Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran.
Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Mol Divers. 2021 Aug;25(3):1395-1407. doi: 10.1007/s11030-021-10192-9. Epub 2021 Feb 7.
Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico methods have been developed to improve the enrichment processes rate. However, the majority of these methods did not show any effort in designing novel aptamers. Moreover, some target proteins may have not any binding RNA candidates in nature and a reductive mechanism is needed to generate novel aptamer pools among enormous possible combinations of nucleotide acids to be examined in vitro. We have applied a genetic algorithm (GA) with an embedded binding predictor fitness function to in silico design of RNA aptamers. As a case study of this research, all steps were accomplished to generate an aptamer pool against aminopeptidase N (CD13) biomarker. First, the model was developed based on sequential and structural features of known RNA-protein complexes. Then, utilizing RNA sequences involved in complexes with positive prediction results, as the first-generation, novel aptamers were designed and top-ranked sequences were selected. A 76-mer aptamer was identified with the highest fitness value with a 3 to 6 time higher score than parent oligonucleotides. The reliability of obtained sequences was confirmed utilizing docking and molecular dynamic simulation. The proposed method provides an important simplified contribution to the oligonucleotide-aptamer design process. Also, it can be an underlying ground to design novel aptamers against a wide range of biomarkers.
适体可以被视为在许多诊断和治疗应用中对抗单克隆抗体的有效替代品。由于 SELEX（配体系统进化的指数富集）的繁琐和高成本性质，已经开发了计算机方法来提高富集过程的速度。然而，这些方法中的大多数都没有在设计新的适体方面做出任何努力。此外，一些靶蛋白在自然界中可能没有任何结合的 RNA 候选物，需要一种简化机制来在体外检查的大量可能的核苷酸组合中生成新的适体池。我们已经应用了一种带有嵌入式结合预测适应性函数的遗传算法 (GA) 来进行 RNA 适体的计算机设计。作为这项研究的案例研究，完成了所有步骤来生成针对氨肽酶 N (CD13) 生物标志物的适体库。首先，基于已知 RNA-蛋白复合物的序列和结构特征来开发模型。然后，利用具有阳性预测结果的复合物中涉及的 RNA 序列作为第一代，设计新的适体，并选择排名靠前的序列。鉴定出一个具有最高适应性值的 76 -mer 适体，其得分比亲本寡核苷酸高 3 到 6 倍。利用对接和分子动力学模拟来确认获得的序列的可靠性。该方法为寡核苷酸适体设计过程提供了重要的简化贡献。此外，它可以作为设计针对广泛生物标志物的新型适体的基础。
