NASA-PAIR/ Proteomics Projects Crystal Austin Gerardo Lopez Elham Sarabi.

NASA-PAIR/ Proteomics NASA-PAIR/ Proteomics ProjectsProjects

Crystal Austin Gerardo LopezElham Sarabi

Interesting Info about Sea Interesting Info about Sea UrchinUrchin

• About 10 cm across and the spines are about 2 cm

• The spines are used for protection, movement and for trapping drifting algae

• They move surprisingly fast on their tube feet and spines

• They can also re-grow broken spines

• Used in public aquariums as an indicator for water quality

Habitat and Distribution:

• Live only in the ocean & can not survive in fresh water. They are found from the intertidal to the deep ocean.

• Besides the S. California Coast,

Location on the Food Chain

• Primary Food- Diet consists of algae, plankton, periwinkles, and mussels

• Predators- they are eaten by crabs, sunflower stars, snails, sea otters, some birds, fish and even people.

Reasons Why We Study the Vitelline Envelope…

• Location on egg?• What is the

vitelline envelope?

• Significance in fertilization

• Significance in research

Outline:

I. Question Being Asked

II. Data Collection

III. Choosing the Best Model

IV. Predicting the molecular weights of the unknown data sets

V. Results Obtained

Questions Being Asked?

*Does the Vitelline Envelopes polypeptides from two species of sea urchins have the same molecular weight?

* Using the mechanically isolated Vitelline Envelope in one species of sea urchin, are the two chemical methods giving the same results with respect to the number and size of polypeptides?

Purpose:

TO DETERMINE THE MOLECULAR WEIGHTS OF UNKNOWN PROTEIN BANDS IN A GEL BY USING STATISTICAL MODELS.

Comparing Strongylocentrotus Purpuratus to Lytechinus Pictus

OH OH | | HS-CH2-CH-CH-CH2-SH

•File Studied:

-S.purp, L.pictus, DTT treatment, 15% gel

•How DTT treatment works.

•Goal:

-Analyze file for comparisons.

Comparing Chemical Treatments w/ a Manual Method

•File Studied:

-“Sea urchin VE removal”

•Methods of isolating the polypeptides:

-DTT

-Alpha-amylase

Comparing Chemical w/ a Manual Cont...

-Manual (standard)

•Goal:

-Analyze file for comparison.

-Find the relationship between the two chemical treatments and the manual method.

Methods Used:

• Using the 15% gel sample and the 12% gel sample, we approximated the dye front for each based on the end of the gel readings.

• We then isolated each lane on a new sheet and read the cm migrated for each band. (In order to get the best result, we adjusted the brightness and contrast in adobe photoshop)

• For each lane we ran three trials, then averaged for best results.

• We recorded all data on excel for future calculations.

Methods Continued:

• Using the recorded dye front and measured values, we calculated the relative mobility by dividing the cm migrated by the centimeters to the dye front.

• Using the standard, we calculated the best fit model with linear, quadratic, cubic, 4th, and some non-standard functions

Modeling:

• Looking for the best fitting model:y = a+bx

y = a+bx+cx^2y

=a+bx+cx^2+dx^3 y = a+bx+cx^2+dx^3+ex^4

Nonlinear: y = a+bx+cLNx

• Choosing the best model

Standard at 15% and an attempt to find the best fitting model

4.200

4.400

4.600

4.800

5.000

5.200

5.400

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

rel mobility

logM

w

Standard 15%


y = -1.1722x + 5.2958

R2 = 0.9596

4.2

4.4

4.6

4.8

5

5.2

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

rel mobility

logM

w

Standard 15%

Linear


y = 0.9925x2 - 2.1297x + 5.4538

R2 = 0.9919

4.2

4.4

4.6

4.8

5

5.2

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

rel mobility

logM

w

Standard 15%

Quad


y = -1.7778x3 + 3.5737x2 - 3.1651x + 5.5571

R2 = 0.9972

4.2

4.4

4.6

4.8

5

5.2

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

rel mobility

logM

w

Standard 15%

Cube


y = 1.38x4 - 4.3534x3 + 5.1369x2 - 3.5098x + 5.579

R2 = 0.9973

4.2

4.4

4.6

4.8

5

5.2

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

rel mobility

logM

w

Standard 15%

4th


y = -1.1722x + 5.2958

R2 = 0.9596

y = 0.9925x2 - 2.1297x + 5.4538

R2 = 0.9919

y = -1.7778x3 + 3.5737x2 - 3.1651x + 5.5571

R2 = 0.9972

y = 1.38x4 - 4.3534x3 + 5.1369x2 - 3.5098x + 5.579

R2 = 0.9973

4.200

4.400

4.600

4.800

5.000

5.200

5.400

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

rel mobility

logM

w

Standard 15%

Linear

Quad

Cube

4th

Standardat 12% and an attempt to find the best fitting model

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2

5.3

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Rel Mobility

Log

Mw Standard 15%


y = -1.108x + 5.3369

R2 = 0.937

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2

5.3

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Rel Mobility

Log

Mw Standard 15%

Linear


y = 1.3719x2 - 2.4379x + 5.5759

R2 = 0.9978

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2

5.3

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Rel Mobility

Log

Mw Standard 15%

Quad


y = -1.3833x3 + 3.3271x2 - 3.2158x + 5.6591

R2 = 0.9997

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2

5.3

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Rel Mobility

Log

Mw Standard 15%

Cube


y = -2.5452x4 + 3.2261x3 + 0.5441x2 - 2.5697x + 5.6119

R2 = 0.9998

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2

5.3

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Rel Mobility

Log

Mw Standard 15%

4th


y = -1.108x + 5.3369

R2 = 0.937

y = 1.3719x2 - 2.4379x + 5.5759

R2 = 0.9978

y = -1.3833x3 + 3.3271x2 - 3.2158x + 5.6591

R2 = 0.9997

y = -2.5452x4 + 3.2261x3 + 0.5441x2 - 2.5697x + 5.6119

R2 = 0.9998

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2

5.3

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Rel Mobility

Log

Mw

Standard 15%

Linear

Quad

Cube

4th

Comparison of 15% & 12% Gel Sample

4.2

4.4

4.6

4.8

5

5.2

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Rel Mobility

log

Mw

15%,y=a+bx+clnx

Standard at 15%

12%,y=a+bx+clnx

Standard at 12%

G E L ID S T A N D A R D 1 5 % D a lt o n s lo g M w S T A N D A R D 1 2 %D a lt o n s lo g M w D y e F ro n t 4 . 9 9 6 6 6 7 2 0 0 0 0 0 5 . 3 0 1 0 3 0 . 1 2 6 9 4 3

2 0 0 0 0 0 5 . 3 0 1 0 3 c m b a n d 1 0 . 0 8 8 0 5 7 1 1 6 2 5 0 5 . 0 6 5 3 9 3 0 . 2 3 5 7 5 11 1 6 2 5 0 5 . 0 6 5 3 9 3 c m b a n d 2 0 . 2 0 0 1 4 3 9 7 4 0 0 4 . 9 8 8 5 5 9 0 . 2 8 4 9 7 4

9 7 4 0 0 4 . 9 8 8 5 5 9 c m b a n d 3 0 . 2 2 3 4 8 7 6 6 2 0 0 4 . 8 2 0 8 5 8 0 . 3 9 1 1 9 26 6 2 0 0 4 . 8 2 0 8 5 8 c m b a n d 4 0 . 3 2 3 5 5 6 4 5 0 0 0 4 . 6 5 3 2 1 3 0 . 5 6 9 9 4 84 5 0 0 0 4 . 6 5 3 2 1 3 c m b a n d 5 0 . 5 2 2 3 3 7 3 1 0 0 0 4 . 4 9 1 3 6 2 0 . 8 2 9 0 1 63 1 0 0 0 4 . 4 9 1 3 6 2 c m b a n d 6 0 . 6 7 1 7 8 42 1 5 0 0 4 . 3 3 2 4 3 8 c m b a n d 7 0 . 8 7 7 9 1 8

c m b a n d 8c m b a n d 9

E x p o n e n t 1 E x p o n e n t 2 0 . 0 8 8 0 5 7 -2 . 4 2 9 7 81 1 0 . 2 0 0 1 4 3 -1 . 6 0 8 7 2 E x p o n e n t 1 E x p o n e n t 2 0 . 1 2 6 9 4 3 -2 . 0 6 4 0 2

