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Secondary structure prediction and structure-specific sequence analysis of single-stranded DNA.

ABSTRACT: DNA sequence analysis by oligonucleotide binding is often affected by interference with the secondary structure of the target DNA. Here we describe an approach that improves DNA secondary structure prediction by combining enzymatic probing of DNA by structure-specific 5'-nucleases with an energy minimization algorithm that utilizes the 5'-nuclease cleavage sites as constraints. The method can identify structural differences between two DNA molecules caused by minor sequence variations such as a single nucleotide mutation. It also demonstrates the existence of long-range interactions between DNA regions separated by >300 nt and the formation of multiple alternative structures by a 244 nt DNA molecule. The differences in the secondary structure of DNA molecules revealed by 5'-nuclease probing were used to design structure-specific probes for mutation discrimination that target the regions of structural, rather than sequence, differences. We also demonstrate the performance of structure-specific 'bridge' probes complementary to non-contiguous regions of the target molecule. The structure-specific probes do not require the high stringency binding conditions necessary for methods based on mismatch formation and permit mutation detection at temperatures from 4 to 37 degrees C. Structure-specific sequence analysis is applied for mutation detection in the Mycobacterium tuberculosis katG gene and for genotyping of the hepatitis C virus.

SUBMITTER: Dong F

PROVIDER: S-EPMC55824 | biostudies-literature | 2001 Aug

REPOSITORIES: biostudies-literature

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Secondary structure prediction and structure-specific sequence analysis of single-stranded DNA.

Dong F F Allawi H T HT Anderson T T Neri B P BP Lyamichev V I VI

Nucleic acids research 20010801 15

DNA sequence analysis by oligonucleotide binding is often affected by interference with the secondary structure of the target DNA. Here we describe an approach that improves DNA secondary structure prediction by combining enzymatic probing of DNA by structure-specific 5'-nucleases with an energy minimization algorithm that utilizes the 5'-nuclease cleavage sites as constraints. The method can identify structural differences between two DNA molecules caused by minor sequence variations such as a ...[more]

PMID: 11470883

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Project description:BackgroundSingle-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation corresponds to their base pairs pattern, i.e. their secondary structure. Many computational tools have been developed to predict the ssNAs secondary structures, making the choice of the appropriate tool difficult, and an up-to-date guide on the limits and applicability of current secondary structure prediction tools is missing. Therefore, we performed a comparative study of the performances of 9 freely available tools (mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2) on a dataset of 538 ssNAs with known experimental secondary structure.ResultsThe minimum free energy-based tools, namely mfold and RNAfold, and some tools based on artificial intelligence, namely CONTRAfold and MXfold2, provided the best results, with [Formula: see text] of exact predictions, whilst MC-fold seemed to be the worst performing tool, with only [Formula: see text] of exact predictions. In addition, UFold and SPOT-RNA are the only options for pseudoknots prediction. Including in the analysis of mfold and RNAfold results 5-10 suboptimal solutions further improved the performances of these tools. Nevertheless, we could observe issues in predicting particular motifs, such as multiple-ways junctions and mini-dumbbells, or the ssNAs whose structure has been determined in complex with a protein. In addition, our benchmark shows that some effort has to be paid for ssDNA secondary structure predictions.ConclusionsIn general, Mfold, RNAfold, and MXfold2 seem to currently be the best choice for the ssNAs secondary structure prediction, although they still show some limits linked to specific structural motifs. Nevertheless, actual trends suggest that artificial intelligence has a high potential to overcome these remaining issues, for example the recently developed UFold and SPOT-RNA have a high success rate in predicting pseudoknots.

Project description:BackgroundThe accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present.ResultsIn this paper, we further refine and extend the hidden semi-Markov model (HSMM) initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition.ConclusionsWe have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable to current similarity search methods.

Dataset Information

Secondary structure prediction and structure-specific sequence analysis of single-stranded DNA.

Publications

Secondary structure prediction and structure-specific sequence analysis of single-stranded DNA.

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