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A generic method for assignment of reliability scores applied to solvent accessibility predictions.

Petersen B, Petersen TN, Andersen P, Nielsen M, Lundegaard C - BMC Struct. Biol. (2009)

Bottom Line: This is evident when comparing the Pearson's correlation coefficient for the upper 20% of predictions sorted according to reliability.For this subset, values of 0.79 and 0.74 are obtained using our and the compared method, respectively.This tendency is true for any selected subset.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Biological Sequence Analysis-CBS, Department of Systems Biology, Kemitorvet 208, Technical University of Denmark-DTU, Lyngby, Denmark. bent@cbs.dtu.dk

ABSTRACT

Background: Estimation of the reliability of specific real value predictions is nontrivial and the efficacy of this is often questionable. It is important to know if you can trust a given prediction and therefore the best methods associate a prediction with a reliability score or index. For discrete qualitative predictions, the reliability is conventionally estimated as the difference between output scores of selected classes. Such an approach is not feasible for methods that predict a biological feature as a single real value rather than a classification. As a solution to this challenge, we have implemented a method that predicts the relative surface accessibility of an amino acid and simultaneously predicts the reliability for each prediction, in the form of a Z-score.

Results: An ensemble of artificial neural networks has been trained on a set of experimentally solved protein structures to predict the relative exposure of the amino acids. The method assigns a reliability score to each surface accessibility prediction as an inherent part of the training process. This is in contrast to the most commonly used procedures where reliabilities are obtained by post-processing the output.

Conclusion: The performance of the neural networks was evaluated on a commonly used set of sequences known as the CB513 set. An overall Pearson's correlation coefficient of 0.72 was obtained, which is comparable to the performance of the currently best public available method, Real-SPINE. Both methods associate a reliability score with the individual predictions. However, our implementation of reliability scores in the form of a Z-score is shown to be the more informative measure for discriminating good predictions from bad ones in the entire range from completely buried to fully exposed amino acids. This is evident when comparing the Pearson's correlation coefficient for the upper 20% of predictions sorted according to reliability. For this subset, values of 0.79 and 0.74 are obtained using our and the compared method, respectively. This tendency is true for any selected subset.

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Related in: MedlinePlus

Reliability baseline and standard deviation fitting. The reliability is shown as a function of the predicted exposure for the Cull-1764 data set. In grey is shown the fitted reliability baseline and standard deviation. The insert shows the baseline corrected Z-scores as a function of the predicted surface exposure.
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Figure 5: Reliability baseline and standard deviation fitting. The reliability is shown as a function of the predicted exposure for the Cull-1764 data set. In grey is shown the fitted reliability baseline and standard deviation. The insert shows the baseline corrected Z-scores as a function of the predicted surface exposure.

Mentions: From the training it became apparent that the two output values (exposure and reliability, respectively) from the network were highly correlated. This is most likely due to the fact that deeply buried residues are relatively simple to predict and hence can be predicted with high reliability in contrast to exposed residues that have more complex characteristics. An example of this correlation is shown in Figure 5.


A generic method for assignment of reliability scores applied to solvent accessibility predictions.

Petersen B, Petersen TN, Andersen P, Nielsen M, Lundegaard C - BMC Struct. Biol. (2009)

Reliability baseline and standard deviation fitting. The reliability is shown as a function of the predicted exposure for the Cull-1764 data set. In grey is shown the fitted reliability baseline and standard deviation. The insert shows the baseline corrected Z-scores as a function of the predicted surface exposure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2725087&req=5

Figure 5: Reliability baseline and standard deviation fitting. The reliability is shown as a function of the predicted exposure for the Cull-1764 data set. In grey is shown the fitted reliability baseline and standard deviation. The insert shows the baseline corrected Z-scores as a function of the predicted surface exposure.
Mentions: From the training it became apparent that the two output values (exposure and reliability, respectively) from the network were highly correlated. This is most likely due to the fact that deeply buried residues are relatively simple to predict and hence can be predicted with high reliability in contrast to exposed residues that have more complex characteristics. An example of this correlation is shown in Figure 5.

Bottom Line: This is evident when comparing the Pearson's correlation coefficient for the upper 20% of predictions sorted according to reliability.For this subset, values of 0.79 and 0.74 are obtained using our and the compared method, respectively.This tendency is true for any selected subset.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Biological Sequence Analysis-CBS, Department of Systems Biology, Kemitorvet 208, Technical University of Denmark-DTU, Lyngby, Denmark. bent@cbs.dtu.dk

ABSTRACT

Background: Estimation of the reliability of specific real value predictions is nontrivial and the efficacy of this is often questionable. It is important to know if you can trust a given prediction and therefore the best methods associate a prediction with a reliability score or index. For discrete qualitative predictions, the reliability is conventionally estimated as the difference between output scores of selected classes. Such an approach is not feasible for methods that predict a biological feature as a single real value rather than a classification. As a solution to this challenge, we have implemented a method that predicts the relative surface accessibility of an amino acid and simultaneously predicts the reliability for each prediction, in the form of a Z-score.

Results: An ensemble of artificial neural networks has been trained on a set of experimentally solved protein structures to predict the relative exposure of the amino acids. The method assigns a reliability score to each surface accessibility prediction as an inherent part of the training process. This is in contrast to the most commonly used procedures where reliabilities are obtained by post-processing the output.

Conclusion: The performance of the neural networks was evaluated on a commonly used set of sequences known as the CB513 set. An overall Pearson's correlation coefficient of 0.72 was obtained, which is comparable to the performance of the currently best public available method, Real-SPINE. Both methods associate a reliability score with the individual predictions. However, our implementation of reliability scores in the form of a Z-score is shown to be the more informative measure for discriminating good predictions from bad ones in the entire range from completely buried to fully exposed amino acids. This is evident when comparing the Pearson's correlation coefficient for the upper 20% of predictions sorted according to reliability. For this subset, values of 0.79 and 0.74 are obtained using our and the compared method, respectively. This tendency is true for any selected subset.

Show MeSH
Related in: MedlinePlus