0 . 2 2 3 4 8 7 -1 . 4 9 8 4 1 1 0 . 2 3 5 7 5 1 -1 . 4 4 4 9 80 . 3 2 3 5 5 6 -1 . 1 2 8 3 8 0 . 2 8 4 9 7 4 -1 . 2 5 5 3 60 . 5 2 2 3 3 7 -0 . 6 4 9 4 4 0 . 3 9 1 1 9 2 -0 . 9 3 8 5 60 . 6 7 1 7 8 4 -0 . 3 9 7 8 2 0 . 5 6 9 9 4 8 -0 . 5 6 2 2 10 . 8 7 7 9 1 8 -0 . 1 3 0 2 0 . 8 2 9 0 1 6 -0 . 1 8 7 5 2

C o n s t a n t = a 4 . 6 9 7 8 6 8C o e ffx -1 = b -0 . 4 5 5 7 6 C o n s t a n t = a 4 . 5 2 2 6 1 4C o e ffx -2 = c -0 . 2 6 6 8 5 C o e ffx -1 = b -0 . 1 3 4 8 5

C o e ffx -2 = c -0 . 3 8 9 5 3

P re d V a lu e 1 5 . 3 0 6 1 1 2 P re d V a lu e 1 5 . 3 0 9 4 8 5P re d V a lu e 2 5 . 0 3 5 9 3 2 P re d V a lu e 2 5 . 0 5 3 6 8P re d V a lu e 3 4 . 9 9 5 8 5 4 P re d V a lu e 3 4 . 9 7 3 1 8P re d V a lu e 4 4 . 8 5 1 5 0 8 P re d V a lu e 4 4 . 8 3 5 4 5 5P re d V a lu e 5 4 . 6 3 3 1 0 7 P re d V a lu e 5 4 . 6 6 4 7 5 2P re d V a lu e 6 4 . 4 9 7 8 4 9 P re d V a lu e 6 4 . 4 8 3 8 6 4P re d V a lu e 7 4 . 3 3 2 4 8 9

R e s id u a l1 -0 . 0 0 8 4 6R e s id u a l2 0 . 0 1 1 7 1 3R e s id u a l3 0 . 0 1 5 3 7 9 0

R e s id u a l1 -0 . 0 0 5 0 8 R e s id u a l4 -0 . 0 1 4 6R e s id u a l2 0 . 0 2 9 4 6 1 R e s id u a l5 -0 . 0 1 1 5 4R e s id u a l3 -0 . 0 0 7 3 R e s id u a l6 0 . 0 0 7 4 9 8R e s id u a l4 -0 . 0 3 0 6 5 1 . 7 8 E -1 5R e s id u a l5 0 . 0 2 0 1 0 5 S u m S q u a re E rro r0 . 0 0 0 8 4 8R e s id u a l6 -0 . 0 0 6 4 9 D e g F re e d o m 3R e s id u a l7 -5 . 0 9 E -0 5 S t d D e v'n 0 . 0 1 6 8 0 9

R -S q u a re A d ju s t e d0 . 9 9 7 5 3 1R -S q u a re d 0 . 9 9 8 0 2 5t -va lu e 3 . 1 8 2 4 4 9

S u m S q u a re E rro r0 . 0 0 2 3 3 3 s t im e s t 0 . 0 5 3 4 9 4D e g F re e d o m 4S t d D e v'n 0 . 0 2 4 1 4 9R -S q u a re A d ju s t e d0 . 9 9 4 9 4 7R -S q u a re d 0 . 9 9 6 6 3 1t -va lu e 2 . 7 7 6 4 5 1s t im e s t 0 . 0 6 7 0 4 9

