Computational predictive approaches for interaction and structure of aptamers

被引:23
作者
Emami, Neda [1 ]
Pakchin, Parvin Samadi [2 ]
Ferdousi, Reza [1 ,2 ]
机构
[1] Tabriz Univ Med Sci, Sch Management & Med Informat, Dept Hlth Informat Technol, Tabriz, Iran
[2] Tabriz Univ Med Sci, Res Ctr Pharmaceut Nanotechnol, Biomed Inst, Tabriz, Iran
关键词
Aptamer; Interaction prediction; Structure prediction; Affinity; Machine learning; RNA SECONDARY STRUCTURE; MACHINE LEARNING APPLICATIONS; DE-NOVO PREDICTION; PROTEIN-STRUCTURE PREDICTION; IN-VITRO; ATOMIC-ACCURACY; WEB SERVER; SEQUENCE; CELL; EVOLUTION;
D O I
10.1016/j.jtbi.2020.110268
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Aptamers are short single-strand sequences that can bind to their specific targets with high affinity and specificity. Usually, aptamers are selected experimentally via systematic evolution of ligands by exponential enrichment (SELEX), an evolutionary process that consists of multiple cycles of selection and amplification. The SELEX process is expensive, time-consuming, and its success rates are relatively low. To overcome these difficulties, in recent years, several computational techniques have been developed in aptamer sciences that bring together different disciplines and branches of technologies. In this paper, a complementary review on computational predictive approaches of the aptamer has been organized. Generally, the computational prediction approaches of aptamer have been proposed to carry out in two main categories: interaction-based prediction and structure-based predictions. Furthermore, the available software packages and toolkits in this scope were reviewed. The aim of describing computational methods and tools in aptamer science is that aptamer scientists might take advantage of these computational techniques to develop more accurate and more sensitive aptamers. (C) 2020 Elsevier Ltd. All rights reserved.
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页数:14
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