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In silico approaches to RNA aptamer design
1Bioinformatics Laboratory, Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan; Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 63-520, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan; Institute for Medical-oriented Structural Biology, Waseda University, 2-2, Wakamatsu-cho Shinjuku-ku, Tokyo 162-8480, Japan; Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan; Graduate School of Medicine, Nippon Medical School, 1-1-5, Sendagi, Bunkyo-ku, Tokyo 113-8602, Japan.
Developing RNA aptamers as drugs is costly. This review covers computational methods to analyze high-throughput SELEX data, aiming to reduce experimental time and cost for aptamer drug discovery.
Area of Science:
- Biochemistry
- Computational Biology
- Drug Discovery
Background:
- RNA aptamers are nucleic acid molecules that bind specific targets.
- Developing aptamers into pharmaceuticals is time-consuming and expensive.
- High-throughput SELEX (HT-SELEX) efficiently selects candidate aptamers.
Purpose of the Study:
- To review existing in silico approaches for RNA aptamer development.
- To highlight computational methods for analyzing HT-SELEX data.
- To demonstrate how computational tools can reduce drug development costs.
Main Methods:
- Review of in silico methods for RNA aptamer analysis.
- Description of algorithms for ranking aptamer candidates from HT-SELEX data.
- Explanation of sequence clustering and motif discovery techniques.
Main Results:
- In silico approaches can significantly streamline aptamer selection and optimization.
- Computational methods aid in identifying high-affinity and specific aptamer candidates.
- Analysis of large aptamer sequence datasets is feasible with computational tools.
Conclusions:
- In silico methods are crucial for efficient RNA aptamer drug development.
- Computational approaches minimize experimental workload and costs.
- Further advancements in computational methods will accelerate aptamer-based therapeutics.