# Presentation: 717355

## Improving Statistical Significance Assignment in Mass Spectrometry Based Peptide Identification

- Overview
- Statistical Significance in Peptide Identification
- Statistics through deNovo method
- Example
- New way to Combine Search Results

- Yi-Kuo Yu
- Quantitative Molecular Biological Physics (QMBP) Group
- National Center for Biotechnology Information
- National Library of Medicine, National Institutes of Health

## QMBP Research using Biowulf

- Molecular Dynamics
-     Protein Folding Simulations
- Molecular Networks
-     Information Transduction in
-      protein-protein interaction networks
- Molecular Interactions
-     Exact electrostatic force/energy
- Mass Spectrometry
-    statistics of peptide/protein ID

## Mass Spect. Task force

-    LCDR
-      Gelio
-      Alves

-                                                        Relevant References:
- Gelio Alves and Yi-Kuo Yu.
-       Statistical Characterization of a 1D Random Potential Problem – with applications in score
-       statistics of MS-based peptide sequencing
-       Physica A  (2008), **387**:6538-6544. doi:10.1016.
- 2.    G. Alves , A. Ogurtsov, Wells W. Wu, Guanhui Wang, R-F Shen and Yi-Kuo Yu
-       Calibrating E-values for MS2 Database Search Methods
-       *Biology Direct*, 2007, **2**:26
- 3.   Gelio Alves, Wells W. Wu,Guanghui Wang, Rong-Fong Shen and Yi-Kuo Yu
-       Enhancing Peptide Identification Confidence by Combining Search Methods
-       *Journal of Proteome Research*, **7**:3102-3113 (2008).

- Dr.  Aleksey
-       Ogurtsov

## Overview: MS-based Proteomics

- Protein ID in a mixture,
- Protein Circuit **/ **Localization,
- (3) Signaling and Communication.

- Desirable to understand
-  Proteins involved?

- A generic pathway

**Protein Identification is important for Proteomics/System Biology**

## What can mass spect do?

- Protein identification through peptide identification:

- MS/MS produces fragments of partial-peptides [(a,b,c)s and (x,y,z)s], thus provides more information about the peptide for sequencing.

- Given a set of MS/MS spectra, by database searches or denovo sequencing,
- one may identify peptides involved and then infer the proteins involved.

## What is the problem?

-   Confidence assignment in peptide identifications
- (How to **confidently interpret** biological experiments):

- Where to draw the line when selecting peptide candidates?
- How to rank peptide candidates across spectra?
- How to compare results analyzed using different search methods?
- (Does a top hit in method M1 carries the same meaning as that in method M2?)
- How to compare results from different experiments?

- A possible solution is to have robust statistical significance assignment that provides
**a quantifiable confidence measure for peptide ID**
**the flexibility to compare results from different spectra and even from different search methods.  **

## Solid Statistics (E-values) might be our best rescue

- In the context of peptide searches,
- both the *E- and P-values *may be viewed as _monotonically decreasing functions_
- of some algorithm-dependent quality score S. For a given quality score cutoff,
_*P-value*_* *refers to the probability of finding a random hit with quality score
- greater than or equal to the cutoff.
_*E-value*_* is defined as the *expected number of hits in a random database
- with quality score greater than or equal to the cutoff.
*E = P**(random_db_size)   [Equivalent to Bonferroni Correction]
- Key assumption needed:
- Aside from the true peptides, the rest of the peptides in the database
- appear to be random with respect to any given MS/MS spectrum.
- Using correct *E-values*, we can compare search results from different spectra
-  and even different search methods!

## Apparently, most E-values reported deviate from the textbook definition.

- Apparently, most *E-values* reported deviate from the textbook definition.

## To circumvent the statistical inaccuracy:

- We developed RAId_DbS,
-       a new search method that has
-       **satisfactory** **statistics** (see below)
-       but without losing performance
-       (see ROC curves to the right)

- using _profile_ data

- using _centroid_ data

## (2) We provide a protocol to calibrate E-values:

- There exist methods that do not report *E-value*. To compare the
- search results from these methods, _one needs to calibrate statistics_, see
- G  Alves, AY Ogurtsov, Y-K Yu,
**Calibrating E-values for MS2 Database Search Methods**
- [*Biol. Direct* (2007), **2**:26]   _problem: may lose spectrum-specific statistics. _

## Other advantage of having accurate E-value:

- Simple connection to the False Discovery Rate (FDR)

- where *E**c* is the *E-value* cutoff, N is the total number of spectra, and H(*E**c*) is the cumulative number of hits with E-value smaller than or equal to *E**c*.
- No need to search in decoy database to get FDR!

