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.
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