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CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval.

Karim R, Aziz MM, Shatabda S, Rahman MS, Mia MA, Zaman F, Rakin S - Sci Rep (2015)

Bottom Line: Our proposed methods borrow ideas from the field of computer vision.The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure.Experimental results clearly indicate the superiority of our approach in both running time and accuracy.

View Article: PubMed Central - PubMed

Affiliation: AlEDA Group, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Bangladesh.

ABSTRACT
The number of entries in a structural database of proteins is increasing day by day. Methods for retrieving protein tertiary structures from such a large database have turn out to be the key to comparative analysis of structures that plays an important role to understand proteins and their functions. In this paper, we present fast and accurate methods for the retrieval of proteins having tertiary structures similar to a query protein from a large database. Our proposed methods borrow ideas from the field of computer vision. The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure. Experimental results clearly indicate the superiority of our approach in both running time and accuracy. Our method is readily available for use from this website: http://research.buet.ac.bd:8080/Comograd/.

No MeSH data available.


Percentage of matches of Class, Fold, Superfamily and Family for up to top 50 retrieval results.
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f4: Percentage of matches of Class, Fold, Superfamily and Family for up to top 50 retrieval results.

Mentions: The comparison of the accuracy of the query results is provided in Fig. 4. In particular, Fig. 4 presents four line graphs one each for each SCOP label. The horizontal axis entitled “number of top results” in each line graph, gives the number of top ranked query results considered whereas the vertical axis entitled “% of ‘label’ match” gives the average number of query results that have matched the SCOP ‘label’ with the corresponding query protein. So, each point in a graph reports the average number of query results having the same SCOP ‘label’ as the corresponding query protein for a specific number of top results. Each line graph reports results for 4 methods: the method of 21 (MASASW), our method with the CoMOGrad feature only (CoMOG), and both the scaled and non-scaled version of our method combining CoMOGrad with PHOG (CoMOG + PHOG 128 and CoMOG + PHOG). We report the results for all the SCOP labels, i.e., class, fold, family and superfamily in the line graphs presented in Fig. 3 starting from the leftmost one and going in clockwise direction respectively and each graph considers top 5 to top 50 retrieval results.


CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval.

Karim R, Aziz MM, Shatabda S, Rahman MS, Mia MA, Zaman F, Rakin S - Sci Rep (2015)

Percentage of matches of Class, Fold, Superfamily and Family for up to top 50 retrieval results.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4543952&req=5

f4: Percentage of matches of Class, Fold, Superfamily and Family for up to top 50 retrieval results.
Mentions: The comparison of the accuracy of the query results is provided in Fig. 4. In particular, Fig. 4 presents four line graphs one each for each SCOP label. The horizontal axis entitled “number of top results” in each line graph, gives the number of top ranked query results considered whereas the vertical axis entitled “% of ‘label’ match” gives the average number of query results that have matched the SCOP ‘label’ with the corresponding query protein. So, each point in a graph reports the average number of query results having the same SCOP ‘label’ as the corresponding query protein for a specific number of top results. Each line graph reports results for 4 methods: the method of 21 (MASASW), our method with the CoMOGrad feature only (CoMOG), and both the scaled and non-scaled version of our method combining CoMOGrad with PHOG (CoMOG + PHOG 128 and CoMOG + PHOG). We report the results for all the SCOP labels, i.e., class, fold, family and superfamily in the line graphs presented in Fig. 3 starting from the leftmost one and going in clockwise direction respectively and each graph considers top 5 to top 50 retrieval results.

Bottom Line: Our proposed methods borrow ideas from the field of computer vision.The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure.Experimental results clearly indicate the superiority of our approach in both running time and accuracy.

View Article: PubMed Central - PubMed

Affiliation: AlEDA Group, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Bangladesh.

ABSTRACT
The number of entries in a structural database of proteins is increasing day by day. Methods for retrieving protein tertiary structures from such a large database have turn out to be the key to comparative analysis of structures that plays an important role to understand proteins and their functions. In this paper, we present fast and accurate methods for the retrieval of proteins having tertiary structures similar to a query protein from a large database. Our proposed methods borrow ideas from the field of computer vision. The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure. Experimental results clearly indicate the superiority of our approach in both running time and accuracy. Our method is readily available for use from this website: http://research.buet.ac.bd:8080/Comograd/.

No MeSH data available.