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Molecular Weight Determination of Unknown Proteins for NASA/JPL PAIR Program August 24, 2001

Molecular Weight Determination of Unknown Proteins for NASA/JPL PAIR Program August 24, 2001. Barbara Falkowski Falgun Patel Celia Smith. The Overall Goal. To determine molecular weight of unknown electrophoresis data. Method to Achieve the Goal.

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Molecular Weight Determination of Unknown Proteins for NASA/JPL PAIR Program August 24, 2001

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  1. Molecular Weight Determination of Unknown Proteinsfor NASA/JPL PAIR Program August 24, 2001 Barbara Falkowski Falgun Patel Celia Smith

  2. The Overall Goal • To determine molecular weight of unknown electrophoresis data

  3. Method to Achieve the Goal • Measure distances of unknown standards with PhotoShop and Spotviewer • Decide whether Spotviewer or Photoshop is the better measuring tool. • Run models on standard proteins • Decide which model(s) work the best for the standards • Run model(s) on unknown proteins. Decide which model(s) worked the best on the unknowns

  4. SpotViewer Disadvantages • Did not measure dye-front distance • One needed to go into Photoshop to mark or crop the dye-front distance. • Spotviewer missed bands • Did not always pick up bands that were thin, blurry or close together. • Sometimes gave two measurement values to one band • Or gave values that were associated with any band. • Did not pick up very light bands.

  5. PhotoShop Advantages • Did not need assistance from another program. • Not as time consuming • Light bands could be more easily discerned through color inversion/manipulation of the image. • This also worked well with tightly packed, thin and blurred bands.

  6. Quadratic Regression Quadratic Cross Validation SLIC Log-Linear Model Log-Log Model Local Linear Model Quadratic Interpolation Gels/Protein Used Models Tested • Vitelline Envelopes (VE) for two species (Strongylocentrotus purpuratus and Lytechinus pictus) • Vitelline Envelopes for two methods (DTT and mechanically isolated)

  7. Which model worked the best? • No single model was best for all of the gels. • It was found that different models worked better for different gels. Quadratic Regression Model - 15 % Gel #1 S.purp/L.pictus VE DTT Removal SLIC Model - Gradient Gel #2 Jelly + Seminal Plasma + VE Time Courses LOG-LOG Model - 12. 5% Gels Gel #4 VE + Tris Supernatant Time Course and Gel # 6 VE + Tris Pellet Time Course

  8. Why was the Quadratic Model chosen for the Gel #1?

  9. Took Quadratic Regression of standards to find the intercept and coefficients. • Used the intercept and coefficients in the equation: • LOG MW = RM^2*a +RM*b +c • Put the relative mobility of the unknowns into the equation to come up with the following results:

  10. Log Molecular Weight Results for 15% Gel

  11. What type of Cross Validation was done? • Quadratic Cross Validation using relative mobility and Log Molecular Weight • Cross Validation was not chosen at all • The predicted value for the missing band was not close the the actual value in any of the gel cases.

  12. Results for Cross Validation Model on Standards

  13. Why was the SLIC Model chosen for the Gradient Gel #2 ? • Residual Sum = 0.00 • Residual Squared Sum = 0.00 • Largest R^2 = 0.99

  14. Why was the SLIC Model was chosen for the Gel #2?

  15. Compare Values: • SLIC Type Models: Log( LN(MW) ) = A + B * LN( -LN(RM) ) • Compare Log Molecular Weight X = e ^ ( LN( X ) ) • Convert Log( LN(MW) ) into Log( MW ) Log( MW) = Log( e ^ LN(MW) )

  16. Log Molecular Weight Results for SLIC

  17. Graph result of SLIC Model

  18. Why was the LOG-LOG Model Chosen for 12.5% Gels • LOG-LOG Model worked best for the 12.5% Gels (Gel #4 VE + Tris Supernatant Time Course and Gel # 6 VE + Tris Pellet Time Course) • Small residuals • R^2 > .9 • Residuals did not have large sections of positive or negative.

  19. The Log-Log Model • The Log-Log model is of the form: Log(MW)=a+bLog(RM)+cLog(RM)^2 • It incorporates the Log model and the quadratic model to make a more successful madel.

  20. Predictions

  21. Conclusion Different models worked better on different on certain gel types. The Quadratic Regression Model on the 15% gel, SLIC Model for the gradient gel and the LOG-LOG Model worked best for 12.% gels. This process could be much improved if there was more data on the different gel types.

  22. Thank You Open for Questions…

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