GPS Manual - gps.biocuckoo.orggps.biocuckoo.org/download/GPS_3.0_Manual_2014.pdf · GPS Manual 1 Index ... XXXXXXXX >protein2 XXXXXXXXXXXXXXXX ... Choose one or more kinases from

GPS Manual Group-based Prediction System

Version 3.0

20/12/2014

Author: Yongbo Wang, Han Cheng, Zhicheng Pan, Zexian Liu* and Yu Xue

*

Contact: Yongbo Wang, [email protected], Yu Xue, [email protected]

The software is only free for academic research.

The latest version of GPS software is available from http://gps.biocuckoo.org/down.php

Copyright (c) 2004-2014. The CUCKOO Workgroup. All right reserved.

mailto:[email protected]

mailto:[email protected]

http://gps.biocuckoo.org/down.php

GPS Manual

1

Index

INDEX ..................................................................................................................................................... 1

STATEMENT.......................................................................................................................................... 2

INTRODUCTION .................................................................................................................................. 3

DOWNLOAD & INSTALLATION ...................................................................................................... 4

PREDICTION OF KINASE-SPECIFIC PHOSPHORYLATION SITES ........................................ 6

DIRECT PREDICTION .............................................................................................................................. 6

BATCH PREDICTION ............................................................................................................................... 8

PREDICTION OF SPECIES-SPECIFIC KINASE-SPECIFIC PHOSPHORYLATION SITES . 12

DIRECT PREDICTION ............................................................................................................................ 12

BATCH PREDICTION ............................................................................................................................. 16

PREDICTION OF DUL-SPECIFIC KINASE-SPECIFIC PHOSPHORYLATION SITES ......... 19

DIRECT PREDICTION ............................................................................................................................ 19

BATCH PREDICTION ............................................................................................................................. 22

ALGORITHMS AND PREDICTION PERFORMANCE ................................................................ 25

ALGORITHM DESIGN ........................................................................................................................... 25

EVALUATION OF PREDICTION PERFORMANCES ................................................................................... 28

ESTIMATION OF FALSE POSITIVE PREDICTIONS AND CUT-OFF SETTING .................... 31

EXISTED RESOURCES AND ONLINE SERVICES FOR PHOSPHORYLATION .................... 33

REFERENCES ..................................................................................................................................... 34

RELEASE NOTE ................................................................................................................................. 35

GPS Manual

2

Statement

1. Implementation. The softwares of the CUCKOO Workgroup are implemented in

JAVA (J2SE). Usually, both of online service and local stand-alone packages will be

provided.

2. Availability. Our softwares are freely available for academic researches. For

non-profit users, you can copy, distribute and use the softwares for your scientific

studies. Our softwares are not free for commercial usage.

3. GPS. Previously, we used the GPS to denote our Group-based Phosphorylation

Scoring algorithm. Currently, we are developing an integrated computational platform

for post-translational modifications (PTMs) of proteins. We re-denote the GPS as

Group-based Prediction Systems. This software is an indispensable part of GPS.

4. Usage. Our softwares are designed in an easy-to-use manner. Also, we invite you

to read the manual before using the softwares.

5. Updation. Our softwares will be updated routinely based on users’ suggestions and

advices. Thus, your feedback is greatly important for our future updation. Please do

not hesitate to contact with us if you have any concerns.

6. Citation. Usually, the latest published articles will be shown on the software

websites. We wish you could cite the article if the software has been helpful for your

work.

7. Acknowledgements. The work of CUCKOO Workgroup is supported by grants

from National Basic Research Program (973 project) [2012CB910101,

2013CB933903, and 2012FY112900], Natural Science Foundation of China

[31171263 and 81272578]; International Science & Technology Cooperation Program

of China [0S2013ZR0003] and China Postdoctoral Science Foundation

[2014M550392].

