Transient Protein-Protein Interaction of the SH3-Peptide Complex via Closely Located Multiple Binding Sites

Protein-protein interactions play an essential role in cellular processes. Certain proteins form stable complexes with their partner proteins, whereas others function by forming transient complexes. The conventional protein-protein interaction model describes an interaction between two proteins under the assumption that a protein binds to its partner protein through a single binding site. In this study, we improved the conventional interaction model by developing a Multiple-Site (MS) model in which a protein binds to its partner protein through closely located multiple binding sites on a surface of the partner protein by transiently docking at each binding site with individual binding free energies. To test this model, we used the protein-protein interaction mediated by Src homology 3 (SH3) domains. SH3 domains recognize their partners via a weak, transient interaction and are therefore promiscuous in nature. Because the MS model requires large amounts of data compared with the conventional interaction model, we used experimental data from the positionally addressable syntheses of peptides on cellulose membranes (SPOT-synthesis) technique. From the analysis of the experimental data, individual binding free energies for each binding site of peptides were extracted. A comparison of the individual binding free energies from the analysis with those from atomistic force fields gave a correlation coefficient of 0.66. Furthermore, application of the MS model to 10 SH3 domains lowers the prediction error by up to 9% compared with the conventional interaction model. This improvement in prediction originates from a more realistic description of complex formation than the conventional interaction model. The results suggested that, in many cases, SH3 domains increased the protein complex population through multiple binding sites of their partner proteins. Our study indicates that the consideration of general complex formation is important for the accurate description of protein complex formation, and especially for those of weak or transient protein complexes

Identifying and Reducing Systematic Errors in Chromosome Conformation Capture Data.

Chromosome conformation capture (3C)-based techniques have recently been used to uncover the mystic genomic architecture in the nucleus. These techniques yield indirect data on the distances between genomic loci in the form of contact frequencies that must be normalized to remove various errors. This normalization process determines the quality of data analysis. In this study, we describe two systematic errors that result from the heterogeneous local density of restriction sites and different local chromatin states, methods to identify and remove those artifacts, and three previously described sources of systematic errors in 3C-based data: fragment length, mappability, and local DNA composition. To explain the effect of systematic errors on the results, we used three different published data sets to show the dependence of the results on restriction enzymes and experimental methods. Comparison of the results from different restriction enzymes shows a higher correlation after removing systematic errors. In contrast, using different methods with the same restriction enzymes shows a lower correlation after removing systematic errors. Notably, the improved correlation of the latter case caused by systematic errors indicates that a higher correlation between results does not ensure the validity of the normalization methods. Finally, we suggest a method to analyze random error and provide guidance for the maximum reproducibility of contact frequency maps

Random error effect.

<p>Pearson's correlation coefficients of off-diagonal elements between contact frequency maps from TH data are plotted. In the off-diagonal elements, <i>cis</i>- and <i>trans</i>-contacts are separately considered. Mean values of experimentally-determined ULCs are plotted as thick lines. Theoretically-determined ULCs are plotted as solid circles with error bars. (A) The segment size effect on ULC was evaluated at the data size of 5,000,000 points. (B) The data size effect on ULC was evaluated based on the fixed segment size of 200 kb. ULCs for larger data sizes were predicted from the data size of 1,000,000 and plotted as thin lines with error bars. (C) ULCs for normalized contact frequency maps were evaluated at the data size of 5,000,000 points. Here error bars denote the standard deviation obtained from the analysis of 10 data sets.</p

A comparison of FoldX energies with experimental values.

<p>The Pearson's correlation coefficients between the FoldX energies and the experimental data are shown.</p>++<p><b>INDEX</b> denotes a starting position in the 14-residue peptides to select representative fragment sequences for energy evaluation.</p>*<p><b>SPOT</b> denotes pseudo-binding energies.</p>+<p><b>MS Model</b> denotes the binding free energies derived from the MS model (MS-model energies).</p><p>We used 2010 SPOT synthesis data for amphiphysin from reference <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032804#pone.0032804-Landgraf1" target="_blank">[12]</a>. We took six consecutive residues in peptide sequences to evaluate the FoldX and MS-model energies.</p

The maximum local populations for representative SH3 domains are shown.

<p>The predicted pseudo-binding energies are plotted based on the MLPs. (a) The MLP data for amphiphysin are shown. The maximum binding affinity data for amphiphysin fall within the middle region of the MLP. A solid circle marks the peptide (PSRPPRPSR) from the Itch protein that has two possible binding sites. (b) The MLP data for Sho1 are shown. The fully localized interaction for Sho1 has the maximum binding affinity. A solid circle marks the peptide (NKPLPPLPVAGSSKV) from the Pbs2 protein, which is a well-known binding partner of Sho1.</p

Explanatory variables and coefficients for various systematic errors.

<p>(A) A fragment length of a fragment end is an explanatory variable of FL, and an average coverage value is a coefficient of the explanatory variable. (B) The standard deviations of the coverage values classified by old mappability scores and new mappability scores were plotted as open circles and solid circles, respectively. (C) The regression coefficients for DR from the iterative method are plotted according to the position of nearby segments. (D) The regression coefficients for DC are plotted according to the nucleotide positions from fragment ends. (E) The regression coefficients for CS were measured using 14 explanatory variables, where the variables denote 14 different chromatin states classified by a previous study. The positive value of a chromatin state means that the chromatin state contains more proteins for binding nearby DNA chains. The error bars denote standard deviations of the coefficients from three experimental results.</p

The prediction error difference between the MS model and the SS model.

<p>The differences in the prediction errors are plotted as squares, where a negative value denotes better performance of the MS model. The error bars denote the standard deviations of the differences for each SH3 domain. In this figure, the difference in prediction errors was measured by the following methods: the lowest prediction error was selected for each computational model, and the difference between the selected prediction errors from the models was evaluated for 10 training/test sets.</p

Comparison of the prediction errors between two computational models.

<p>The prediction errors from the MS and SS models are plotted as circles and triangles, respectively, and the points with the lowest prediction error are marked as solid circles and triangles. In this figure, the RMSE denotes the normalized prediction error against the experimental values.</p

Systematic errors increase the correlation.

<p>Comparison results at the fragment level between HH and TH data are shown. (A) The correlation coefficients between raw coverage values are plotted as open circles, and those between corrected values (CS + noise) are plotted as solid circles. (B) The correlation coefficients between correction factors from two experimental results are shown as a bar graph.</p