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Prodepth: predict residue depth by support vector regression approach from protein sequences only.

Song J, Tan H, Mahmood K, Law RH, Buckle AM, Webb GI, Akutsu T, Whisstock JC - PLoS ONE (2009)

Bottom Line: The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally.We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling.This method might prove to be a powerful tool for sequence analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, Monash University, Clayton, Melbourne, Victoria, Australia. Jiangning.Song@med.monash.edu.au

ABSTRACT
Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis.

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The relationship between RD and ASA.Error bars represent the standard deviations.
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pone-0007072-g003: The relationship between RD and ASA.Error bars represent the standard deviations.

Mentions: To investigate the interdependencies of various solvent exposure measures, we calculated the correlation coefficients between RD and other measures such as ASA, rASA, CN and B-factor (Table 1). Measure pairs that have low correlation coefficients are likely to be unrelated and can potentially provide complementary information for each other [28]. As a residue's rASA value is calculated as the normalization of its ASA using the maximum ASA for its residue type, it is easy to understand that ASA and rASA are strongly correlated with a CC of 0.92. On one hand, RD is correlated with CN (0.77). On the other hand, RD is negatively correlated with the ASA (−0.62) and rASA measure (−0.66), respectively, which is understandable as residues with smaller ASA values are inclined to be buried deeply and as a consequence they would have larger RD values. This negative correlation between RD and ASA is also clearly manifested in Figure 3. We also calculated the CC between RD and B-factor, but did not find any strong correlation between these two measures (Table 1).


Prodepth: predict residue depth by support vector regression approach from protein sequences only.

Song J, Tan H, Mahmood K, Law RH, Buckle AM, Webb GI, Akutsu T, Whisstock JC - PLoS ONE (2009)

The relationship between RD and ASA.Error bars represent the standard deviations.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2742725&req=5

pone-0007072-g003: The relationship between RD and ASA.Error bars represent the standard deviations.
Mentions: To investigate the interdependencies of various solvent exposure measures, we calculated the correlation coefficients between RD and other measures such as ASA, rASA, CN and B-factor (Table 1). Measure pairs that have low correlation coefficients are likely to be unrelated and can potentially provide complementary information for each other [28]. As a residue's rASA value is calculated as the normalization of its ASA using the maximum ASA for its residue type, it is easy to understand that ASA and rASA are strongly correlated with a CC of 0.92. On one hand, RD is correlated with CN (0.77). On the other hand, RD is negatively correlated with the ASA (−0.62) and rASA measure (−0.66), respectively, which is understandable as residues with smaller ASA values are inclined to be buried deeply and as a consequence they would have larger RD values. This negative correlation between RD and ASA is also clearly manifested in Figure 3. We also calculated the CC between RD and B-factor, but did not find any strong correlation between these two measures (Table 1).

Bottom Line: The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally.We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling.This method might prove to be a powerful tool for sequence analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, Monash University, Clayton, Melbourne, Victoria, Australia. Jiangning.Song@med.monash.edu.au

ABSTRACT
Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis.

Show MeSH
Related in: MedlinePlus