Overview

When this provider is enabled, the user may input proteins as a single solid block of text.

Each line of the text is assumed to represent a protein and is sent to the other protein providers for parsing.

If this provider is not enabled, proteins must be provided as a Python list ([]).

This provider mainly exists to support the web GUI, which is only capable of providing a single protein string. If disabled, the web GUI may not function correctly.

Scores

The score is a boolean value on a protein, indicating if the protein was produced by this provider. It is for information only and has no analytical purpose.

Overview

When this provider is enabled, proteins/peptides may be input by the user as either a solid block of text or the name of a text file.

FASTA@c:\proteins

Scores

The score is a boolean value on a protein, indicating if the protein was produced by this provider. It is for information only and has no analytical purpose.

Overview

When this provider is enabled, proteins can be provided in JSON format.

JSON format is used internally by the application and the user is not expected to craft such data manually.

JSON format is used in the web GUI. If this provider is disabled, the web GUI may not function correctly.

Scores

The score is a boolean value on a protein, indicating if the protein was produced by this provider. It is for information only and has no analytical purpose.

Overview

When enabled, the user may input proteins using database accessions:

• Uniprot accession
• Uniprot ID
• Ensembl genome protein ID

The database accessions supported may be changed in the API by setting the constructor parameter. Any accession convertible by Uniprot is supported. In the web interface, no configuration is possible and only the default values are supported.

Scores

The score is a boolean value on a protein, indicating if the protein was produced by this provider. It is for information only and has no analytical purpose.

Overview

When enabled, Consequence online is used to digest the protein and obtain scores indicating the flyability of the digested peptides.

Scores

The score output is the "consensus" value from Consequence. This indicates the number of Consequence's predictors that indicate positive peptide flyability. The value lies in the range [0,4], with all 4 predictors being the ideal outcome for a flyable peptide.

Overview

When enabled, McPred online is used to digest the protein and score the peptides based on their simulated successful cleavage probability.

Scores

The output is a "mis-cleavage chance" in the range 0 to 1. Lower scores indicate less chance of mis-cleavage and are generally considered to be better.

Overview

When enabled, OpenMS is used to provide a wide variety of digests from a protein.

The same enzyme specified in the model is assumed.

Scores

The score is a boolean value on a peptide, indicating if the peptide was produced by this provider. This has some analytical value, peptides not produced by this digester may be the result of non-standard digestion, such as miscleavage.

Overview

When enabled, this peptide provider uses PPA online to digest the peptides and score them based on their simulated detectability.

Scores

The score for each peptide is a single numerical value, indicating the simulated detectability of the peptide. Higher values indicate that the peptide is more likely to be detected.

Overview

When enabled this provider allows peptides to be entered into the workflow before the actual digest stage.

Such peptides include peptides entered manually and proteins with known digestions.

Disabling this provider in either of these cases will result in an error.

The base class, PrespecifiedProvider providers specific implementation details.

Scores

The score is a boolean value on a peptide, indicating if the peptide was produced by this provider. It is for information only and has no analytical purpose.

Overview

When enabled, this scorer asserts a sequence of "Rob rules" designed to isolate a set of "good" peptides.

Only the rank of the score is assessed (not the actual value), hence we pick from the set of "best" peptides.

Scores

One score is output, which follows the following protocol for each peptide:

To all but these are assigned SCORE 0:

H, M, NG, NV, n-term Q

Of the remainder, to all but these are assigned SCORE 1:

H > 1, NG, NV

Of the remainder, to all but these are assigned SCORE 3 (then, 4, etc...):

H > 2, NG, NV

To the remainder, to all are assigned SCORE 100.

Overview

When enabled, basic information about the peptides is output into several columns.

This information includes peptide start/end positions, terminal type, and flanking sequences.

Scores

Several scores are produced:

• Length

  The length of the peptide, in amino-acids. Excessively small or large peptides may be problematic for different reasons. The default assessor flags up lengths outside of the 6-30 range.

