National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA.
Bioinformatics. 2012 Jun 15;28(12):i215-23. doi: 10.1093/bioinformatics/bts210.
Systematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state-of-the-art technology to isolate single-stranded (ribo)nucleic acid fragments, named aptamers, which bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence-dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind its wide application due to the lack of dedicated computational approaches. At the same time, the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process.
To close this gap we developed, Aptamotif, a computational method for the identification of sequence-structure motifs in SELEX-derived aptamers. To increase the chances of identifying functional motifs, Aptamotif uses an ensemble-based approach. We validated the method using two published aptamer datasets containing experimentally determined motifs of increasing complexity. We were able to recreate the author's findings to a high degree, thus proving the capability of our approach to identify binding motifs in SELEX data. Additionally, using our new experimental dataset, we illustrate the application of Aptamotif to elucidate several properties of the selection process.
指数富集配体进化系统(SELEX)代表了一种最先进的技术,可以分离单链(核糖)核酸片段,这些片段被命名为适体,通过其序列依赖性折叠诱导的特定结构区域与感兴趣的分子(或分子)结合。该强大的方法在设计蛋白质抑制剂、分子检测系统、治疗药物和抗体替代等方面具有应用。然而,由于缺乏专用的计算方法,因此对该过程的全面理解以及因此的最佳利用落后于其广泛应用。与此同时,SELEX 与新型测序技术的结合开始提供数据,这些数据将允许检查选择过程的各种性质。
为了弥补这一差距,我们开发了 Aptamotif,这是一种用于识别 SELEX 衍生适体中序列-结构基序的计算方法。为了增加识别功能基序的机会,Aptamotif 使用基于集合的方法。我们使用两个包含实验确定的越来越复杂的基序的已发表适体数据集来验证该方法。我们能够高度重现作者的发现,从而证明了我们的方法在 SELEX 数据中识别结合基序的能力。此外,使用我们新的实验数据集,我们说明了 Aptamotif 在阐明选择过程的几个性质方面的应用。