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TIM-Finder: a new method for identifying TIM-barrel proteins.

Si JN, Yan RX, Wang C, Zhang Z, Su XD - BMC Struct. Biol. (2009)

Bottom Line: The triosephosphate isomerase (TIM)-barrel fold occurs frequently in the proteomes of different organisms, and the known TIM-barrel proteins have been found to play diverse functional roles.With the assistance of Support Vector Machine (SVM), the three descriptors were combined to obtain a new method with improved performance, which we call TIM-Finder.TIM-Finder can serve as a competitive tool for proteome-wide TIM-barrel protein identification.

View Article: PubMed Central - HTML - PubMed

Affiliation: State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China. sijingna@gmail.com

ABSTRACT

Background: The triosephosphate isomerase (TIM)-barrel fold occurs frequently in the proteomes of different organisms, and the known TIM-barrel proteins have been found to play diverse functional roles. To accelerate the exploration of the sequence-structure protein landscape in the TIM-barrel fold, a computational tool that allows sensitive detection of TIM-barrel proteins is required.

Results: To develop a new TIM-barrel protein identification method in this work, we consider three descriptors: a sequence-alignment-based descriptor using PSI-BLAST e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. With the assistance of Support Vector Machine (SVM), the three descriptors were combined to obtain a new method with improved performance, which we call TIM-Finder. When tested on the whole proteome of Bacillus subtilis, TIM-Finder is able to detect 194 TIM-barrel proteins at a 99% confidence level, outperforming the PSI-BLAST search as well as one existing fold recognition method.

Conclusions: TIM-Finder can serve as a competitive tool for proteome-wide TIM-barrel protein identification. The TIM-Finder web server is freely accessible at http://202.112.170.199/TIM-Finder/.

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The overall performance of three descriptors individually measured by ROC analysis.
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Figure 2: The overall performance of three descriptors individually measured by ROC analysis.

Mentions: The overall performance of the PSI-BLAST-based descriptor was measured using Receiver Operator Characteristic (ROC) analysis [27], which plots true positive rate (TPR) (i.e., Sensitivity) as a function of false positive rate (FPR) (i.e., 1-Specificity). The area under the ROC curve (AUC) was also employed to assess the performance. As shown in Figure 2, the PSI-BLAST-based descriptor results in an AUC value of 0.920. At a 5% FPR control, the PSI-BLAST-based descriptor can correctly detect 74.8% of TIM-barrel proteins. As a profile-based sequence searching algorithm, PSI-BLAST has been widely applied in many aspects of protein structure and function prediction. For instance, the PSI-BLAST algorithm has been integrated into most state-of-the-art fold recognition methods [12-14]. It also acts as a reference algorithm to benchmark any newly developed fold recognition method. In this work, the PSI-BLAST-based descriptor was used as a key component to construct our TIM-barrel protein prediction system.


TIM-Finder: a new method for identifying TIM-barrel proteins.

Si JN, Yan RX, Wang C, Zhang Z, Su XD - BMC Struct. Biol. (2009)

The overall performance of three descriptors individually measured by ROC analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2803183&req=5

Figure 2: The overall performance of three descriptors individually measured by ROC analysis.
Mentions: The overall performance of the PSI-BLAST-based descriptor was measured using Receiver Operator Characteristic (ROC) analysis [27], which plots true positive rate (TPR) (i.e., Sensitivity) as a function of false positive rate (FPR) (i.e., 1-Specificity). The area under the ROC curve (AUC) was also employed to assess the performance. As shown in Figure 2, the PSI-BLAST-based descriptor results in an AUC value of 0.920. At a 5% FPR control, the PSI-BLAST-based descriptor can correctly detect 74.8% of TIM-barrel proteins. As a profile-based sequence searching algorithm, PSI-BLAST has been widely applied in many aspects of protein structure and function prediction. For instance, the PSI-BLAST algorithm has been integrated into most state-of-the-art fold recognition methods [12-14]. It also acts as a reference algorithm to benchmark any newly developed fold recognition method. In this work, the PSI-BLAST-based descriptor was used as a key component to construct our TIM-barrel protein prediction system.

Bottom Line: The triosephosphate isomerase (TIM)-barrel fold occurs frequently in the proteomes of different organisms, and the known TIM-barrel proteins have been found to play diverse functional roles.With the assistance of Support Vector Machine (SVM), the three descriptors were combined to obtain a new method with improved performance, which we call TIM-Finder.TIM-Finder can serve as a competitive tool for proteome-wide TIM-barrel protein identification.

View Article: PubMed Central - HTML - PubMed

Affiliation: State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China. sijingna@gmail.com

ABSTRACT

Background: The triosephosphate isomerase (TIM)-barrel fold occurs frequently in the proteomes of different organisms, and the known TIM-barrel proteins have been found to play diverse functional roles. To accelerate the exploration of the sequence-structure protein landscape in the TIM-barrel fold, a computational tool that allows sensitive detection of TIM-barrel proteins is required.

Results: To develop a new TIM-barrel protein identification method in this work, we consider three descriptors: a sequence-alignment-based descriptor using PSI-BLAST e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. With the assistance of Support Vector Machine (SVM), the three descriptors were combined to obtain a new method with improved performance, which we call TIM-Finder. When tested on the whole proteome of Bacillus subtilis, TIM-Finder is able to detect 194 TIM-barrel proteins at a 99% confidence level, outperforming the PSI-BLAST search as well as one existing fold recognition method.

Conclusions: TIM-Finder can serve as a competitive tool for proteome-wide TIM-barrel protein identification. The TIM-Finder web server is freely accessible at http://202.112.170.199/TIM-Finder/.

Show MeSH