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Stability of the I-motif structure is related to the interactions between phosphodiester backbones.

ABSTRACT: The i-motif DNA tetrameric structure is formed of two parallel duplexes intercalated in a head-to-tail orientation, and held together by hemiprotonated cytosine pairs. The four phosphodiester backbones forming the structure define two narrow and wide grooves. The short interphosphate distances across the narrow groove induce a strong repulsion which should destabilize the tetramer. To investigate this point, molecular dynamics simulations were run on the [d(C2)]4 and [d(C4)]4 tetramers in 3'E and 5'E topologies, for which the interaction of the phosphodiester backbones through the narrow groove is different. The analysis of the simulations, using the Molecular Mechanics Generalized Born Solvation Area and Molecular Mechanics Poisson-Boltzmann Solvation Area approaches, shows that it is the van der Waals energy contribution which displays the largest relative difference between the two topologies. The comparison of the solvent-accessible area of each topology reveals that the sugar-sugar interactions account for the greater stability of the 3'E topology. This stresses the importance of the sugar-sugar contacts across the narrow groove which, enforcing the optimal backbone twisting, are essential to the base stacking and the i-motif stability. Tighter interactions between the sugars are observed in the case of N-type sugar puckers.

SUBMITTER: Malliavin TE

PROVIDER: S-EPMC1302965 | biostudies-literature | 2003 Jun

REPOSITORIES: biostudies-literature

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Stability of the I-motif structure is related to the interactions between phosphodiester backbones.

Malliavin Thérèse E TE Gau Jocelyne J Snoussi Karim K Leroy Jean-Louis JL

Biophysical journal 20030601 6

The i-motif DNA tetrameric structure is formed of two parallel duplexes intercalated in a head-to-tail orientation, and held together by hemiprotonated cytosine pairs. The four phosphodiester backbones forming the structure define two narrow and wide grooves. The short interphosphate distances across the narrow groove induce a strong repulsion which should destabilize the tetramer. To investigate this point, molecular dynamics simulations were run on the [d(C2)]4 and [d(C4)]4 tetramers in 3'E an ...[more]

PMID: 12770889

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Project description:BACKGROUND: Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data. RESULTS: We developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at http://virus.hpid.org/interactions.aspx. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at http://virus.hpid.org/PDB_IDs.html. CONCLUSION: When the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.

Dataset Information

Stability of the I-motif structure is related to the interactions between phosphodiester backbones.

Publications

Stability of the I-motif structure is related to the interactions between phosphodiester backbones.

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