Literature DB >> 26642994

Searching the Sequence Space for Potent Aptamers Using SELEX in Silico.

Qingtong Zhou1, Xiaole Xia1,2, Zhaofeng Luo, Haojun Liang, Eugene Shakhnovich1.   

Abstract

To isolate functional nucleic acids that bind to defined targets with high affinity and specificity, which are known as aptamers, the systematic evolution of ligands by exponential enrichment (SELEX) methodology has emerged as the preferred approach. Here, we propose a computational approach, SELEX in silico, that allows the sequence space to be more thoroughly explored regarding binding of a certain target. Our approach consists of two steps: (i) secondary structure-based sequence screening, which aims to collect the sequences that can form a desired RNA motif as an enhanced initial library, followed by (ii) sequence enrichment regarding target binding by molecular dynamics simulation-based virtual screening. Our SELEX in silico method provided a practical computational solution to three key problems in aptamer sequence searching: design of nucleic acid libraries, knowledge of sequence enrichment, and identification of potent aptamers. Six potent theophylline-binding aptamers, which were isolated by SELEX in silico from a sequence space containing 4(13) sequences, were experimentally verified to bind theophylline with high affinity: Kd ranging from 0.16 to 0.52 μM, compared with the dissociation constant of the original aptamer-theophylline, 0.32 μM. These results demonstrate the significant potential of SELEX in silico as a new method for aptamer discovery and optimization.

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Year:  2015        PMID: 26642994     DOI: 10.1021/acs.jctc.5b00707

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  8 in total

1.  Heuristic algorithms in evolutionary computation and modular organization of biological macromolecules: Applications to in vitro evolution.

Authors:  Alexander V Spirov; Ekaterina M Myasnikova
Journal:  PLoS One       Date:  2022-01-27       Impact factor: 3.240

2.  RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information.

Authors:  Ryoga Ishida; Tatsuo Adachi; Aya Yokota; Hidehito Yoshihara; Kazuteru Aoki; Yoshikazu Nakamura; Michiaki Hamada
Journal:  Nucleic Acids Res       Date:  2020-08-20       Impact factor: 16.971

3.  Mapping a Systematic Ribozyme Fitness Landscape Reveals a Frustrated Evolutionary Network for Self-Aminoacylating RNA.

Authors:  Abe D Pressman; Ziwei Liu; Evan Janzen; Celia Blanco; Ulrich F Müller; Gerald F Joyce; Robert Pascal; Irene A Chen
Journal:  J Am Chem Soc       Date:  2019-04-05       Impact factor: 15.419

Review 4.  Methods and Applications of In Silico Aptamer Design and Modeling.

Authors:  Andrey A Buglak; Alexey V Samokhvalov; Anatoly V Zherdev; Boris B Dzantiev
Journal:  Int J Mol Sci       Date:  2020-11-10       Impact factor: 5.923

5.  Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.

Authors:  Andrea Di Gioacchino; Jonah Procyk; Marco Molari; John S Schreck; Yu Zhou; Yan Liu; Rémi Monasson; Simona Cocco; Petr Šulc
Journal:  PLoS Comput Biol       Date:  2022-09-29       Impact factor: 4.779

6.  A generative model for constructing nucleic acid sequences binding to a protein.

Authors:  Jinho Im; Byungkyu Park; Kyungsook Han
Journal:  BMC Genomics       Date:  2019-12-27       Impact factor: 3.969

Review 7.  A Bottom-Up Approach for Developing Aptasensors for Abused Drugs: Biosensors in Forensics.

Authors:  Eda Celikbas; Simge Balaban; Serap Evran; Hakan Coskunol; Suna Timur
Journal:  Biosensors (Basel)       Date:  2019-10-01

8.  Modelling aptamers with nucleic acid mimics (NAM): From sequence to three-dimensional docking.

Authors:  Ricardo Oliveira; Eva Pinho; Ana Luísa Sousa; Óscar Dias; Nuno Filipe Azevedo; Carina Almeida
Journal:  PLoS One       Date:  2022-03-23       Impact factor: 3.240

  8 in total

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