GPS Manual

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Introduction

Identification of phosphorylation sites with their cognate protein kinases (PKs) is the

foundation for understanding the functional dynamics and plasticity of various

cellular processes. Although nearly 10 kinase-specific predictors were developed,

numerous PKs were casually classified into sub-groups without a standard rule. And

for large-scale predictions, the false positive rate (FPR) was also never addressed.

Here we adopted a well-established rule to classify PKs with their verified sites into a

hierarchical structure with four levels, including group, family, subfamily and single

PK 1. Then we constructed the GPS (Group-based Prediction System, ver 2.0)

software, with a modified version of GPS (Group-based Phosphorylation Scoring)

algorithm 2,3

. As the first stand-alone software for computational phosphorylation,

GPS 2.0 was implemented in JAVA and could predict kinase-specific phosphorylation

sites for 408 human PKs in hierarchy.

Currently, as more and more phosphorylation sites have been experimentally validated,

a more accurate and reliable kinases-specific phosphorylation sites prediction tool is

in urgent need. At the same time, several advanced GPS algorithm have been

developed and achieved higher performance, including GPS 2.1 and GPS 2.2. To

improve the performance of phosphorylation sites prediction, we applied the GPS 2.2

algorithm and enlarged the training set. In addition, we included the phosphorylation

sites prediction in 84 species. For dul-specific kinases, we also provided the

prediction for these kinases. Finally, we constructed the GPS (Group-based

Prediction System, ver 3.0) software.

Group-based Prediction System v3.0 User Interface

GPS Manual

4

Download & Installation

The software of GPS 3.0 was implemented in JAVA, and could be installed on

Windows systems. GPS 3.0 distributions for Windows can be found at

http://gps.biocuckoo.org/down.php. We recommend that users could download the

latest release.

After downloading, please double-click on the file GPS_3.0_windows.exe to begin

installation. Follow the user prompts through the installation. And snapshots of the

setup program are shown below:

http://gps.biocuckoo.org/down.php

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5

Click on the Finish button to complete the setup program.

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6

Prediction of Kinase-specific Phosphorylation

Sites

Direct Prediction

For convenience, the GPS 3.0 allows users to input their protein sequences into the

“TEXT form” for prediction. One or more protein sequences should be prepared in

FATSA format as below:

>protein1

XXXXXXXXXXXXX

XXXXXXXX

>protein2

XXXXXXXXXXXXXXXX…

>protein3

XXXXXXXXXXXX

Please note: All irregular words, including non-amino acid word (eg, number) and blank,

will be removed automatically. As an instance, we put rat spinophilin protein sequence

as an example for GPS 3.0. Users could click on the “Example” button to access the

example.

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7

Choose one or more kinases from the Kinase Hierarchy Tree

Choose a Threshold what you need, default is Medium.

Click on the Submit button, then the predicted phosphorylation sites will be shown.

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8

Batch Prediction

We also provide an alternative approach for processing multiple protein sequences. If the

file is large, the Batch Predictor will be convenient for users.

The following steps show you how to use it:

Put protein sequences into a file with FATSA format as below:

>protein1

XXXXXXXXXXXXX

XXXXXXXX

>protein2

XXXXXXXXXXXXXXXX…

>protein3

XXXXXXXXXXXX

The names of proteins are necessary (the line with “>” and a protein name/accession

number).

To run the Batch Predictor just select the Batch Predictor option in the Tools menu.

GPS Manual

9

Click on the Add File button and add one or more protein sequence files in your hard

disk.

The name of added files will be shown in the Sequence File List

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10

The output directory of prediction results should also be defined. Please click on the

“>>” button to specify the export file fold.

5. Choose one or more kinases from Kinase Hierarchy Tree, and then pick a Threshold,

click on the Submit button, then the Batch Predictor will begin to process all of the

GPS Manual

11

sequence files that have been added to the list. The results of predictions will be exported

to the Prediction Export Fold, and the name of result files will be shown in the

Prediction File List.