• Terminal type

  Which terminal the peptide appears on, if any. While non-terminal peptides are ideal, this issue is usually caught by the missing-linkers assessment.

• Start position.

  The starting position of the peptide within the protein. By default, for information only.

• End position.

  The end position of the peptide within the protein. By default, this column is for information only.

• N-flanking sequence.

  The adjacent amino-acid sequence to the N-side. By default, this column is for information only.

• C-flanking sequence.

  The adjacent amino-acid sequence to the C-side. By default, this column is for information only.

• Organism of protein.

  This is the NCBI taxonomic ID of the organism for the protein, if known. By default, this column is for information only.

• Accession of protein.

  This is the accession as provided in the input. By default, this column is for information only.

• Title of protein.

  This is the human-readable title of the protein, if known. By default, this column is for information only.

Usage notes

Note this Handler only considers the originating (specified) protein, not any other proteins this peptide might occur in. Hence, viable output is not produced if no protein was specified (i.e. if the peptide was specified manually).

Overview

When enabled, this scorer uses the DeepMsPeptide model to score the peptides based on their estimated proteotypicality.

Scores

The score produced is a real-typed classification per peptide, with 0 indicating the class of non-proteotypic peptides, and 1 indicating the class of proteotypic peptides. The ideal value is above 0.5.

Usage notes

Keras must be installed if you wish to use this scorer, it's rather big so it is not included in the program by default.

Overview

When enabled, a column is produced that red-flags digestable linkers.

Scores

The value in the output columns is the number of cuts obtained by digesting the linker. Ideally this is 0. This is 1 less than the number of fragments.

If there is no linker, or if the linker is too short, the output is "SHORT_LINKER". This is also unfavourable.

• Digestable N linker?

  If this is non-zero, the N linker is digestible by the selected enzyme. This means that the linker will need to be modified in the Qbrick, making the environment of the peptide different to that in the original protein.

• Digestable C linker?

  If this is non-zero, the N linker is digestible by the selected enzyme. This means that the linker will need to be modified in the Qbrick, making the environment of the peptide different to that in the original protein.

Overview

When enabled, a scores column is produced for the peptides based on a machine learning method that determines the quantotypicness of each peptide.

Scores

For the default model, the output is an indication of the peptides variability, with higher values being more variable (less quantotypic).

The default model has been trained to assess the variability of the peptide in relation to the protein quantification, as assessed by SeaMassSigma's Bayesian MCMC. Variabilities of the training set have been scaled to lie in the range [0, 1]. Output is thus predicted variability, with lower values indicating more quantotypic peptides. Note that the the median value of the training data is 0.14, so values above this suggest that at least half of all peptides in the training data may be more quantotypic.

Usage notes

A file_name may be provided to specify a custom ML model.

Example using the default ML algorithm:

scorer = HandlerFactory( DoesItFlyScorer )

Example using a custom ML algorithm which performs classification, where the "1" classification is more favourable:

scorer = HandlerFactory(
    klass            = DoesItFlyScorer,
    kwargs           = { "file_name": "my_classification_model.doesitfly" },
    column_overrides = [ ColumnOverride( column   = "doesitfly",
                                         weight = EWeight.NORMAL,
                                         assessor   = Assessors.Equal( 1 ) ) ] )

Overview

When enabled, an output column is produced that shows the isoelectric point estimate for each of the peptides.

Scores

• Isoelec

  An estimate of the isoelectric point for the peptide.

Overview

When enabled, output is produced for NextProt's uniqueness scorer.

Scores

• NextProt idents
• NextProt variants

The output is in two columns- the number of similar peptides including and not including variants. Ideally, peptides will have only 1 match - for the protein they originate from. Values above 1 indicate the peptide is non- proteotypic, while zero-values indicate the data is not present in NextProt.

