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

Basit Öğe Kaydı
dc.contributor.authorTorkamanian-Afshar, Mahsa
dc.contributor.authorNematzadeh, Sajjad
dc.contributor.authorTabarzad, Maryam
dc.contributor.authorNajafi, Ali
dc.contributor.authorLanjanian, Hossein
dc.contributor.authorMasoudi-Nejad, Ali
dc.date.accessioned2024-03-13T10:30:47Z
dc.date.available2024-03-13T10:30:47Z
dc.date.issued2021
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractAptamers 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.en_US
dc.identifier.doi10.1007/s11030-021-10192-9
dc.identifier.endpage1407en_US
dc.identifier.issn1381-1991
dc.identifier.issn1573-501X
dc.identifier.issue3en_US
dc.identifier.pmid33554306en_US
dc.identifier.scopus2-s2.0-85100596785en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1395en_US
dc.identifier.urihttps://doi.org/10.1007/s11030-021-10192-9
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3539
dc.identifier.volume25en_US
dc.identifier.wosWOS:000615762500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofMolecular Diversityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAptameren_US
dc.subjectAminopeptidase N (CD13)en_US
dc.subjectGenetic algorithm (GA)en_US
dc.subjectDockingen_US
dc.subjectMolecular dynamic simulationen_US
dc.titleIn silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithmen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu