Mass Spectrometry

Outline the steps involved in identifying a protein by peptide mass fingerprinting (PMF) using MALDI-TOF MS.

1. Protein Separation and Digestion:

2. Peptide Extraction and Preparation:

3. MALDI-TOF MS Analysis

4. Database Matching:

1. Protein Separation and Digestion:

   • The protein of interest is

• isolated (e.g., by 2D gel electrophoresis) and excised from the gel.

1. Protein Separation and Digestion:

   • In-gel digestion

• trypsin

• cleaves the protein at lysine (K) and arginine (R) residues,

  • generating peptide fragments.

1. Peptide Extraction and Preparation:

   • Peptides are

• extracted from the gel

• mixed with a matrix compound (e.g., α-cyano-4-hydroxycinnamic acid).

• Spotted onto a MALDI target plate

• crystallized.

1. MALDI-TOF MS Analysis:

   • A laser

• ionizes the peptides,

• accelerated into the time-of-flight (TOF) analyzer.

1. MALDI-TOF MS Analysis:

   • Peptides separate based on

• their mass-to-charge ratio (m/z),

• smaller/lighter ions reaching the detector first.

1. MALDI-TOF MS Analysis:

   • A mass spectrum is generated, displaying

• peaks corresponding to peptide masses.

1. Database Matching:

   • The experimental peptide masses are

• compared to theoretical masses

• from in silico digests of proteins in databases

• (e.g., Mascot).

1. Database Matching:

   • Statistical scoring identifies the best-match protein

• based on the number of matching peptides and mass accuracy.

Key Limitation: PMF requires the protein to be

in the database

novel or heavily modified proteins may not be identified.

Label-Free Quantitation
Advantages:

1. Simplicity and Cost-Effectiveness:

2. Unlimited Sample Comparisons:

3. Compatibility with Any Sample:

Stable Isotope Labeling (e.g., ICAT)
Advantages:

1. Higher Accuracy: .

2. Reduced Sample Complexity:

3. Multiplexing Capability:

Label-Free Quantitation
Advantages:

1. Simplicity and Cost-Effectiveness:

1. No chemical labeling or metabolic incorporation required, reducing experimental complexity and cost.

Label-Free Quantitation
Advantages:

1. Unlimited Sample Comparisons:

1. Suitable for large cohort studies (e.g., clinical proteomics) as it is not constrained by the number of isotopic labels.

Label-Free Quantitation
Advantages:

1. Compatibility with Any Sample:

1. Applicable to tissues, biofluids, or organisms where metabolic labeling (e.g., SILAC) is impractical (e.g., human samples).

Stable Isotope Labeling (e.g., ICAT)
Advantages:

1. Higher Accuracy:

1. Internal standards (e.g., heavy/light ICAT tags) minimize technical variability during MS analysis, improving quantitative precision.

table Isotope Labeling (e.g., ICAT)
Advantages:

1. Reduced Sample Complexity:

1. Enrichment of labeled peptides (e.g., cysteine-containing peptides in ICAT) simplifies spectra and enhances detection of low-abundance proteins.

stable Isotope Labeling (e.g., ICAT)
Advantages:

1. Multiplexing Capability:

1. Enables simultaneous comparison of multiple samples (e.g., TMT/iTRAQ), streamlining differential analysis.

Key Trade-off: Label-free is ?,

more flexible but less precise

Key Trade-off: isotopic labeling offers

precision at the cost of sample limitations and higher expense.

A Ka/Ks ratio > 1 indicates

positive selection,

positive selection where

nonsynonymous substitutions occur at

a higher rate than synonymous substitutions (Ks, silent changes).

nonsynonymous substitutions

(Ka, changes altering the amino acid sequence)

Ks,

silent changes).

A Ka/Ks ratio > 1 indicates positive selection

is suggests:

• adaptive evolution,

  • amino acid changes conferring a functional advantage

• functional advantage (e.g.,

• pathogen-host interactions, drug resistance, or novel traits).

• Examples positive selection:

• Immune-related genes (e.g., MHC), viral proteins, or reproductive proteins.

Calculation of Ka/Ks Ratio

1. Align homologous gene sequences

2. Count substitutions:

3. Use models

   1. Compute ratio:

1. Align homologous gene sequences (e.g.,

1. from different species or populations).

1. Count substitutions:

   • Ka:

• Nonsynonymous substitutions per nonsynonymous site.

1. Count substitutions:

   • Ks:

• Synonymous substitutions per synonymous site.

1. Use models (e.g., Nei-Gojobori, PAML)

1. to correct for multiple hits and codon bias.

1. Use models (e.g.,

1. Nei-Gojobori, PAML)

1. Compute ratio: Ka/Ks =

1. (nonsynonymous changes / nonsynonymous sites) / (synonymous changes / synonymous sites).

• Ka/Ks ≈ 1:

• Neutral evolution.

• Ka/Ks < 1:

• Purifying selection (functional constraint).

• Ka/Ks > 1:

• Positive selection (adaptive evolution).

Limitation: of ka/ks

Requires high-quality sequence data and robust statistical methods to distinguish selection from demographic effects.