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Comparison of protein structures by growing neighborhood alignments.

Bhattacharya S, Bhattacharyya C, Chandra NR - BMC Bioinformatics (2007)

Bottom Line: The experimental results show that the current programs show better performance on Fischer and Novotny's benchmark datasets, than state of the art programs, e.g. DALI, CE and SSM.Our programs were also found to calculate correct alignments for proteins with huge amount of indels and internal repeats.A new scheme, resulting in two algorithms, have been developed, implemented and tested.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept. of Computer Science and Automation, Indian Institute of Science, Bangalore, India. sourangshu@csa.iisc.ernet.in <sourangshu@csa.iisc.ernet.in>

ABSTRACT

Background: Design of protein structure comparison algorithm is an important research issue, having far reaching implications. In this article, we describe a protein structure comparison scheme, which is capable of detecting correct alignments even in difficult cases, e.g. non-topological similarities. The proposed method computes protein structure alignments by comparing, small substructures, called neighborhoods. Two different types of neighborhoods, sequence and structure, are defined, and two algorithms arising out of the scheme are detailed. A new method for computing equivalences having non-topological similarities from pairwise similarity score is described. A novel and fast technique for comparing sequence neighborhoods is also developed.

Results: The experimental results show that the current programs show better performance on Fischer and Novotny's benchmark datasets, than state of the art programs, e.g. DALI, CE and SSM. Our programs were also found to calculate correct alignments for proteins with huge amount of indels and internal repeats. Finally, the sequence neighborhood based program was used in extensive fold and non-topological similarity detection experiments. The accuracy of the fold detection experiments with the new measure of similarity was found to be similar or better than that of the standard algorithm CE.

Conclusion: A new scheme, resulting in two algorithms, have been developed, implemented and tested. The programs developed are accessible at http://mllab.csa.iisc.ernet.in/mp2/runprog.html.

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Overview of algorithms developed here.
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Figure 1: Overview of algorithms developed here.

Mentions: Most protein structure comparison algorithms can be broadly classified as either distance matrix based or transformation based. This article proposes a new scheme which compares protein structures by comparing small and compact sub-structures, called neighborhoods. Neighborhoods spanning the entire protein are calculated for both the proteins. All pairs of neighborhoods from the two structures are aligned and resulting transformations are used to calculate the optimal alignments between the two proteins. Thus, alignments between protein structures are calculated by growing neighborhood alignments. This leads to a middle approach of comparing the neighborhoods using distance matrix based methods and calculating actual alignment using transformations obtained from neighborhood alignments (figure 1).


Comparison of protein structures by growing neighborhood alignments.

Bhattacharya S, Bhattacharyya C, Chandra NR - BMC Bioinformatics (2007)

Overview of algorithms developed here.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1828169&req=5

Figure 1: Overview of algorithms developed here.
Mentions: Most protein structure comparison algorithms can be broadly classified as either distance matrix based or transformation based. This article proposes a new scheme which compares protein structures by comparing small and compact sub-structures, called neighborhoods. Neighborhoods spanning the entire protein are calculated for both the proteins. All pairs of neighborhoods from the two structures are aligned and resulting transformations are used to calculate the optimal alignments between the two proteins. Thus, alignments between protein structures are calculated by growing neighborhood alignments. This leads to a middle approach of comparing the neighborhoods using distance matrix based methods and calculating actual alignment using transformations obtained from neighborhood alignments (figure 1).

Bottom Line: The experimental results show that the current programs show better performance on Fischer and Novotny's benchmark datasets, than state of the art programs, e.g. DALI, CE and SSM.Our programs were also found to calculate correct alignments for proteins with huge amount of indels and internal repeats.A new scheme, resulting in two algorithms, have been developed, implemented and tested.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept. of Computer Science and Automation, Indian Institute of Science, Bangalore, India. sourangshu@csa.iisc.ernet.in <sourangshu@csa.iisc.ernet.in>

ABSTRACT

Background: Design of protein structure comparison algorithm is an important research issue, having far reaching implications. In this article, we describe a protein structure comparison scheme, which is capable of detecting correct alignments even in difficult cases, e.g. non-topological similarities. The proposed method computes protein structure alignments by comparing, small substructures, called neighborhoods. Two different types of neighborhoods, sequence and structure, are defined, and two algorithms arising out of the scheme are detailed. A new method for computing equivalences having non-topological similarities from pairwise similarity score is described. A novel and fast technique for comparing sequence neighborhoods is also developed.

Results: The experimental results show that the current programs show better performance on Fischer and Novotny's benchmark datasets, than state of the art programs, e.g. DALI, CE and SSM. Our programs were also found to calculate correct alignments for proteins with huge amount of indels and internal repeats. Finally, the sequence neighborhood based program was used in extensive fold and non-topological similarity detection experiments. The accuracy of the fold detection experiments with the new measure of similarity was found to be similar or better than that of the standard algorithm CE.

Conclusion: A new scheme, resulting in two algorithms, have been developed, implemented and tested. The programs developed are accessible at http://mllab.csa.iisc.ernet.in/mp2/runprog.html.

Show MeSH