improvement Byonic speed

There are two major reasons that why Byonic run so slow:
too large database and too many modifications.



[using Byonic funtion "Create focused database"]
 http://www.proteinmetrics.com/wp-content/uploads/2014/10/AppNote-ByonicFocusedDatabase.pdf
Do Byonic search with few modificationsto to find possible proteins,
then using these proteins as new database to subsequent search with more modifications.



[More on Modifications ]
http://www.proteinmetrics.com/products/byonic/byonic-manual/#3.0

Glycopeptide searches.
 The fully automatic, pre-set, glycopeptide searches (the checkboxes under Glycan preset tables) allow only one glycan per peptide.
The limitation of one glycan per peptide is not a severe restriction for N-linked glycosylation, because few peptides contain two N-glycosylation motifs, that is, two occurrences of NX{S/T}.  This limitation is, however, a serious restriction for O-linked glycosylation, especially mucin glycosylation, and for O-GlcNAc-ylation on S/T, because sites for these modifications often cluster together.
The following rule gives a reasonable O-GlcNAc search.
HexNAc / +203.079373 @ S, T | common3
Alternative forms for the same rule are HexNAc @ S, T | common3 (letting the software compute the mass) and HexNAc @ OGlycan | common3.
For a faster but narrower search, use HexNAc @ S, T | common2, or for an intermediate search, designate the modification as both common2 and rare1.
Here is a slightly expanded search rule:


[using Pviewer process first]
http://www.proteinmetrics.com/products/byonic/byonic-faq/
What’s the difference between Byonic and Preview?
Preview offers an initial peek preview of your data to help you set the parameters for a much more sensitive Byonic search.
Preview advises the user on mass accuracy, digestion specificity, and the prevalence of ~ 60 common modifications. Preview optionally recalibrates the m/z measurements to improve sensitivity/specificity for subsequent searches (using any search engine).

How should I prepare my data for Byonic?
First convert the data from the instrument’s native format to one of Byonic’s supported formats, initially just MGF.
To perform this conversion, use the instrument vendor’s software or ProteoWizard.
 Then run the data through Preview, and if the scatter plots of mass error vs. m/z for precursor and fragment mass errors reveal systematic m/z measurement errors, ask Preview to recalibrate the data and return a new m/z-recalibrated MGF file.

How should I choose search parameters?
Set mass tolerances appropriate for the type of instrument, for example, 10 ppm precursor tolerance for a high resolution instrument and 0.3 Dalton fragment tolerance for ion trap fragmentation.
Preview’s mass error plots can help you choose these tolerances.
Preview’s m/z recalibration can remove systematic errors so that data can be run with tighter tolerances, for example, 5 ppm instead of 10 ppm tolerance for a high resolution instrument. Tight tolerances offer significant advantage for difficult searches, for example, resolving nearly isobaric modifications such as sulfation and phosphorylation, or identifying glycopeptides with poor fragmentation.
Tolerances can be set in either Da or ppm, as appropriate for the instrument.
Set digestion specificity based on the prevalence of nonspecific digestion and the complexity of the search.
If the modification complexity of the search is high, as in wildcard, glycosylation, or oxidative footprinting searches, it is best to avoid the extra complexity of searching for nonspecific digestion, unless the nonspecific digestion rate is high (say, over 20% of all peptides).
Set modifications based upon prevalence reported by Preview and the goal of the study.
If the goal of the study is phosphorylation site identification, enable up to 3 or even 4 phosphorylation sites per peptide, and avoid other modifications unless they are prevalent.
If the goal of the study is simply protein identification, it is best to enable only the most common modifications (for example, oxidized methionine and deamidation).
Be especially alert to over-alkylation; in some samples, over-alkylation is so common that the majority of peptides carry iodoacetamide artifacts.
Some modifications are more costly (for example, sodiation on any residue as opposed to just E and D), but others (such as pyro-glu on N-terminal glutamine) barely increase the size of the search space.