Suppr超能文献

探索 PAINS 的活性特征及其在靶标-配体复合物中的结构背景。

Exploring Activity Profiles of PAINS and Their Structural Context in Target-Ligand Complexes.

机构信息

Structural Bioinformatics Group , Charité-Universitätsmedizin Berlin , 10115 Berlin , Germany.

BB3R - Berlin Brandenburg 3R Graduate School , Freie Universität Berlin , 14195 Berlin , Germany.

出版信息

J Chem Inf Model. 2018 Sep 24;58(9):1847-1857. doi: 10.1021/acs.jcim.8b00385. Epub 2018 Aug 27.

Abstract

Assay interference is an acknowledged problem in high-throughput screening, and pan-assay interference compounds (PAINS) filters are one of a number of approaches that have been suggested for identification of potential screening artifacts or frequent hitters. Many studies have highlighted that the unwary usage of these structural alerts should be reconsidered and criticized their extrapolation beyond the applicability domain. A large-scale investigation of the activity profiles and the structural context of PAINS might provide a better assessment of whether this extrapolation is valid. To this end, multiple publicly accessible compound collections were screened, and the PAINS statistics are comprehensively presented and discussed. Next, the promiscuity trends and activity profiles of PAINS were compared with those compounds not matching any PAINS substructures. Overall, PAINS demonstrated higher promiscuity and relatively higher assay hit rates compared with the other compounds. Furthermore, nearly 2000 distinct target-ligand complexes containing PAINS were analyzed, and the interactions were quantified and compared. In more than 50% of the instances, the PAINS atoms participated in interactions more frequently compared with the remaining atoms of the ligand structure. Many PAINS participated in crucial interactions that were often responsible for binding of the ligand, which reaffirms their distinction from those responsible for assay interference. In conclusion, we reinforce that while it is important to employ compound filters to eliminate nonspecific hits, establishing a set of statistically significant and validated PAINS filters is essential to restrain the black-box practice of triaging screening hits matching any of the proposed 480 alerts.

摘要

高通量筛选中存在分析物干扰是公认的问题,而泛分析物干扰化合物 (PAINS) 过滤器是已被提出的用于鉴定潜在筛选假象或高频化合物的方法之一。许多研究强调,应重新考虑这些结构警示的轻率使用,并批评其超出适用范围的推断。对 PAINS 的活性谱和结构背景进行大规模调查,可能会更好地评估这种推断是否有效。为此,对多个公开可得的化合物库进行了筛选,并全面呈现和讨论了 PAINS 的统计数据。接下来,将 PAINS 的混杂趋势和活性谱与不匹配任何 PAINS 亚结构的化合物进行了比较。总体而言,与其他化合物相比,PAINS 表现出更高的混杂性和相对更高的检测命中率。此外,还分析了近 2000 个包含 PAINS 的独特靶标-配体复合物,并对相互作用进行了量化和比较。在超过 50%的情况下,PAINS 原子比配体结构的其余原子更频繁地参与相互作用。许多 PAINS 参与了关键相互作用,这些相互作用通常负责配体的结合,这再次证实了它们与负责分析物干扰的相互作用不同。总之,我们强调,虽然使用化合物过滤器来消除非特异性命中很重要,但建立一套具有统计学意义和经过验证的 PAINS 过滤器对于限制基于任何已提出的 480 个警示进行筛选命中分类的黑盒实践至关重要。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验