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In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm

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Abstract

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.

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Availability of data and materials

All the features and the dataset are available in the following link: http://rpinbase.com

Abbreviations

GA :

Genetic algorithm

CD13 :

Aminopeptidase N

SELEX :

Systematic evolution of ligands by exponential enrichment

MD :

Molecular dynamics

PseAAC :

Pseudo-amino acid composition

SSE :

Secondary structure elements

PS :

Parallel beta Sheets

APS :

Antiparallel beta sheets

PDB :

The Protein Data Bank

DSSP :

Define secondary structure of proteins

JPred :

The Protein Secondary Structure Prediction Server

AUC :

Area under the curve

AUPRC :

Area under the precision–recall curve

ACC :

Accuracy

F1-score :

Weighted average of precision and recall

SD :

Standard deviation

FORNA :

Force-directed RNA

SN :

Sensitivity

SP :

Specificity

PSA :

Prostate-specific antigen

HIV-1 :

Human immunodeficiency virus type 1

ERα :

Estrogen receptor alpha

HGF :

Hepatocyte growth factor

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Authors and Affiliations

  1. Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran

    Mahsa Torkamanian-Afshar & Ali Masoudi-Nejad

  2. Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

    Mahsa Torkamanian-Afshar & Ali Masoudi-Nejad

  3. Department of Computer Technologies, Beykent University, Istanbul, Turkey

    Mahsa Torkamanian-Afshar & Sajjad Nematzadeh

  4. Protein Technology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Maryam Tabarzad

  5. Molecular Biology Research Center, Systems Biology and Poisonings Institute, Tehran, Iran

    Ali Najafi

  6. Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Hossein Lanjanian

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  1. Mahsa Torkamanian-Afshar
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  2. Sajjad Nematzadeh
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Contributions

MT-A was involved in conceptualization, implementation, formal analysis, investigation, and writing, editing, and revising the manuscript. SN carried out conceptualization, implementation, formal analysis, and investigation. MT, AN, and HL took part in conceptualization and writing, editing, and revising the manuscript. AM-N performed conceptualization, supervision, project administration, and writing, editing, and revising the manuscript. All authors have read and approved the manuscript.

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Correspondence to Ali Masoudi-Nejad.

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Supplementary Information

Below is the link to the electronic supplementary material.

11030_2021_10192_MOESM1_ESM.xlsx

Supplementary File 1: Macromolecules features. Excel file containing features of RNA and protein macromolecules (XLSX 100 KB)

Supplementary File 3: Aptamer sequences. Excel file containing RNA sequences against CD13 (XLSX 298 KB)

Supplementary File 2: ML.net results of five datasets. Word file containing results of each dataset (DOCX 913 KB)

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Torkamanian-Afshar, M., Nematzadeh, S., Tabarzad, M. et al. In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm. Mol Divers 25, 1395–1407 (2021). https://doi.org/10.1007/s11030-021-10192-9

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