- (3) _Statistical calibration leads to a way to __**combine **__search results from _
- _different methods (__but can’t enforce spectrum-specific statistics__)_, see
- Alves et al.  **Enhancing peptide identification by **
**combining search methods **[J*PR* (2008), **7**:3102–3113].

## Spectrum-Specific Statistics

- Why **spectrum-specific **statistics?

- Fragment peaks depend on parent ion charge state,
- the presence of co-eluted materials and their physical interactions
- with each other, and the relative kinetic energy of the inert gas (CID),
- or the relative kinetic energy of the electrons (ECD, ETD), and
- the peptide/co-eluted material concentrations, and the peptide/co-eluted
- material conformation in gaseous phase, etc.

## The complication

-  Spectrum-specific noise demands spectrum-specific statistics.
-  Not every search method can do this.
-  Only two known methods use spectrum-specific statistics:
-   X!Tandem (fitted empirically)
-   RAId_DbS (derived theoretically)
-  Recently SEQUEST developers have also investigated the
-   possibility of using spectrum-specific XCorr statistics.

## A new approach: obtaining statistical standard from scoring all possible peptides

- Merit: Bypass the need of decoy database (when FDR is considered)
-           and the need of E-value calibration.

- Challenge: the **astronomically large **number of peptides to score.
-            For a peptide of  molecular weight 2300 Da there are ≈ 1026 .
-                   Scoring  109  peptides per second would take  ≈ 3.2 x 109 years!

- all possible human tryptic peptides

- all possible tryptic peptides

- Solution: see our recent paper, Physica A  (2008), **387**:6538-6544. doi:10.1016.

## A new approach: obtaining statistical standard from scoring all possible peptides (cont...)

- Algorithm: also capable of incorporating internal structures such as
-                   peptide lengths, hydrophobicity etc. by extending the dimension
-                   of the internal array. [Physica A  (2008), **387**:6538-6544]
- Scoring functions: RAId_DbS, Hyperscore (X!Tandem), K-score, XCorr, WP.

- This dynamic programming algorithm can score all possible peptides
- in a few seconds. A similar algorithm was proposed independently
- by Pevzner’s group [JPR (2008), **7**:3354-3363].

## ISB, Centroid data 

- ISB, Centroid data

- P-value of each candidate peptide from the 50MB random database is
- Inferred from the denovo score histogram of *all* possible peptides.

- (RAId_DbS strategy).

## Combining search results

- For a given spectrum *σ*, search a database using methods,
- M1 and M2, return hit lists L1(*σ*) and L2(*σ*) respectively along with database P-values *P*db.

- L1(*σ*)
- SAMPLER      1.4e-4
- TVPMRQK      4.6e-2
- HVGTMHK      0.13
- ............

- L2(*σ*)
- GAMHLER      3.4e-6
- TVPMRQK      1.6e-3
- VGTMGSK      0.06
- ............

- L1(*σ*) U L2(*σ*)
- GAMHLER      1.0        3.4e-6
- SAMPLER      1.4e-4    1.0
- TVPMRQK      4.6e-2   1.6e-3
- VGTMGSK      1.0         0.06
- HVGTMHK      0.13       1.0
- ............

- Peptide not present in a report list is assigned a database P-value 1.

##  Remarks and Acknowledgement

- It is anticipated that combining search methods that are orthogonal to each other might be most advantageous. It is easy to check the correlation between information utilized by various scoring methods.
- RAId_denovo can be accessed from
-          _[http://www.ncbi.nlm.nih.gov/CBBresearch/qmbp/](http://www.ncbi.nlm.nih.gov/CBBresearch/qmbp/)_
- (standalone version will be available for download this summer)

- We thank the administrative group of the Biowulf computers for constant technical support, which considerably helped our computational progress in improving the peptide identification statistics over the past few years.

- We thank Dr. R.-F. Shen for providing various peptide MS/MS data.