Search our website. Other help pages describe the format and content of the various result reports. This page attempts to explain some of the underlying concepts, especially those relating to protein inference. The reported score is Log P. During a search, if peptides fell within the mass tolerance window about the precursor mass, and the significance threshold was chosen to be 0.
Extensive testing with large target-decoy searches showed this to be too high, and the identity threshold displayed in reports has always had an empirical correction of applied. Even so, the best match could have a relatively high score, which is well separated from the distribution of random scores. In other words, the score is an outlier. This would indicate that the match is not a random event and, if tested using a method such as a target-decoy search , such matches can be shown to be reliable.
For this reason, Mascot also attempts to characterise the distribution of random scores, and provide a second, lower threshold to highlight the presence of any outlier. The lower, relative threshold is reported as the homology threshold while the higher threshold is reported as the identity threshold. The identity threshold is still useful because it is not always possible to estimate a homology threshold.
If the instrument accuracy is very high or the database is very small, there may only be a small handful of candidate sequences, so that it is not possible to say whether a match is an outlier. For a search of at least spectra, where an automatic decoy search was used, you can choose to process the Mascot scores through Percolator.
This uses machine learning to re-rank the matches, so as to obtain an optimum false discovery rate. The revised probabilities are converted to scores for reporting purposes, together with a single score threshold to indicate significance. For a search that contains a small number of queries, the protein score is the sum of the highest ions score for each distinct sequence.
That is, excluding the scores of duplicate matches, which are shown in parentheses. A small correction is applied to reduce the contribution of low-scoring random matches. This correction is a function of the total number of molecular mass matches for each query. This correction is usually very small, except in no enzyme searches. This protein score works well for small searches, and provides a logical order to the report.
If multiple queries match to a single protein, but the individual ions scores are below threshold, the combined ions scores can still place the protein high in the report.
However, the standard protein score is less satisfactory for searches with very large numbers of queries, such as MudPIT data sets. When the number of queries is comparable with the number of entries in the database, this means that there can be random, low-scoring matches for every entry. Although the average number of random matches per entry might be low, the actual number will follow a distribution, and some entries will have large numbers of low scoring matches, leading to large protein scores.
While it is obvious from a detailed study of the report that these are meaningless matches, it would be better to eliminate them entirely. So, if the ratio between the number of queries and the number of entries in the database exceeds a pre-determined threshold, the basis for calculating the protein score is changed. Only those ions scores that exceed one or both significance thresholds contribute to the score, so that low scoring, random matches have no effect.
This gives a much cleaner report for a large scale search. This threshold is 0. Note that, when calculating this threshold, if a taxonomy filter is being used, the number of entries in the database is the number remaining after the taxonomy filter.
In most cases, the matched peptides will not be unique to a single protein. Yet, we usually want to know which proteins were present in the sample. So, we are faced with the challenge of protein inference : given a set of peptide matches, which proteins do we believe were present in the sample? The usual approach is based on the "Principle of Parsimony". We report the minimum set of proteins that account for the observed peptide matches.
If we had four peptide matches, two of which occurred in protein A and two in protein B but all four were found in protein C, we would report that protein C had been identified.
Proteins A and B might be listed as "sub-set" proteins. It is perfectly possible that our sample actually contained a mixture of proteins A and B, but there is no evidence for this.
The Peptide Summary and Select Summary uses a very simple algorithm. First, we take the protein with the highest protein score, and call this hit number 1. We then take all other proteins that share the same set of peptide matches or a sub-set and include these in the same hit.
In the report, they are listed as same-set and sub-set proteins. With these proteins removed from the list, we now take the remaining protein with the highest score and repeat the process until all the significant peptide matches are accounted for. This sounds simple enough, and works well for small datasets, but larger search results create difficulties:. Answer: The Protein Identification report contains a list of proteins that have been positively identified by a significant Mascot Score in the Mascot database search [1, 2].
What makes a positive Mascot Protein identification? Read more. Mass spectrometric identification of proteins from Western blots Read more. Common protein contaminants in mass spectrometric protein ID Read more. About Alphalyse Alphalyse uses expertise in the field of protein chemistry and bioinformatics, combined with top-of-the-line mass spectrometry equipment, to provide protein analysis services globally.
Newsletter Subscribe to The Alphalyse Newsletter and get more information on protein analysis methods and applications. Who is Alphalyse? Although the molecular weight of this protein is 1. It is difficult to know how to treat such cases. If a single experimental peptide mass is allowed to match to multiple calculated masses, then a single experimental mass which matches within a repeat will give a huge and meaningless score. But, if duplicate matches are not permitted, it will be virtually impossible to get a match to such a protein because the number of measurable mass values is too small to give a statistically significant score.
Another assumption is that the experimental measurements are independent determinations. This will not be true if the data include multiple mass values for the same peptide, even if these are from ions with different charge states in an electrospray LC-MS run. Good peak detection and thresholding in both mass and time domains for LC-MS are essential for any scoring algorithm to give meaningful results.
Amino acid sequence or composition information, if included as seq … or comp … qualifiers, is treated as a filter on the candidate sequences. Ambiguous sequence or composition data can be used in a manner similar to a regular expression search in computing but it still functions as a filter, not a probabilistic match of the type found in a Blast or Fasta search. In contrast, tag … and etag … qualifiers are scored probabilistically. That is, the more qualifiers that match, the higher the score, but all qualifiers are not required to match.
Matrix Science Search our website. The histogram of the score distribution looks like this: The protein with the high score of is a 26 kDa heat shock protein from yeast. The discrimination of the search is greatly reduced, and the score for the correct match falls just below the significance level: The best match is still correct, but it not significant.
Mass Tolerances If the number of matched mass values is constant, the score in a peptide mass fingerprint will be inversely related to the mass tolerance, as shown in the example above. Limitations Like any statistical approach, Probability Based scoring depends on assumptions and models.
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