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TAP score: torsion angle propensity normalization applied to local protein structure evaluation.

Tosatto SC, Battistutta R - BMC Bioinformatics (2007)

Bottom Line: While the readily available criteria are sufficient to detect totally wrong structures, establishing the more subtle differences between plausible structures remains more challenging.It is shown to be more accurate than previous methods at estimating the validity of a protein model in terms of commonly used experimental quality parameters on two test sets representing the full PDB database and a subset of obsolete PDB structures.Highly selective TAP thresholds are derived to recognize over 90% of the top experimental structures in the absence of experimental information.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept. of Biology and CRIBI Biotechnology Centre, University of Padova, Italy. silvio.tosatto@unipd.it

ABSTRACT

Background: Experimentally determined protein structures may contain errors and require validation. Conformational criteria based on the Ramachandran plot are mainly used to distinguish between distorted and adequately refined models. While the readily available criteria are sufficient to detect totally wrong structures, establishing the more subtle differences between plausible structures remains more challenging.

Results: A new criterion, called TAP score, measuring local sequence to structure fitness based on torsion angle propensities normalized against the global minimum and maximum is introduced. It is shown to be more accurate than previous methods at estimating the validity of a protein model in terms of commonly used experimental quality parameters on two test sets representing the full PDB database and a subset of obsolete PDB structures. Highly selective TAP thresholds are derived to recognize over 90% of the top experimental structures in the absence of experimental information. Both a web server and an executable version of the TAP score are available at http://protein.cribi.unipd.it/tap/.

Conclusion: A novel procedure for energy normalization (TAP) has significantly improved the possibility to recognize the best experimental structures. It will allow the user to more reliably isolate problematic structures in the context of automated experimental structure determination.

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Related in: MedlinePlus

TAP-10 vs. R-free scatter plot on the all PDB set. The distribution of TAP score vs. R-free is shown for 13,691 structures in the all PDB set, together with the corresponding linear regression (red line). The correlation coefficient is -0.652.
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Figure 1: TAP-10 vs. R-free scatter plot on the all PDB set. The distribution of TAP score vs. R-free is shown for 13,691 structures in the all PDB set, together with the corresponding linear regression (red line). The correlation coefficient is -0.652.

Mentions: The Pearson correlation coefficients (cc) between the different experimental quality parameters were calculated for the available data (see Table 2). As expected, the correlation between experimental parameters is usually very high (cc > 0.8). The main exception is R-free with cc <= 0.62 to the X-ray resolution and DPI (see also Figure 1). As information contained in the various measures appears largely redundant, we restrict further analysis to resolution and R-free. R-free is not a perfect measure, but rather available for more structures and, perhaps, less inaccurate than the other quality parameters.


TAP score: torsion angle propensity normalization applied to local protein structure evaluation.

Tosatto SC, Battistutta R - BMC Bioinformatics (2007)

TAP-10 vs. R-free scatter plot on the all PDB set. The distribution of TAP score vs. R-free is shown for 13,691 structures in the all PDB set, together with the corresponding linear regression (red line). The correlation coefficient is -0.652.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1878508&req=5

Figure 1: TAP-10 vs. R-free scatter plot on the all PDB set. The distribution of TAP score vs. R-free is shown for 13,691 structures in the all PDB set, together with the corresponding linear regression (red line). The correlation coefficient is -0.652.
Mentions: The Pearson correlation coefficients (cc) between the different experimental quality parameters were calculated for the available data (see Table 2). As expected, the correlation between experimental parameters is usually very high (cc > 0.8). The main exception is R-free with cc <= 0.62 to the X-ray resolution and DPI (see also Figure 1). As information contained in the various measures appears largely redundant, we restrict further analysis to resolution and R-free. R-free is not a perfect measure, but rather available for more structures and, perhaps, less inaccurate than the other quality parameters.

Bottom Line: While the readily available criteria are sufficient to detect totally wrong structures, establishing the more subtle differences between plausible structures remains more challenging.It is shown to be more accurate than previous methods at estimating the validity of a protein model in terms of commonly used experimental quality parameters on two test sets representing the full PDB database and a subset of obsolete PDB structures.Highly selective TAP thresholds are derived to recognize over 90% of the top experimental structures in the absence of experimental information.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept. of Biology and CRIBI Biotechnology Centre, University of Padova, Italy. silvio.tosatto@unipd.it

ABSTRACT

Background: Experimentally determined protein structures may contain errors and require validation. Conformational criteria based on the Ramachandran plot are mainly used to distinguish between distorted and adequately refined models. While the readily available criteria are sufficient to detect totally wrong structures, establishing the more subtle differences between plausible structures remains more challenging.

Results: A new criterion, called TAP score, measuring local sequence to structure fitness based on torsion angle propensities normalized against the global minimum and maximum is introduced. It is shown to be more accurate than previous methods at estimating the validity of a protein model in terms of commonly used experimental quality parameters on two test sets representing the full PDB database and a subset of obsolete PDB structures. Highly selective TAP thresholds are derived to recognize over 90% of the top experimental structures in the absence of experimental information. Both a web server and an executable version of the TAP score are available at http://protein.cribi.unipd.it/tap/.

Conclusion: A novel procedure for energy normalization (TAP) has significantly improved the possibility to recognize the best experimental structures. It will allow the user to more reliably isolate problematic structures in the context of automated experimental structure determination.

Show MeSH
Related in: MedlinePlus