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An efficient algorithmic approach for mass spectrometry-based disulfide connectivity determination using multi-ion analysis.

Murad W, Singh R, Yen TY - BMC Bioinformatics (2011)

Bottom Line: Additionally, each bond is associated with a confidence score, which aids in interpretation and assimilation of the results.The method was also compared with other techniques at the state-of-the-art.It was found to perform as well or better than the competing techniques.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA. whemurad@sfsu.edu

ABSTRACT

Background: Determining the disulfide (S-S) bond pattern in a protein is often crucial for understanding its structure and function. In recent research, mass spectrometry (MS) based analysis has been applied to this problem following protein digestion under both partial reduction and non-reduction conditions. However, this paradigm still awaits solutions to certain algorithmic problems fundamental amongst which is the efficient matching of an exponentially growing set of putative S-S bonded structural alternatives to the large amounts of experimental spectrometric data. Current methods circumvent this challenge primarily through simplifications, such as by assuming only the occurrence of certain ion-types (b-ions and y-ions) that predominate in the more popular dissociation methods, such as collision-induced dissociation (CID). Unfortunately, this can adversely impact the quality of results.

Method: We present an algorithmic approach to this problem that can, with high computational efficiency, analyze multiple ions types (a, b, bo, b*, c, x, y, yo, y*, and z) and deal with complex bonding topologies, such as inter/intra bonding involving more than two peptides. The proposed approach combines an approximation algorithm-based search formulation with data driven parameter estimation. This formulation considers only those regions of the search space where the correct solution resides with a high likelihood. Putative disulfide bonds thus obtained are finally combined in a globally consistent pattern to yield the overall disulfide bonding topology of the molecule. Additionally, each bond is associated with a confidence score, which aids in interpretation and assimilation of the results.

Results: The method was tested on nine different eukaryotic Glycosyltransferases possessing disulfide bonding topologies of varying complexity. Its performance was found to be characterized by high efficiency (in terms of time and the fraction of search space considered), sensitivity, specificity, and accuracy. The method was also compared with other techniques at the state-of-the-art. It was found to perform as well or better than the competing techniques. An implementation is available at: http://tintin.sfsu.edu/~whemurad/disulfidebond.

Conclusions: This research addresses some of the significant challenges in MS-based disulfide bond determination. To the best of our knowledge, this is the first algorithmic work that can consider multiple ion types in this problem setting while simultaneously ensuring polynomial time complexity and high accuracy of results.

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Related in: MedlinePlus

Comparison of the computational time (in seconds) for the exhaustive and partial generation of DMS and FMS of the proteins from Table 3. On average there was a 49.5% decrease in time to compute the DMS and 88.7% decrease in time to compute the FMS. The computations were carried out on an Intel T2390 1.86 GHz single-core processor with 1GB RAM.
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Figure 6: Comparison of the computational time (in seconds) for the exhaustive and partial generation of DMS and FMS of the proteins from Table 3. On average there was a 49.5% decrease in time to compute the DMS and 88.7% decrease in time to compute the FMS. The computations were carried out on an Intel T2390 1.86 GHz single-core processor with 1GB RAM.

Mentions: It may be noted that across the molecules, on an average, the proposed approach required examining about 78% of the entire DMS and only about 14% of the entire FMS. It is crucial to note that this reduction in search was achieved without impacting the accuracy and having considered all multiple fragment ion types (a, b, bo, b*, c, x, y, yo, y*, and z). The DMS decrease was less than the FMS decrease because the disulfide-bonded structures in the DMS were bigger and fewer in number and consequently dispersed across the spectra mass range. In Figure 6, we show the actual time taken to obtain a solution by generating the complete DMS and FMS, as well as their truncated counterparts, for each of the molecules.


An efficient algorithmic approach for mass spectrometry-based disulfide connectivity determination using multi-ion analysis.

Murad W, Singh R, Yen TY - BMC Bioinformatics (2011)

Comparison of the computational time (in seconds) for the exhaustive and partial generation of DMS and FMS of the proteins from Table 3. On average there was a 49.5% decrease in time to compute the DMS and 88.7% decrease in time to compute the FMS. The computations were carried out on an Intel T2390 1.86 GHz single-core processor with 1GB RAM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3044266&req=5

Figure 6: Comparison of the computational time (in seconds) for the exhaustive and partial generation of DMS and FMS of the proteins from Table 3. On average there was a 49.5% decrease in time to compute the DMS and 88.7% decrease in time to compute the FMS. The computations were carried out on an Intel T2390 1.86 GHz single-core processor with 1GB RAM.
Mentions: It may be noted that across the molecules, on an average, the proposed approach required examining about 78% of the entire DMS and only about 14% of the entire FMS. It is crucial to note that this reduction in search was achieved without impacting the accuracy and having considered all multiple fragment ion types (a, b, bo, b*, c, x, y, yo, y*, and z). The DMS decrease was less than the FMS decrease because the disulfide-bonded structures in the DMS were bigger and fewer in number and consequently dispersed across the spectra mass range. In Figure 6, we show the actual time taken to obtain a solution by generating the complete DMS and FMS, as well as their truncated counterparts, for each of the molecules.

Bottom Line: Additionally, each bond is associated with a confidence score, which aids in interpretation and assimilation of the results.The method was also compared with other techniques at the state-of-the-art.It was found to perform as well or better than the competing techniques.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA. whemurad@sfsu.edu

ABSTRACT

Background: Determining the disulfide (S-S) bond pattern in a protein is often crucial for understanding its structure and function. In recent research, mass spectrometry (MS) based analysis has been applied to this problem following protein digestion under both partial reduction and non-reduction conditions. However, this paradigm still awaits solutions to certain algorithmic problems fundamental amongst which is the efficient matching of an exponentially growing set of putative S-S bonded structural alternatives to the large amounts of experimental spectrometric data. Current methods circumvent this challenge primarily through simplifications, such as by assuming only the occurrence of certain ion-types (b-ions and y-ions) that predominate in the more popular dissociation methods, such as collision-induced dissociation (CID). Unfortunately, this can adversely impact the quality of results.

Method: We present an algorithmic approach to this problem that can, with high computational efficiency, analyze multiple ions types (a, b, bo, b*, c, x, y, yo, y*, and z) and deal with complex bonding topologies, such as inter/intra bonding involving more than two peptides. The proposed approach combines an approximation algorithm-based search formulation with data driven parameter estimation. This formulation considers only those regions of the search space where the correct solution resides with a high likelihood. Putative disulfide bonds thus obtained are finally combined in a globally consistent pattern to yield the overall disulfide bonding topology of the molecule. Additionally, each bond is associated with a confidence score, which aids in interpretation and assimilation of the results.

Results: The method was tested on nine different eukaryotic Glycosyltransferases possessing disulfide bonding topologies of varying complexity. Its performance was found to be characterized by high efficiency (in terms of time and the fraction of search space considered), sensitivity, specificity, and accuracy. The method was also compared with other techniques at the state-of-the-art. It was found to perform as well or better than the competing techniques. An implementation is available at: http://tintin.sfsu.edu/~whemurad/disulfidebond.

Conclusions: This research addresses some of the significant challenges in MS-based disulfide bond determination. To the best of our knowledge, this is the first algorithmic work that can consider multiple ion types in this problem setting while simultaneously ensuring polynomial time complexity and high accuracy of results.

Show MeSH
Related in: MedlinePlus