Dataset Information

Fold classification based on secondary structure--how much is gained by including loop topology?

ABSTRACT:

Background

It has been proposed that secondary structure information can be used to classify (to some extend) protein folds. Since this method utilizes very limited information about the protein structure, it is not surprising that it has a higher error rate than the approaches that use full 3D fold description. On the other hand, the comparing of 3D protein structures is computing intensive. This raises the question to what extend the error rate can be decreased with each new source of information, especially if the new information can still be used with simple alignment algorithms. We consider the question whether the information about closed loops can improve the accuracy of this approach. While the answer appears to be obvious, we had to overcome two challenges. First, how to code and to compare topological information in such a way that local alignment of strings will properly identify similar structures. Second, how to properly measure the effect of new information in a large data sample. We investigate alternative ways of computing and presenting this information.

Results

We used the set of beta proteins with at most 30% pairwise identity to test the approach; local alignment scores were used to build a tree of clusters which was evaluated using a new log-odd cluster scoring function. In particular, we derive a closed formula for the probability of obtaining a given score by chance. Parameters of local alignment function were optimized using a genetic algorithm. Of 81 folds that had more than one representative in our data set, log-odds scores registered significantly better clustering in 27 cases and significantly worse in 6 cases, and small differences in the remaining cases. Various notions of the significant change or average change were considered and tried, and the results were all pointing in the same direction.

Conclusion

We found that, on average, properly presented information about the loop topology improves noticeably the accuracy of the method but the benefits vary between fold families as measured by log-odds cluster score.

SUBMITTER: Jeong J

PROVIDER: S-EPMC1434743 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature

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Publications

Fold classification based on secondary structure--how much is gained by including loop topology?

Jeong Jieun J Berman Piotr P Przytycka Teresa T

BMC structural biology 20060308

<h4>Background</h4>It has been proposed that secondary structure information can be used to classify (to some extend) protein folds. Since this method utilizes very limited information about the protein structure, it is not surprising that it has a higher error rate than the approaches that use full 3D fold description. On the other hand, the comparing of 3D protein structures is computing intensive. This raises the question to what extend the error rate can be decreased with each new source of ...[more]

PMID: 16524467

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Project description:Although most proteins conform to the classical one-structure/one-function paradigm, an increasing number of proteins with dual structures and functions have been discovered. In response to cellular stimuli, such proteins undergo structural changes sufficiently dramatic to remodel even their secondary structures and domain organization. This "fold-switching" capability fosters protein multi-functionality, enabling cells to establish tight control over various biochemical processes. Accurate predictions of fold-switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Recently, we developed a prediction method for fold-switching proteins using structure-based thermodynamic calculations and discrepancies between predicted and experimentally determined protein secondary structure (Porter and Looger, Proc Natl Acad Sci U S A 2018; 115:5968-5973). Here we seek to leverage the negative information found in these secondary structure prediction discrepancies. To do this, we quantified secondary structure prediction accuracies of 192 known fold-switching regions (FSRs) within solved protein structures found in the Protein Data Bank (PDB). We find that the secondary structure prediction accuracies for these FSRs vary widely. Inaccurate secondary structure predictions are strongly associated with fold-switching proteins compared to equally long segments of non-fold-switching proteins selected at random. These inaccurate predictions are enriched in helix-to-strand and strand-to-coil discrepancies. Finally, we find that most proteins with inaccurate secondary structure predictions are underrepresented in the PDB compared with their alternatively folded cognates, suggesting that unequal representation of fold-switching conformers within the PDB could be an important cause of inaccurate secondary structure predictions. These results demonstrate that inconsistent secondary structure predictions can serve as a useful preliminary marker of fold switching.

Dataset Information

Fold classification based on secondary structure--how much is gained by including loop topology?

Background

Results

Conclusion

Publications

Fold classification based on secondary structure--how much is gained by including loop topology?

Similar Datasets

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