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Transmembrane protein alignment and fold recognition based on predicted topology.

Wang H, He Z, Zhang C, Zhang L, Xu D - PLoS ONE (2013)

Bottom Line: We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs.The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition.The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, People's Republic of China.

ABSTRACT

Background: Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the increasing number of experimental TMP structures available, template-based methods have the potential to become broadly applicable for TMP structure prediction. However, the current fold recognition methods for TMPs are not as well developed as they are for soluble proteins.

Methodology: We developed a novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment. The method utilizes topology-based features in alignment together with sequence profile and solvent accessibility. It also incorporates a gap penalty that depends on predicted topology structure segments. Given the difference between α-helical transmembrane protein (αTMP) and β-strands transmembrane protein (βTMP), parameters of scoring functions are trained respectively for these two protein categories using 58 αTMPs and 17 βTMPs in a non-redundant training dataset.

Results: We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs. Our method achieved 10% and 9% better accuracies than HHalign in αTMPs and βTMPs, respectively. The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition. The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.

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Topological arrangements of top-ranked templates for target 1NEK_D.1YQ3_D and 1KF6_D are the top-2 templates ranked by raw score.
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pone-0069744-g005: Topological arrangements of top-ranked templates for target 1NEK_D.1YQ3_D and 1KF6_D are the top-2 templates ranked by raw score.

Mentions: As expected, the targets yielded the best raw scores (smallest) when they aligned to themselves as shown by the data points in the graph’s left-top area. In the case of 1NEK_D, templates with structural similarity less than 0.4 of TM-Score cluster in the graph’s right-bottom area, while a few templates fall in the middle area, e.g., mitochondrial respiratory Complex II (1YQ3_D) [83] and Escherichia coli quinol-fumarate reductase (1KF6_D) [84]. These protein domains having high raw scores also have the similar topological arrangement as shown in Fig. 5. The trend line clearly indicates that the distribution of templates reflects the tendency that raw scores are negatively correlated with their structural similarities to the target protein. Although the ranking of raw scores does not always follow the structure similarities, especially for the templates with low TM-Scores, the templates in the same fold with target (TM-Scores>0.5) have more significant correlation, which is more relevant for fold recognition.


Transmembrane protein alignment and fold recognition based on predicted topology.

Wang H, He Z, Zhang C, Zhang L, Xu D - PLoS ONE (2013)

Topological arrangements of top-ranked templates for target 1NEK_D.1YQ3_D and 1KF6_D are the top-2 templates ranked by raw score.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3716705&req=5

pone-0069744-g005: Topological arrangements of top-ranked templates for target 1NEK_D.1YQ3_D and 1KF6_D are the top-2 templates ranked by raw score.
Mentions: As expected, the targets yielded the best raw scores (smallest) when they aligned to themselves as shown by the data points in the graph’s left-top area. In the case of 1NEK_D, templates with structural similarity less than 0.4 of TM-Score cluster in the graph’s right-bottom area, while a few templates fall in the middle area, e.g., mitochondrial respiratory Complex II (1YQ3_D) [83] and Escherichia coli quinol-fumarate reductase (1KF6_D) [84]. These protein domains having high raw scores also have the similar topological arrangement as shown in Fig. 5. The trend line clearly indicates that the distribution of templates reflects the tendency that raw scores are negatively correlated with their structural similarities to the target protein. Although the ranking of raw scores does not always follow the structure similarities, especially for the templates with low TM-Scores, the templates in the same fold with target (TM-Scores>0.5) have more significant correlation, which is more relevant for fold recognition.

Bottom Line: We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs.The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition.The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, People's Republic of China.

ABSTRACT

Background: Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the increasing number of experimental TMP structures available, template-based methods have the potential to become broadly applicable for TMP structure prediction. However, the current fold recognition methods for TMPs are not as well developed as they are for soluble proteins.

Methodology: We developed a novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment. The method utilizes topology-based features in alignment together with sequence profile and solvent accessibility. It also incorporates a gap penalty that depends on predicted topology structure segments. Given the difference between α-helical transmembrane protein (αTMP) and β-strands transmembrane protein (βTMP), parameters of scoring functions are trained respectively for these two protein categories using 58 αTMPs and 17 βTMPs in a non-redundant training dataset.

Results: We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs. Our method achieved 10% and 9% better accuracies than HHalign in αTMPs and βTMPs, respectively. The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition. The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.

Show MeSH