem/. We thus generated a priority list of nine small molecules as candidates for direct TNF function inhibition. In vitro evaluation of these compounds led to the selection of two small molecules that act as potent direct inhibitors of TNF function, with IC50 values comparable to those of a previously-described direct inhibitor (SPD304), but with significantly reduced toxicity. These molecules were also identified as RANKL inhibitors and validated in vitro with respect to this second functionality. Direct binding of the two compounds was confirmed both for TNF and RANKL, as well as their ability to inhibit the biologically-active trimer forms. Molecular dynamics calculations were also carried out for the two small molecules in each protein to offer additional insight into the interactions that govern TNF and RANKL complex formation. To our knowledge, these compounds, namely T8 and T23, constitute the second and third published examples of dual small-molecule direct function inhibitors of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.
我们展示了一种通过计算机模拟的药物发现流程,该流程已开发并应用于识别和虚拟筛选小分子蛋白质 - 蛋白质相互作用(PPI)化合物,这些化合物通过三聚化界面作为TNF和RANKL的双重抑制剂。该流程的化学信息学部分是通过结合基于结构的建模和基于配体的建模开发的,使用了文献中最大的已知TNF抑制剂集合(2481个小分子)。为便于虚拟筛选,共识预测模型可在以下网址免费获取:http://enalos.insilicotox.com/TNFPubChem/。因此,我们生成了一份包含九个小分子的优先级列表,作为直接抑制TNF功能的候选物。对这些化合物的体外评估导致选择了两种小分子,它们作为TNF功能的有效直接抑制剂,IC50值与先前描述的直接抑制剂(SPD304)相当,但毒性显著降低。这些分子也被鉴定为RANKL抑制剂,并针对这第二种功能进行了体外验证。证实了这两种化合物与TNF和RANKL的直接结合,以及它们抑制生物活性三聚体形式的能力。还对每种蛋白质中的两种小分子进行了分子动力学计算,以进一步深入了解控制TNF和RANKL复合物形成的相互作用。据我们所知,这些化合物,即T8和T23,是已发表的第二和第三个TNF和RANKL双重小分子直接功能抑制剂的例子,可作为开发炎症和自身免疫性疾病新疗法的先导化合物。