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12

Prediction of Species-specific Kinase-specific

Phosphorylation Sites

Direct Prediction

An advance feature of GPS 3.0 is Prediction of Species-specific Kinase-specific

Phosphorylation Sites. Users can predict phosphorylation sites in different species. Since

there isn’t a standard principle classification for all animals and plants, we applied the

kinases classification for eukaryotes from a new published database – EKPD, which is a

eukaryotic protein kinases and protein phosphatases database. Kinases and phosphatases

in 84 species are classified in a hierarchy structure, including group, family and single

protein.

To predict phosphorylation sites in 84 species, users could open the species-specific

prediction interface by clicking the item “GPS 3.0 – Species Specific” item in “Tool”

menu.

Similar with the classical version, this prediction interface also allows users to input their

protein sequences into the “TEXT form” for prediction. One or more protein sequences

should be prepared in FATSA format as below:

GPS Manual

13

>protein1

XXXXXXXXXXXXX

XXXXXXXX

>protein2

XXXXXXXXXXXXXXXX…

>protein3

XXXXXXXXXXXX




example.

Choose a species from the Organism List.

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14


Choose a Threshold what you need, default is Medium

GPS Manual

15

Click on the Submit button, then the predicted phosphorylation sites will be shown.

GPS Manual

16

Batch Prediction

In GPS 3.0 – Species Specific prediction interface, we also provide an alternative

approach for processing multiple protein sequences. If the file is large, the Batch

Predictor will be convenient for users.



>protein1

XXXXXXXXXXXXX

XXXXXXXX

>protein2

XXXXXXXXXXXXXXXX…

>protein3

XXXXXXXXXXXX


number).



GPS Manual

17

disk.


The output directory of prediction results should also be defined. Please click on the “>>”

button to specify the export file fold.

GPS Manual

18






GPS Manual

19

Prediction of Dul-Specific kinase-specific

phosphorylation sites

Direct Prediction

A lot of kinases have been found that are responsible for more than one phosphorylated

residues. In GPS 3.0, we also collected several kinases which can both catalyze Ser/Thr

and Tyr sites. We picked out these special phosphorylation sites and kinases, and

provided a standalone prediction interface.

To predict phosphorylation sites in dul-specific kinases, users could open the

species-specific prediction interface by clicking the item “GPS 3.0 – Dul Specific” item

in “Tool” menu.

Similar with the classical version, this prediction interface also allows users to input their

protein sequences into the “TEXT form” for prediction. One or more protein sequences

should be prepared in FATSA format as below:

>protein1

XXXXXXXXXXXXX

XXXXXXXX

>protein2

XXXXXXXXXXXXXXXX…

>protein3

XXXXXXXXXXXX

GPS Manual

20




example.


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21

Choose a Threshold what you need, default is Medium.

Click on the Submit button, then the predicted phosphorylation sites will be shown

GPS Manual

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Batch Prediction

We also provide an alternative approach for processing multiple protein sequences. If the

file is large, the Batch Predictor will be convenient for users.



>protein1

XXXXXXXXXXXXX

XXXXXXXX

>protein2

XXXXXXXXXXXXXXXX…

>protein3

XXXXXXXXXXXX


number).



disk.

GPS Manual

23


The output directory of prediction results should also be defined. Please click on the “>>”

button to specify the export file fold.

GPS Manual

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25

Algorithms and Prediction Performance

Algorithm Design

To predict kinase-specific phosphorylation sites, we employed our previous GPS

(Group-based Phosphorylation Scoring) method with improvement 2,3

. The basic

hypothesis of GPS algorithm is that if two short peptides are highly similar, they might

also bear similar 3D structures and biochemical properties. Then we used the amino acid

substitution matrix BLOSUM62 to calculate the similarity between two PSP (7, 7)

peptides.

