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Using interpolation for fast and accurate calculation of ion-ion interactions.

ABSTRACT: We perform extensive molecular dynamics (MD) simulations between pairs of ions of various diameters (2-5.5 Å in increments of 0.5 Å) and charge (+1 or -1) interacting in explicit water (TIP3P) under ambient conditions. We extract their potentials of mean force (PMFs). We develop an interpolation scheme, called i-PMF, that is capable of capturing the full set of PMFs for arbitrary combinations of ion sizes ranging from 2 to 5.5 Å. The advantage of the interpolation process is computational cost. Whereas it can take 100 h to simulate each PMF by MD, we can compute an equivalently accurate i-PMF in seconds. This process may be useful for rapid and accurate calculation of the strengths of salt bridges and the effects of bridging waters in biomolecular simulations. We also find that our data is consistent with Collins' "law of matching affinities" of ion solubilities: small-small or large-large ion pairs are poorly soluble in water, whereas small-large are highly soluble.

SUBMITTER: Luksic M

PROVIDER: S-EPMC4142335 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Using interpolation for fast and accurate calculation of ion-ion interactions.

Lukšič Miha M Fennell Christopher J CJ Dill Ken A KA

The journal of physical chemistry. B 20140325 28

We perform extensive molecular dynamics (MD) simulations between pairs of ions of various diameters (2-5.5 Å in increments of 0.5 Å) and charge (+1 or -1) interacting in explicit water (TIP3P) under ambient conditions. We extract their potentials of mean force (PMFs). We develop an interpolation scheme, called i-PMF, that is capable of capturing the full set of PMFs for arbitrary combinations of ion sizes ranging from 2 to 5.5 Å. The advantage of the interpolation process is computational cost. ...[more]

PMID: 24625086

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Project description:BACKGROUND:There are many different types of microRNAs (miRNAs) and elucidating their functions is still under intensive research. A fundamental step in functional annotation of a new miRNA is to classify it into characterized miRNA families, such as those in Rfam and miRBase. With the accumulation of annotated miRNAs, it becomes possible to use deep learning-based models to classify different types of miRNAs. In this work, we investigate several key issues associated with successful application of deep learning models for miRNA classification. First, as secondary structure conservation is a prominent feature for noncoding RNAs including miRNAs, we examine whether secondary structure-based encoding improves classification accuracy. Second, as there are many more non-miRNA sequences than miRNAs, instead of assigning a negative class for all non-miRNA sequences, we test whether using softmax output can distinguish in-distribution and out-of-distribution samples. Finally, we investigate whether deep learning models can correctly classify sequences from small miRNA families. RESULTS:We present our trained convolutional neural network (CNN) models for classifying miRNAs using different types of feature learning and encoding methods. In the first method, we explicitly encode the predicted secondary structure in a matrix. In the second method, we use only the primary sequence information and one-hot encoding matrix. In addition, in order to reject sequences that should not be classified into targeted miRNA families, we use a threshold derived from softmax layer to exclude out-of-distribution sequences, which is an important feature to make this model useful for real transcriptomic data. The comparison with the state-of-the-art ncRNA classification tools such as Infernal shows that our method can achieve comparable sensitivity and accuracy while being significantly faster. CONCLUSION:Automatic feature learning in CNN can lead to better classification accuracy and sensitivity for miRNA classification and annotation. The trained models and also associated codes are freely available at https://github.com/HubertTang/DeepMir.

Dataset Information

Using interpolation for fast and accurate calculation of ion-ion interactions.

Publications

Using interpolation for fast and accurate calculation of ion-ion interactions.

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