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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

Pseudo code for APROX-FMS routine
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Figure 5: Pseudo code for APROX-FMS routine

Mentions: The pseudocode of the APPROX-FMS procedure used for generating the FMS is shown in Figure 5. The function GENFRAGS(.), in line 7, generates multiple fragment ions (a, b, bo, b*, c, x, y, yo, y*, and z) for peptide sequences in Pepsequences, which contains the disulfide-bonded peptides involved in the IM being analyzed. Next, for each element in the FMS and for each fragment in the FragSet (lines 8-11), new disulfide-bonded peptide fragment structures are formed. Line 10 rejects values greater than the TMSval, considering the Validation Match threshold. In line 12, the current FMS set is combined with the disulfide-bonded peptide fragments set TempSet using MERGE. In line 13, the FMS is trimmed using the TRIM routine. Lastly, a Validation Match VM is declared (lines 14-15) when a correspondence is found between the mass of the largest value in FMS and an experimentally determined mass value TMSval, given a Validation Match threshold.


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

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

Pseudo code for APROX-FMS routine
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Pseudo code for APROX-FMS routine
Mentions: The pseudocode of the APPROX-FMS procedure used for generating the FMS is shown in Figure 5. The function GENFRAGS(.), in line 7, generates multiple fragment ions (a, b, bo, b*, c, x, y, yo, y*, and z) for peptide sequences in Pepsequences, which contains the disulfide-bonded peptides involved in the IM being analyzed. Next, for each element in the FMS and for each fragment in the FragSet (lines 8-11), new disulfide-bonded peptide fragment structures are formed. Line 10 rejects values greater than the TMSval, considering the Validation Match threshold. In line 12, the current FMS set is combined with the disulfide-bonded peptide fragments set TempSet using MERGE. In line 13, the FMS is trimmed using the TRIM routine. Lastly, a Validation Match VM is declared (lines 14-15) when a correspondence is found between the mass of the largest value in FMS and an experimentally determined mass value TMSval, given a Validation Match threshold.

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