As previously described 2,3

, for two amino acids a and b, let the substitution score

between them in BLOSUM62 be Score(a, b). The similarity between two PSP (7, 7)

peptides (15aa) A and B is defined as:

151

])[],[(),(i

iBiAScoreBAS

If S(A, B)<0, we simply redefine it as S(A, B)=0.

Given a putative PSP (7, 7) peptide, it will be compared with all known sites pairwisely

to calculate the substitution scores, separately. The average value of the substitution

scores is computed as the final prediction score of the given site. Given a putative PSP

(7,7) peptide, we can calculate its GPS score. Then we can judge whether the given site is

a potentially real phosphorylation site, under different thresholds.

In previous versions (GPS 1.0 and 1.10), we hypothesized that the bona fide pattern for

PK recognition and modification might be compromised by heterogeneity of multiple

structural determinants with different features. Then all known phosphorylation sites are

automatically partitioned into several clusters with the Markov Cluster Algorithm (MCL

for short) to improve the prediction performance 2,3

. However, only ~11% of the PK

groups (8 out of 71) could be divided into more than one cluster with enhanced

performances. Thus, the clustering method was not used in GPS 2.0.

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26

To improve the robustness of the prediction system globally without influencing the

prediction performance significantly, we employed an alternative approach of matrix

mutation.

Firstly, the BLOSUM62 was chosen as the initial matrix. The performance (Sn and Sp) of

leave-one-out validation for each PK groups was calculated. Then we fixed Sp as 90% to

improve Sn by matrix mutation. Although matrix mutation in other types was also valid,

the method we used in this article could improve the robustness significantly.

In GPS 3.0, we adopted a GPS 2.2 algorithm to try to improve the performance. The

peptide selection and weight training were introduced in our training procedure before

Matrix mutation (MaM).

1) Motif length selection (MLS). In this step, the optimized combination of NSP(m, n)

was determined for better performance. The combinations of NSP(m, n) (m = 1,. . .,

30; n = 1,. . ., 30) were extensively tested, while the optimal NSP(m, n) for each

cluster with the highest leave-one-out performance was respectively selected. We

fixed the Sp at 95% to compare Sn values.

2) Weight training (WT). We updated the substitution score between two NSP(m, n)

peptides A and B as:

nimi iBiAScorewBAS ])[],[(),('

The wi is the weight of position i. Again, if S0(A, B) o 0, we simply redefined it as

S0(A, B)=0. Initially, the w was defined as 1 for each position. We randomly picked

out the weight of any position for +1 or -1, and adopted the manipulation if the Sn

value of the re-computed leave-one-out result with the Sp fixed at 95% was increased.

The following pseudocode show you how to get a mutation matrix：

Initialize matrix with BLOSUM62

Set the default mutation times to 0

While mutation times less than 10000

Pick an element of matrix at random to mutate

Increase or decrease the value of the element

Calculate score of Leave-one-out with the mutated matrix

If the score increase

Keep the forward mutation

Else

Give up the mutation

Endif

Score the number of mutation times

Endwhile

Return the mutated matrix

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27

The process was repeated until convergence was reached. The following training

procedure is Matrix mutation, which is same with that in GPS 2.0.

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28

Evaluation of Prediction Performances

Performance measurements

To evaluate the prediction performances, four standard measurements were used,

including accuracy (Ac), sensitivity (Sn), specificity (Sp) and Mathew correlation

coefficient (MCC). Accuracy (Ac) represents the correct ratio between both positive (+)

and negative (-) data sets, while sensitivity (Sn) and specificity (Sp) illustrate the correct

prediction ratios of positive (+) and negative data (-) sets respectively. Since the number

of positive data and negative data differed too much from each other, the Mathew

correlation coefficient (MCC) was also included. The value of MCC ranges from -1 to 1,

and a larger MCC value stands for better prediction performance.

