Optimizing Scoring Function of Protein-Nucleic Acid Interactions with Both Affinity and Specificity

<div><p>Protein-nucleic acid (protein-DNA and protein-RNA) recognition is fundamental to the regulation of gene expression. Determination of the structures of the protein-nucleic acid recognition and insight into their interactions at molecular level are vital to understanding the regulation function. Recently, quantitative computational approach has been becoming an alternative of experimental technique for predicting the structures and interactions of biomolecular recognition. However, the progress of protein-nucleic acid structure prediction, especially protein-RNA, is far behind that of the protein-ligand and protein-protein structure predictions due to the lack of reliable and accurate scoring function for quantifying the protein-nucleic acid interactions. In this work, we developed an accurate scoring function (named as SPA-PN, SPecificity and Affinity of the Protein-Nucleic acid interactions) for protein-nucleic acid interactions by incorporating both the specificity and affinity into the optimization strategy. Specificity and affinity are two requirements of highly efficient and specific biomolecular recognition. Previous quantitative descriptions of the biomolecular interactions considered the affinity, but often ignored the specificity owing to the challenge of specificity quantification. We applied our concept of intrinsic specificity to connect the conventional specificity, which circumvents the challenge of specificity quantification. In addition to the affinity optimization, we incorporated the quantified intrinsic specificity into the optimization strategy of SPA-PN. The testing results and comparisons with other scoring functions validated that SPA-PN performs well on both the prediction of binding affinity and identification of native conformation. In terms of its performance, SPA-PN can be widely used to predict the protein-nucleic acid structures and quantify their interactions.</p></div

Optimization of SPA-PN.

<p>(A) Evolution of the success rate and the average interfacial RMSD () as the iteration precedes. (B) The distribution of ISR values calculated with pre-optimized SPA-PN and optimized SPA-PN respectively.</p

Success rates () of identifying the native or near-native conformations for testing dataset2 including 232 protein-DNA and 83 protein-RNA complexes.

<p>Success rates () of identifying the native or near-native conformations for testing dataset2 including 232 protein-DNA and 83 protein-RNA complexes.</p

Schematic view of illustrating the equivalence of conventional specificity to intrinsic specificity.

<p>(A) The same nucleic acid (N, red) binding with multiple protein receptors (blue, to ), showing the conventional specificity as the gap in binding affinity of the nucleic acid binding to the specific protein receptor () in discrimination against other protein receptors. The binding affinities are represented with corresponding energy spectrum (green). (B) The same nucleic acid (N, red) binding on a large protein receptor thought as the multiple different receptors linked together (blue) with multiple binding modes ( to ), showing the intrinsic specificity as the gap in binding affinity of the native binding mode () in discrimination against other binding modes.</p

A typical example of protein-nucleic acid complex (PDB 1TRO).

<p>(A) Protein-nucleic acid binding structure with protein colored in blue and nucleic acid colored in red. (B) Plot of interfacial RMSD () as a function of the fraction of native contacts () for 1000 docking decoys of the typical complex. (C) Energy spectrum and distribution calculated with pre-optimized SPA-PN (green) and optimized SPA-PN (magenta), the corresponding ISR values are shown and the energy of the native conformation is marked as red.</p

Pearson correlation between the predicted affinities calculated by SPA-PN and experimental binding affinities for 30 protein-DNA complexes of testing dataset1.

<p>The correlation coefficient () is 0.862 (statistical significance ). The predicted affinities are obtained by scaling the binding scores with a linear equation:y = 0.0045x-5.129 which is a fitting equation based on the experimental affinities.</p

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Codonopilate A, a Triterpenyl Ester as Main Autotoxin in Cultivated Soil of <i>Codonopsis pilosula</i> (Franch.) Nannf

Codonopilate A (<b>1</b>), a triterpenyl ester, was isolated from monocultivated soil of annual <i>Codonopsis pilosula</i> and identified as the main autotoxin. The yield ratio of codonopilate A in dried soil was calculated as 2.04 μg/g. Other two triterpenoids, taraxeryl acetate (<b>2</b>) and 24-methylenecycloartanol (<b>3</b>), were isolated and identified as well showing weaker autotoxity. This was the first time that the potential allelochemicals and autotoxins in the cultivated soil of <i>Codonopsis pilosula</i> were reported. Accumulation of reactive oxygen species (ROS) induced by the autotoxins in the root tips of <i>Codonopsis pilosula</i> was considered as an important factor for the phytotoxic effect. This work systematically investigates the allelopathic and autotoxic effect of <i>Codonopsis pilosula</i>, and the preliminary autotoxic action mode of the three autotoxins. These findings are helpful to understand the molecular mechanism of autotoxicity and conducive to explore proper ways to degrade the autotoxins and eliminate the replanting problems of <i>Codonopsis pilosula</i>