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. 2011 Jan;79(1):153-64.
doi: 10.1002/prot.22868. Epub 2010 Oct 11.

A Computational Tool for Identifying Minimotifs in Protein-Protein Interactions and Improving the Accuracy of Minimotif Predictions

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Free PMC article

A Computational Tool for Identifying Minimotifs in Protein-Protein Interactions and Improving the Accuracy of Minimotif Predictions

Sanguthevar Rajasekaran et al. Proteins. .
Free PMC article

Abstract

Protein-protein interactions are important to understanding cell functions; however, our theoretical understanding is limited. There is a general discontinuity between the well-accepted physical and chemical forces that drive protein-protein interactions and the large collections of identified protein-protein interactions in various databases. Minimotifs are short functional peptide sequences that provide a basis to bridge this gap in knowledge. However, there is no systematic way to study minimotifs in the context of protein-protein interactions or vice versa. Here we have engineered a set of algorithms that can be used to identify minimotifs in known protein-protein interactions and implemented this for use by scientists in Minimotif Miner. By globally testing these algorithms on verified data and on 100 individual proteins as test cases, we demonstrate the utility of these new computation tools. This tool also can be used to reduce false-positive predictions in the discovery of novel minimotifs. The statistical significance of these algorithms is demonstrated by an ROC analysis (P = 0.001).

Figures

Figure 1
Figure 1. Evaluation of the BLAST threshold used to create extended-MINT in the Similarity-PPI filter
The Similarity-PPI filter was applied to various datasets and sensitivity, specificity and the discrimination ratio were measured and plotted. Different datasets were created by using BLAST to identify proteins with sequence similarity; the BLAST threshold was varied.
Figure 2
Figure 2. ROC curve for Similarity- PPI Filter
ROC curves for the Similarity-PPI curve were generated with the R project software package. The empirical curve (black) and binormal curve (red) are shown. The binormal curve is calculated based on the assumption that the data is from a normal distribution. The area under the empirical curve is 0.9 (p = 0.001).
Figure 3
Figure 3. ROC curve for Frequency Score Filter
The Frequency Score Filter previously implemented in MnM was analyzed and an ROC curve was plotted for the sensitivity and specificity of the filter by varying the frequency threshold. Curves are as in Fig. 2. The area under the empirical curve is 0.7 (p = 0.08).
Figure 4
Figure 4. Image of filter selector and results modification added to the Minimotif Miner 2 web application
The category filters in the Protein-Protein Interaction section was added for this paper.
Figure 5
Figure 5. Screenshots of Results table in MnM
Image of restuls table from an MnM analysis of acyl-CoA dehydrogenase (NP_000007). MnM returns 75 minimotifs predictions.
Figure 6
Figure 6. Screenshots of MnM website with PPI filter applied
Image of results table from an MnM analysis of specific acyl-CoA dehydrogenase (NP_000007) after applying the PPI filter. MnM returns 27 minimotif predictions after applying the PPI filter.

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