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Prediction of the secondary structure of short DNA aptamers.

ABSTRACT: Aptamers have a spectrum of applications in biotechnology and drug design, because of the relative simplicity of experimental protocols and advantages of stability and specificity associated with their structural properties. However, to understand the structure-function relationships of aptamers, robust structure modeling tools are necessary. Several such tools have been developed and extensively tested, although most of them target various forms of biological RNA. In this study, we tested the performance of three tools in application to DNA aptamers, since DNA aptamers are the focus of many studies, particularly in drug discovery. We demonstrated that in most cases, the secondary structure of DNA can be reconstructed with acceptable accuracy by at least one of the three tools tested (Mfold, RNAfold, and CentroidFold), although the G-quadruplex motif found in many of the DNA aptamer structures complicates the prediction, as well as the pseudoknot interaction. This problem should be addressed more carefully to improve prediction accuracy.

SUBMITTER: Afanasyeva A

PROVIDER: S-EPMC6975895 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Prediction of the secondary structure of short DNA aptamers.

Afanasyeva Arina A Nagao Chioko C Mizuguchi Kenji K

Biophysics and physicobiology 20191129

Aptamers have a spectrum of applications in biotechnology and drug design, because of the relative simplicity of experimental protocols and advantages of stability and specificity associated with their structural properties. However, to understand the structure-function relationships of aptamers, robust structure modeling tools are necessary. Several such tools have been developed and extensively tested, although most of them target various forms of biological RNA. In this study, we tested the p ...[more]

PMID: 31984183

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Project description:BackgroundWhen characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult.ResultsIn this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an n-gram pattern of amino acids that reflects the sequence variation in a protein's evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.On a large non-redundant dataset of 8,297 protein chains (DsspNr-25), the average Q3 of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (EVA Set_1 and EVA_Set2), the average Q3 of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases.ConclusionsOur experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at http://bio-cluster.iis.sinica.edu.tw/SymPred/.

Dataset Information

Prediction of the secondary structure of short DNA aptamers.

Publications

Prediction of the secondary structure of short DNA aptamers.

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