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Mining the TRAF6/p62 interactome for a selective ubiquitination motif.

Jadhav TS, Wooten MW, Wooten MC - BMC Proc (2011)

Bottom Line: NRIF (K19), TrkA (K485), TrkB (K811), TrkC (K602 and K815), NTRK2 (K828), NTRK3 (K829) and MBP (K169) were found to possess a perfect match for the amino acid consensus motif for TRAF6/p62 ubiquitination.Collectively, our results reveal an unappreciated role for the scaffold protein in targeting ubiquitination.The findings described herein could be used to aid in identification of other E3/scaffold ubiquitination sites.

View Article: PubMed Central - HTML - PubMed

Affiliation: Program in Cellular and Molecular Biosciences, Department of Biological Sciences, 331 Funchess Hall, Auburn University, Auburn, AL, 36849, USA. wootemc@auburn.edu.

ABSTRACT
A new approach is described here to predict ubiquitinated substrates of the E3 ubiquitin ligase, TRAF6, which takes into account its interaction with the scaffold protein SQSTM1/p62. A novel TRAF6 ubiquitination motif defined as [-(hydrophobic)-k-(hydrophobic)-x-x-(hydrophobic)- (polar)-(hydrophobic)-(polar)-(hydrophobic)] was identified and used to screen the TRAF6/p62 interactome composed of 155 proteins, that were either TRAF6 or p62 interactors, or a negative dataset, composed of 54 proteins with no known association to either TRAF6 or p62. NRIF (K19), TrkA (K485), TrkB (K811), TrkC (K602 and K815), NTRK2 (K828), NTRK3 (K829) and MBP (K169) were found to possess a perfect match for the amino acid consensus motif for TRAF6/p62 ubiquitination. Subsequent analyses revealed that this motif was biased to the C-terminal regions of the protein (nearly 50% the sites), and had preference for loops (~50%) and helices (~37%) over beta-strands (15% or less). In addition, the motif was observed to be in regions that were highly solvent accessible (nearly 90%). Our findings suggest that specific Lysines may be selected for ubiquitination based upon an embedded code defined by a specific amino acid motif with structural determinants. Collectively, our results reveal an unappreciated role for the scaffold protein in targeting ubiquitination. The findings described herein could be used to aid in identification of other E3/scaffold ubiquitination sites.

No MeSH data available.


Probability – Probability (P/P) plots illustrating the relationships between values predicted under Discrete Uniform Distribution models and empirical motif match results. A: P/P plot for the experimental dataset match distribution. B: P/P plot for the negative dataset match distribution. C: P/P plot for the randomized dataset match distribution
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Figure 3: Probability – Probability (P/P) plots illustrating the relationships between values predicted under Discrete Uniform Distribution models and empirical motif match results. A: P/P plot for the experimental dataset match distribution. B: P/P plot for the negative dataset match distribution. C: P/P plot for the randomized dataset match distribution

Mentions: As expected given the large number of motif combinations analyzed (201,684 variants), results from the motif searches yielded a wide distribution of matches in the regions surrounding individual Lysines (Figure 2). The number of positive matches ranged from 1 to 7 in the experimental proteins and 1 to 6 in the negative dataset. A score of 7 “hits” represented a perfect match in that an appropriate amino acid was found at each of the variable residue locations surrounding the core Lysine. The 7 position matches found using MotifFinder were identical to those produced using SLiMSearch, thus verifying the accuracy of our programming. Both visual inspection and statistical analysis indicated, not surprisingly, a strong similarity between the overall distributions of motif matches for the experimental and negative datasets (Figure 2A). Based on Kolmogorov-Smirnov statistics the best-fitting model for both distributions, as well as the randomized dataset, was the discrete uniform distribution. Figure 3 illustrates the fit of each distribution to its predicted values. The overall similarity among the dataset match distributions was driven by the domination of low accuracy hit (1, 2, 3 or 4) frequencies in both distributions (Figure 2B: 96.5% of experimental dataset matches; 96.6% of negative). This condition was reflected in skewness and kurtosis estimates for each distribution. Both datasets were positively skewed (estimate = 0.31 for both). Consistent with their fit to a discrete uniform model, both distributions were platykurtotic (-1.96 for experimental; -1.81 for negative). Chi-squared goodness-of-fit tests were used to examine how well the observed data from the negative and experimental datasets agreed. Using multiple analyses we investigated whether the distribution of positive hits in the negative dataset conformed to the positive hits in the experimental dataset. These analyses included comparisons of the full sets of positive matches (1-7) and smaller subsets (4, 5, 6, 7 or 5, 6, 7). Because some expected cell counts were below 5, probabilities were estimated using resampling techniques. No comparisons yielded Chi-squared estimates considered to be indicative of departure from expectations (P >0.3 for all tests).


