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Protein structural similarity search by Ramachandran codes.

Lo WC, Huang PJ, Chang CH, Lyu PC - BMC Bioinformatics (2007)

Bottom Line: It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms.It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools.These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan. b861636@life.nthu.edu.tw

ABSTRACT

Background: Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases.

Results: We propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms.

Conclusion: As a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era.

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Average precision-recall curves of several search methods. FAST was the most accurate search method. SARST ranked third and achieved precisions ~4% lower than CE, which was the second most accurate method in this experiment. Linear encoding methods TOPSCAN [17], YAKUSA [21] and 3D-BLAST [22] describe protein structures as strings. ProtDex2 transforms protein structures into indexes [30]. These curves of ProtDex2 and TOPSCAN were adapted from Aung and Tan's report [30]. The precision percentage is plotted on the y-axis and the recall percentage is plotted on the x-axis.
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Figure 3: Average precision-recall curves of several search methods. FAST was the most accurate search method. SARST ranked third and achieved precisions ~4% lower than CE, which was the second most accurate method in this experiment. Linear encoding methods TOPSCAN [17], YAKUSA [21] and 3D-BLAST [22] describe protein structures as strings. ProtDex2 transforms protein structures into indexes [30]. These curves of ProtDex2 and TOPSCAN were adapted from Aung and Tan's report [30]. The precision percentage is plotted on the y-axis and the recall percentage is plotted on the x-axis.

Mentions: The goal of SARST is to create an efficient database search method, information retrieval techniques that have been widely used in many database search and management fields were used to evaluate its accuracy. As shown in Figure 3, FAST was the most accurate method. SARST was the third most accurate, and had a higher accuracy when compared with YAKUSA, 3D-BLAST, TOPSCAN, BLAST, and ProtDex2, the former three of which are linear encoding methods.


Protein structural similarity search by Ramachandran codes.

Lo WC, Huang PJ, Chang CH, Lyu PC - BMC Bioinformatics (2007)

Average precision-recall curves of several search methods. FAST was the most accurate search method. SARST ranked third and achieved precisions ~4% lower than CE, which was the second most accurate method in this experiment. Linear encoding methods TOPSCAN [17], YAKUSA [21] and 3D-BLAST [22] describe protein structures as strings. ProtDex2 transforms protein structures into indexes [30]. These curves of ProtDex2 and TOPSCAN were adapted from Aung and Tan's report [30]. The precision percentage is plotted on the y-axis and the recall percentage is plotted on the x-axis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2194796&req=5

Figure 3: Average precision-recall curves of several search methods. FAST was the most accurate search method. SARST ranked third and achieved precisions ~4% lower than CE, which was the second most accurate method in this experiment. Linear encoding methods TOPSCAN [17], YAKUSA [21] and 3D-BLAST [22] describe protein structures as strings. ProtDex2 transforms protein structures into indexes [30]. These curves of ProtDex2 and TOPSCAN were adapted from Aung and Tan's report [30]. The precision percentage is plotted on the y-axis and the recall percentage is plotted on the x-axis.
Mentions: The goal of SARST is to create an efficient database search method, information retrieval techniques that have been widely used in many database search and management fields were used to evaluate its accuracy. As shown in Figure 3, FAST was the most accurate method. SARST was the third most accurate, and had a higher accuracy when compared with YAKUSA, 3D-BLAST, TOPSCAN, BLAST, and ProtDex2, the former three of which are linear encoding methods.

Bottom Line: It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms.It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools.These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan. b861636@life.nthu.edu.tw

ABSTRACT

Background: Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases.

Results: We propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms.

Conclusion: As a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era.

Show MeSH
Related in: MedlinePlus