Among the data with positive hits by GPS 3.0, the real positives were defined as true

positives (TP), while the others were defined as false positives (FP). Among the data with

negative predictions, the real positives were defined as false negatives (FN), while the

others were defined as true negatives (TN). The four measurements of sensitivity (Sn),

specificity (Sp), accuracy (Ac), and Mathew correlation coefficient (MCC) were defined

as below:

FNTP

TPSn

,

FPTN

TNSp

,

FNTNFPTP

TNTPAc

, and

)()()()(

)()(

FNTNFPTPFPTNFNTP

FPFNTNTPMCC

.

The self-consistency, leave-one-out validation and n-fold cross-validation

The self-consistency used the training positive data and negative data directly to evaluate

the prediction performance, and represented the computational power of the prediction

system. However, the robustness and stability of the software should also be evaluated by

leave-one-out validation and n-fold cross-validation. In the leave-one-out validation,

which is also called as Jack-Knife validation, each sites in the dataset was picked out in

turn as an independent test sample, and all the remaining sites were regarded as training

data. This process was repeated until each site was used as test data one time. In n-fold

cross-validation all the (+) sites and (-) sites were combined and then divided equally

into n parts, keeping the same distribution of (+) and (-) sites in each part. Then n-1 parts

were merged into a training data set while the remanent part was taken as a testing data

set. This process was repeated twenty times and the average performance of n-fold cross-

validation was used to estimate the performance. In this work, the performances of

self-consistency and leave-one-out validation were calculated for all PK groups. And the

GPS Manual

29

4-, 6-, 8-, 10-fold cross-validations were performed for 70 PK groups without less than

30 sites. All self-consistency performances of GPS 3.0 have been included in

Performance option in the Tools menu.

Accessing of prediction performances of GPS 3.0 with related information

To check the prediction performance of the 464 human PKs just select the Performance

option in the Tools menu.

Choose a kinase what you want check from the Kinase Hierarchy Tree.

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Then Kinase Information and Prediction Performance of the kinase are shown in the

tables.

If you want get more kinase information, you can click on the hyperlinks in the table.

The hyperlinks will access the UniProt database and show you the detailed information

of the kinases.

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Estimation of False Positive Predictions and

Cut-off setting

Estimation and control of false positive prediction is the key point in large-scale

predictions of kinase-specific phosphorylation sites. The false positive rate (FPR) is the

proportion of negative sites that are erroneously predicted as positive hits. From our

analysis, the real phosphorylation sites were only a very small part of all S/T residues in

proteins. For 144 serine/threonine PK groups, the positive sites vs. the negative sites was

from 1:13.2 (Other/PEK, 16 positive sites and 211 negative sites) to 1:141.2

(CAMK/CAMK1/CAMK4, 9 positive sites with 1271 negative sites), with the average

number of 49.0. And for 69 tyrosine PK groups, the positive sites vs. the negative sites

was from 1:1.6 (TK/Trk/TRKA, 5 positive sites with 8 negative sites) to 1:28.2 (TK/Csk,

5 positive sites and 141 negative sites), with the average number of 9.7. Thus, even a very

small FPR will generate too many false positive hits.

Given a data set containing all of non-phosphorylation sites, the real FPR could be easily

computed. However, precise calculation of FPR is unavailable due to lack of a

“gold-standard” negative data set. In order to estimate the false positive rate (FPR), we

tried to construct a near-negative data set by several approaches. The first method was to

generate PSP(30,30) peptides randomly. However, the frequencies of the twenty amino

acids are not equal in eukaryotes. Thus, the method was not used since it could not reflect

the real distributions of PSP(30,30) peptides in proteomes. Also, the negative sites could

also be randomly retrieved from eukaryotic proteomes. However, this method need a

large sequence file to retrieve PSP(30,30) peptides with slow speed. In this article, we

chose a simple and fast method to construct the near-negative data set. Firstly, we

calculated the distributions of amino acids composition in six organisms, including S.