Mining the TRAF6/p62 interactome for a selective ubiquitination motif.

Jadhav TS, Wooten MW, Wooten MC - BMC Proc (2011)

Probability – Probability (P/P) plots illustrating the relationships between values predicted under Discrete Uniform Distribution models and empirical motif match results. A: P/P plot for the experimental dataset match distribution. B: P/P plot for the negative dataset match distribution. C: P/P plot for the randomized dataset match distribution
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3090762&req=5

Figure 3: Probability – Probability (P/P) plots illustrating the relationships between values predicted under Discrete Uniform Distribution models and empirical motif match results. A: P/P plot for the experimental dataset match distribution. B: P/P plot for the negative dataset match distribution. C: P/P plot for the randomized dataset match distribution
Mentions: As expected given the large number of motif combinations analyzed (201,684 variants), results from the motif searches yielded a wide distribution of matches in the regions surrounding individual Lysines (Figure 2). The number of positive matches ranged from 1 to 7 in the experimental proteins and 1 to 6 in the negative dataset. A score of 7 “hits” represented a perfect match in that an appropriate amino acid was found at each of the variable residue locations surrounding the core Lysine. The 7 position matches found using MotifFinder were identical to those produced using SLiMSearch, thus verifying the accuracy of our programming. Both visual inspection and statistical analysis indicated, not surprisingly, a strong similarity between the overall distributions of motif matches for the experimental and negative datasets (Figure 2A). Based on Kolmogorov-Smirnov statistics the best-fitting model for both distributions, as well as the randomized dataset, was the discrete uniform distribution. Figure 3 illustrates the fit of each distribution to its predicted values. The overall similarity among the dataset match distributions was driven by the domination of low accuracy hit (1, 2, 3 or 4) frequencies in both distributions (Figure 2B: 96.5% of experimental dataset matches; 96.6% of negative). This condition was reflected in skewness and kurtosis estimates for each distribution. Both datasets were positively skewed (estimate = 0.31 for both). Consistent with their fit to a discrete uniform model, both distributions were platykurtotic (-1.96 for experimental; -1.81 for negative). Chi-squared goodness-of-fit tests were used to examine how well the observed data from the negative and experimental datasets agreed. Using multiple analyses we investigated whether the distribution of positive hits in the negative dataset conformed to the positive hits in the experimental dataset. These analyses included comparisons of the full sets of positive matches (1-7) and smaller subsets (4, 5, 6, 7 or 5, 6, 7). Because some expected cell counts were below 5, probabilities were estimated using resampling techniques. No comparisons yielded Chi-squared estimates considered to be indicative of departure from expectations (P >0.3 for all tests).

Bottom Line: NRIF (K19), TrkA (K485), TrkB (K811), TrkC (K602 and K815), NTRK2 (K828), NTRK3 (K829) and MBP (K169) were found to possess a perfect match for the amino acid consensus motif for TRAF6/p62 ubiquitination.Collectively, our results reveal an unappreciated role for the scaffold protein in targeting ubiquitination.The findings described herein could be used to aid in identification of other E3/scaffold ubiquitination sites.

View Article: PubMed Central - HTML - PubMed

Affiliation: Program in Cellular and Molecular Biosciences, Department of Biological Sciences, 331 Funchess Hall, Auburn University, Auburn, AL, 36849, USA. wootemc@auburn.edu.

ABSTRACT
A new approach is described here to predict ubiquitinated substrates of the E3 ubiquitin ligase, TRAF6, which takes into account its interaction with the scaffold protein SQSTM1/p62. A novel TRAF6 ubiquitination motif defined as [-(hydrophobic)-k-(hydrophobic)-x-x-(hydrophobic)- (polar)-(hydrophobic)-(polar)-(hydrophobic)] was identified and used to screen the TRAF6/p62 interactome composed of 155 proteins, that were either TRAF6 or p62 interactors, or a negative dataset, composed of 54 proteins with no known association to either TRAF6 or p62. NRIF (K19), TrkA (K485), TrkB (K811), TrkC (K602 and K815), NTRK2 (K828), NTRK3 (K829) and MBP (K169) were found to possess a perfect match for the amino acid consensus motif for TRAF6/p62 ubiquitination. Subsequent analyses revealed that this motif was biased to the C-terminal regions of the protein (nearly 50% the sites), and had preference for loops (~50%) and helices (~37%) over beta-strands (15% or less). In addition, the motif was observed to be in regions that were highly solvent accessible (nearly 90%). Our findings suggest that specific Lysines may be selected for ubiquitination based upon an embedded code defined by a specific amino acid motif with structural determinants. Collectively, our results reveal an unappreciated role for the scaffold protein in targeting ubiquitination. The findings described herein could be used to aid in identification of other E3/scaffold ubiquitination sites.

No MeSH data available.