Sub-atomic Weight Determination of Obscure Proteins for NASA/JPL PAIR Program August 24, 2001

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The Overall Goal. To decide atomic weight of obscure electrophoresis information. System to Achieve the Goal. Choose which model(s) worked the best on the questions. Measure separations of obscure guidelines with PhotoShop and SpotviewerDecide whether Spotviewer or Photoshop is the better measuring instrument. .

Presentation Transcript

Slide 1

Sub-atomic Weight Determination of Unknown Proteins for NASA/JPL PAIR Program August 24, 2001 Barbara Falkowski Falgun Patel Celia Smith

Slide 2

The Overall Goal To decide sub-atomic weight of obscure electrophoresis information

Slide 3

Method to Achieve the Goal Measure separations of obscure principles with PhotoShop and Spotviewer Decide whether Spotviewer or Photoshop is the better measuring device. Run models on standard proteins Decide which model(s) work the best for the norms Run model(s) on obscure proteins. Choose which model(s) worked the best on the questions

Slide 4

SpotViewer Disadvantages Did not quantify color front separation One expected to go into Photoshop to stamp or harvest the color front separation. Spotviewer missed groups Did not generally get groups that were thin, hazy or near one another. At times gave two estimation qualities to one band Or gave values that were connected with any band. Did not get light groups.

Slide 5

PhotoShop Advantages Did not require help from another program. Not as tedious Light groups could be all the more effectively recognized through shading reversal/control of the picture. This additionally functioned admirably with firmly pressed, thin and obscured groups.

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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 techniques (DTT and mechanically separated)

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Which show worked the best? No single model was best for the greater part of the gels. It was found that distinctive models worked better for various 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

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Why was the Quadratic Model decided for the Gel #1?

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Took Quadratic Regression of guidelines to discover the block and coefficients. Utilized the block and coefficients as a part of the condition: LOG MW = RM^2*a +RM*b +c Put the relative portability of the questions into the condition to concoct the accompanying results:

Slide 10

Log Molecular Weight Results for 15% Gel

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What sort of Cross Validation was finished? Quadratic Cross Validation utilizing relative portability and Log Molecular Weight Cross Validation was not picked at all The anticipated esteem for the missing band was not close the real esteem in any of the gel cases.

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Results for Cross Validation Model on Standards

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Why was the SLIC Model decided for the Gradient Gel #2 ? Remaining Sum = 0.00 Residual Squared Sum = 0.00 Largest R^2 = 0.99

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Why was the SLIC Model was decided for the Gel #2?

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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) )

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Log Molecular Weight Results for SLIC

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Graph aftereffect of SLIC Model

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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 substantial segments of positive or negative.

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The Log-Log Model The Log-Log model is of the shape: Log(MW)=a+bLog(RM)+cLog(RM)^2 It joins the Log show and the quadratic model to make a more fruitful madel.

Slide 21

Predictions

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Conclusion Different models worked better on various on certain gel sorts. The Quadratic Regression Model on the 15% gel, SLIC Model for the inclination gel and the LOG-LOG Model worked best for 12.% gels. This procedure could be tremendously enhanced if there was more information on the diverse gel sorts.

Slide 24

Thank You Open for Questions…

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