Overview

When enabled this scorer retrieves data from peptide-atlas, notably the "empirical_proteotypic_score".

Scores

A number of scores are output into columns for each peptide:

• Isoelectric point
• Molecular weight
• Rel hydrophobicity
• N observations
• N genome location
• N protein_samples
• Proteotypic score

Usage notes

This scorer requires prior setup:

• Ensure the mysqlclient Python package is installed.

• Ensure MySql is running on localhost.

• Ensure the alacat@localhost user exists

• Ensure the alacat user has write-access to the alacat_* databases:

  GRANT ALL PRIVILEGES ON `alacat\_%` .  * TO 'alacat'@'localhost';
  

• Ensure AlacatDesigner knows the database password by adding it to the keyring:

  python -m mhelper.password_helper --set alacat dbpassword
  

Overview

When enabled, the monoisotopic and average masses are output for each peptide. Peptide formulae are also output as a byproduct.

Scores

• Average mass
• Monoisotopic mass
• Molecular formula
• AA composition

Very large or small values may be problematic, and are detected by the default assessor on the monoisotopic mass column.

Overview

When enabled this scorer invokes PeptideSieve.

Scores

Scores are produced indicating the likelihood each peptide is proteotypic for various mass-spec platforms:

• ICAT ESI
• MUDPIT ESI
• PAGE ESI
• PAGE MALDI

If PeptideSieve does not return a score, the peptide is assigned a score of 0, since this usually indicates that PeptideSieve considers the peptide to be non- proteotypic.

By default, all non-zero values are considered good. To avoid multiple similar scores affecting the output, only the MUDPIT ESI has a non VERY-LOW priority.

Overview

When enabled, output is produced that indicates if this peptide is present in the Pride Clusters human data dump.

Scores

The output is a single presence/absence (boolean) column and indicates whether this peptide has been seen before, in the Pride Clusters database

Overview

When enabled, this scorer counts the number of times this peptide's sequence and monoisotopic mass appears in the proteome.

The "proteome" .

By default, the scorer applies the following filters to the proteome:

• Forbid protein isoforms
• Forbid historic (Uniparc) proteins
• Forbid protein fragments
• Permit unreviewed (TREMBL) proteins
• Use the organisms of the state (the organism(s) mentioned in the model plus any organism(s) covered by the input protein(s)).

In code, these values may be changed via their respective constructor arguments, whilst in the web interface, the values are hardcoded to the defaults.

Scores

The output values are the sequence occurrence count, and the monoisotopic mass occurrence count, within the specified proteome(s). Two more columns are provided that give the actual IDs of the proteins in which the sequences and MMIs are repeated:

• Rep count
• MMI-rep
• Rep IDs
• MMI-rep IDs

By default, the rep-count and MMI count are assessed by the rule "n<=1". This allows peptides found in their own protein to pass, as well as peptides not found in any protein (e.g. if the organism differs or the reference proteome is missing the user's entry). A more ideal rule might be "n=1" or "n!=1" depending on whether the user expects to see their protein in the proteome or not.

Known limitations

The scorer has some limitations and manual checking the IDs of the proteins actually detected is recommended:

• By default, duplicates are red-flagged, however in some cases, such as when trying to find protein isoforms, these might be exactly what the user is looking for.
• Duplicate sequences may appear in the proteome due to multiple entries for the same protein under different names in the FASTA file.
• The simulated digest does not include missed cleavages and the scorer is unable to offer accurate results on miscleaved peptides.

Usage notes

An initial index file is generated, whereby the digester must be invoked to produce the digests for the entire proteome. This process may take some time: proteomes require a download of many megabytes and a simulated digest must be performed on their contents.

Overview

When enabled, this scorer counts the number of times the peptide occurs within its protein and input set.

Scores

• Repeats in protein

  Indicates the within-protein sequence occurrence count

• Repeats in input

  Indicates the within-input sequence occurrence count

Overview

When enabled, this scorer asserts certain string-based no-go peptide rules, the "Rob rules".