Values of the standard error for the Ln model

Comparison of DTT & Amylase to Manual Method

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Number of Polypeptides

LogM

w

Manual DTT Amylase

AMYLASE Rm Lane#3

LogUMw y=a+bx+cl

nxAntiLogU

MwAMYLASE Rm Lane#4

LogUMw y=a+bx+cl

nxAntiLogU

MwDTT Rm Lane#6

LogUMw y=a+bx+cl

nxAntiLogU

MwDTT Rm Lane#7

LogUMw y=a+bx+cl

nxAntiLogU

MwManuel Lane #9

LogUMw y=a+bx+cl

nxAntiLogU

MwManuel Lane #10

LogUMw y=a+bx+cl

nxAntiLogU

Mw0.0585366 5.620236 417096.1 0.1536585 5.231485 170405.9 0.15051 5.239973 173769.3 0.147287 5.248841 177353.9 0.089922 5.448782 281048.8 0.091473 5.441914 276639.30.1560976 5.225021 167888.6 0.2512195 5.02684 106375 0.193878 5.135499 136615.1 0.185271 5.154346 142674.3 0.124806 5.316385 207197.9 0.117054 5.342409 2199930.2146341 5.093082 123902.9 0.3658537 4.864956 73274.99 0.255102 5.020342 104795.4 0.220155 5.082444 120905 0.154264 5.229872 169774.4 0.149612 5.242425 1747530.2560976 5.018691 104397.6 0.4707317 4.752631 56575.83 0.280612 4.979776 95450.06 0.237984 5.049706 112125.9 0.200000 5.122562 132605.7 0.168992 5.192365 155727.40.2829268 4.976264 94681.32 0.502439 4.722964 52840.09 0.352041 4.88181 76174.54 0.262791 5.007739 101797.8 0.248062 5.032192 107694.2 0.199225 5.12418 133100.50.3609756 4.870842 74274.92 0.5487805 4.682349 48122.56 0.40051 4.825028 66838.69 0.280620 4.979764 95447.41 0.306202 4.942332 87565.21 0.24031 5.045605 1110720.4292683 4.794139 62249.95 0.5878049 4.650327 44702.02 0.423469 4.800219 63127.54 0.324031 4.917882 82771.68 0.319380 4.924141 83973.22 0.306202 4.942332 87565.210.4707317 4.752631 56575.83 0.6243902 4.621874 41867.19 0.469388 4.753926 56744.77 0.344961 4.890678 77745.95 0.345736 4.889699 77570.91 0.33876 4.89858 79173.620.497561 4.727422 53385.29 0.7853659 4.510819 32420.44 0.492347 4.732228 53979.42 0.368992 4.861205 72644.9 0.387597 4.839535 69109.12 0.374419 4.854787 71579.18

0.5463415 4.684413 48351.81 0.9097561 4.436774 27338.46 0.512755 4.713656 51719.66 0.389922 4.836892 68689.7 0.417829 4.806202 64003.27 0.412403 4.812026 64867.30.5878049 4.650327 44702.02 0.540816 4.689117 48878.41 0.417054 4.80703 64125.4 0.463566 4.759572 57487.37 0.429457 4.793942 62221.720.6658537 4.591238 39015.58 0.589286 4.649147 44580.75 0.433333 4.789919 61648.07 0.488372 4.735922 54440.45 0.504651 4.720954 52596.150.7756098 4.517004 32885.44 0.686224 4.576753 37735.73 0.452713 4.770264 58920.14 0.517829 4.709135 51184.15 0.55814 4.6745 47260.640.8926829 4.446456 27954.77 0.795918 4.504197 31929.85 0.473643 4.749836 56212.94 0.541860 4.688225 48778.11 0.602326 4.638863 43537.49

0.84949 4.471599 29620.97 0.499225 4.725897 53198.19 0.571318 4.663632 46092.71 0.686047 4.576878 37746.60.545736 4.684926 48408.98 0.616279 4.628061 42467.91 0.717829 4.554952 35888.190.682946 4.57906 37936.78 0.661240 4.594568 39315.92 0.764341 4.524224 33436.760.724031 4.550764 35543.85 0.696124 4.569839 37139.72 0.803876 4.499249 31568.120.779845 4.514311 32682.21 0.735659 4.54299 34913.26 0.84031 4.477069 29996.420.845736 4.47383 29773.53 0.783721 4.511857 32498.06