Scores

For the amino-acid checks, the scores are counts of how many violations occur. The ideal score is 0. Non-zero scores indicate the peptide contains the specified subsequence and may not make a suitable Qbrick. Note that the "GLUTAMINE_START" rule only looks at one residue and so only two possible values are allowed (true or false).

• C count

  Number of C/cysteine/cys.

• M count

  Number of M/methionine/met.

• H count,

  Number of H/histidine/his.

• NG count

  Number of NG (N/asparagine/asn, G/glycine/gly).

• DG count

  Number of DG (D/aspartic-acid/asp, G/glycine/gly).

• Q start

  Peptide starts with Q/glutamine/gln.

For the linker checks, the scores are strings, "N" indicating the N-terminal violates the rule, and "C" indicating the C-terminal violates the rule. The ideal value for such scores is blank (""). Non-blank scores indicate the amino acid will need to be substituted in the Qbrick meaning the environment of the peptide in the Qconcat will be less similar to the environment in the original protein.

• R in linker

  R/arginine/arg in linker.

• K in linker

  K/lysine/lys in linker.

• Linker missing

  No linker (terminal peptide) or linker unknown.

Source

Email from Rob B:

The problem is one of charge state, and distribution of signal over multiple charge states.

So, a ‘normal’ peptide: doubly charged.. [M+2H]++ Add a histidine, we now have [M+2H]++ and [M+3H]+++ which will, at best, split the signal Add more histidines and it gets worse

Ideally, we’d have rules that say:

Eliminate H Of all [M+2H]++ peptides, Eliminate M Eliminate NG, NV Eliminate n-term Q

Are there any left?

If not, then relax rules Add back M Add back n-term Q (note I do not want to add back NG…)

Allow one more H

Of all [M+3H]+++ peptides…. Etc

Overview

This scorer contacts the Yolanda database to score the peptides, based on whether they are found in various experiments.

Scores

One output column is produced, which indicates the presence of the peptide within the database. Positive scores indicate evidence has been recorded for the peptide, while zero scores indicate a lack of evidence. -1 indicates the peptide is unknown to Yolanda.

Note that no attempt is made to filter out false positives, non-proteotypic evidence, non-intensity evidence, or zero-valued evidence.

Usage notes

If Yolanda is not installed, no output is produced.

Overview

When enabled, Qblocks are generated by trying different possible permutations.

Scores

The score is a boolean value on a qblock, indicating if the qblock was produced by this provider. It is for information only and has no analytical purpose.

Usage notes

Usually the number of peptides in a qblock is the same as the number of peptides in a brick, which is 2. There will thus only be 2 possible permutations, both of which will be attempted. However max_permutations may be used to provide a limit in case of more esoteric scenarios.

Overview

When enabled this provider allows qblocks to be entered into the workflow before the actual qblock-producing stage.

Such qblocks include qblocks entered manually.

Disabling this provider will result in an error if qblocks are entered into the initial model.

The base class, PrespecifiedProvider providers specific implementation details.

Scores

The score is a boolean value on a qblock, indicating if the qblock was produced by this provider. It is for information only and has no analytical purpose.

Overview

When enabled, a score is produced that indicates if qbricks occur naturally in the protein sequence.

Scores

If the score is zero then the Qbrick sequence does not occur in the original protein. This means the environment of the peptides in the Qbrick will be less like the environment of the peptides in the original protein. This is the case for almost all Qbricks, unless concurrent peptides were chosen and no linker substitution was performed.

Overview

When enabled, Qmenus are generated by trying different possible permutations.

Scores

The score is a boolean value on a Qmenu, indicating if the Qmenu was produced by this provider. It is for information only and has no analytical purpose.

Overview

When enabled a meaningless test value is produced for each Qmenu.

Scores

The output value has no purpose, please ignore it.