0.815504 4.492087 31051.790.851938 4.470148 29522.17

Comparison and eleimination of possible human error

Manuel Lane #9

Manuel Lane #10 Average Rel mobility

DTT RmLane#6

DTT RmLane#7 Average Rel mobility

AMYLASE RmLane#3

AMLASE RmLane#4 Average Rel mobility

5.448782 5.441914 5.445348 0.09069767 5.239973 5.248841 5.2444069 0.14889851 5.2250213 5.231485 5.228253 0.1548785.316385 5.342409 5.329397 0.12093023 5.135499 5.154346 5.1449221 0.18957443 5.0186907 5.02684 5.022765 0.25365855.229872 5.242425 5.236148 0.15193798 5.020342 5.049706 5.0350241 0.24654327 4.8708422 4.864956 4.867899 0.36341465.122562 5.12418 5.123371 0.1996124 4.979776 4.979764 4.9797702 0.2806162 4.7526309 4.752631 4.752631 0.47073175.032192 5.045605 5.038898 0.24418605 4.88181 4.890678 4.8862438 0.34850103 4.7274216 4.722964 4.725193 0.54.942332 4.942332 4.942332 0.30620155 4.825028 4.836892 4.8309598 0.39521634 4.6844127 4.682349 4.683381 0.5475614.889699 4.89858 4.89414 0.34224806 4.800219 4.80703 4.8036245 0.42026183 4.6503271 4.650327 4.650327 0.58780494.839535 4.854787 4.847161 0.38100775 4.753926 4.749836 4.7518811 0.47151558 4.5170037 4.510819 4.513911 0.78048784.806202 4.812026 4.809114 0.41511628 4.732228 4.725897 4.7290625 0.49578587 4.446456 4.436774 4.441615 0.90121954.759572 4.793942 4.776757 0.44651163 4.689117 4.684926 4.6870215 0.543276384.735922 4.720954 4.728438 0.49651163 4.576753 4.57906 4.5779066 0.684585114.688225 4.6745 4.681362 0.55 4.504197 4.514311 4.5092541 0.787881664.628061 4.638863 4.633462 0.60930233 4.471599 4.47383 4.4727148 0.847613124.569839 4.576878 4.573358 0.691085274.54299 4.554952 4.548971 0.72674419

4.511857 4.524224 4.518041 0.774031014.492087 4.499249 4.495668 0.809689924.470148 4.477069 4.473609 0.84612403

Comparison of the data within the standard error

DTT & AMYLASE Vs. MANUAL

4.4

4.6

4.8

5

5.2

5.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Rel Mobility

Log

Mw

MANUAL DTT AMYLASE

In Concluson...

The 12% gel sample contain less errors in fitting a model, than the 15% gel sample.

After choosing a nonlinear standard model, it was found that the natural log yeilded the smallest standard deviation.

It also maximized the degrees of freedom overall, allowing us to have a more normal distribution.

References

• http://www.yorvic.york.ac.uk/projects/2/2.2.3.1.htm

• http://www.sidwell.edu/sidwell.resources/bio/VirtualLB/sea.html

• http://stanford.edu/group/Urchin/nathistory.html

• http://www.wcaslab.com/tech/Dithiothreitol.htm

• Proteomics: Dr. Edward J. Carroll, JR.• Data Analysis: Dr. Larry Clevenson

THANK YOU!!!!!!!

We Thank Dr. Carroll, Dr. Clevenson, Dr. Shubin, Vred, and our fellow students for a great CSUN/JPL- PAIR Program!

THE END!

NASA-PAIR/ Proteomics Projects Crystal Austin Gerardo Lopez Elham Sarabi.

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Transcript of NASA-PAIR/ Proteomics Projects Crystal Austin Gerardo Lopez Elham Sarabi.