cerevisiae, S. pombe, C. elegans, D. melanogaster, M. musculus, and H. sapiens. Then

we randomly generated PSP(30,30) peptides based on the real frequencies of twenty

amino acids. And FPR values based on the latter two methods were very similar. By this

method, we randomly generated 10,000 PSP(30,30) peptides and used GPS 3.0 to

estimate the theoretically maximal FPR. The process was repeated twenty times and the

average value was calculated as the final FPR. Then for large-scale predictions, we

defined the precision (Pr) as below:

.Pr

*.PrPr

e

FPRNe

Here, N is the number of sites (S/T or Y) for prediction; Pre. is the number of predicted

sites by GPS 3.0. Since the FPR is the theoretically maximal false positive hits, the

precision (Pr) is the minimal proportion of correct predictions.

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32

Threshold setting was also a difficult problem. By experiences, we and others chose

different thresholds for every PK groups2-12

. Here we proposed that a uniform rule to

choose cut-off values based on calculated FPRs. For serine/threonine kinases, the high,

medium and low thresholds were established with FPRs of 2%, 6% and 10%. And for

tyrosine kinases, the high, medium and low thresholds were selected with FPRs of 4%,

9% and 15%. The high threshold was validated by a large-scale prediction of mammalian

phosphorylation sites, with satisfying performance. And the medium threshold relaxed

the stringency to be useful in small-scale experiments. Also, the low threshold reduced

the Sp to improve Sn considerably to be useful in exhaustively experimental identifying

all potential phosphorylation sites in substrates.

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Existed resources and online services for

phosphorylation

Predictors Web Link Ref.

Phosphorylation Databases

Phospho.ELM http://phospho.elm.eu.org/ 13

PhosphoSite http://www.phosphosite.org/ 14

Online Services and tools

<1> For General phosphorylation sites prediction

DISPHOS http://core.ist.temple.edu/pred/ 15

NetPhos 2.0 http://www.cbs.dtu.dk/services/NetPhos/ 16

NetPhosYeast http://www.cbs.dtu.dk/services/NetPhosYeast/ 17

GANNPhos not available 18

<2> For kinase-specific sites prediction

GPS 1.10 http://bioinformatics.lcd-ustc.org/gps_web 2,3

PPSP http://bioinformatics.lcd-ustc.org/PPSP/ 4

NetPhosK 1.0 http://www.cbs.dtu.dk/services/NetPhosK/ 5

ScanSite http://scansite.mit.edu/ 6

KinasePhos 1.0 http://kinasephos.mbc.nctu.edu.tw/ 7

KinasePhos 2.0 http://kinasephos2.mbc.nctu.edu.tw/ 12

PredPhospho http://pred.ngri.re.kr/PredPhospho.htm 8

PREDIKIN http://florey.biosci.uq.edu.au/kinsub/home.htm 9

PhoScan http://bioinfo.au.tsinghua.edu.cn/phoscan/ 10

pkaPS http://mendel.imp.ac.at/sat/pkaPS/ 11

<3> For construction of phosphorylation network

NetworKIN http://networkin.info/ 19

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34

References

1. G. Manning, D. B. Whyte, R. Martinez et al., Science 298 (5600), 1912 (2002).

2. Y. Xue, F. Zhou, M. Zhu et al., Nucleic Acids Res 33 (Web Server issue), W184 (2005).

3. F. F. Zhou, Y. Xue, G. L. Chen et al., Biochem Biophys Res Commun 325 (4), 1443 (2004).

4. Y. Xue, A. Li, L. Wang et al., BMC Bioinformatics 7, 163 (2006).

5. N. Blom, T. Sicheritz-Ponten, R. Gupta et al., Proteomics 4 (6), 1633 (2004).

6. J. C. Obenauer, L. C. Cantley, and M. B. Yaffe, Nucleic Acids Res 31 (13), 3635 (2003).

7. H. D. Huang, T. Y. Lee, S. W. Tzeng et al., Nucleic Acids Res 33 (Web Server issue), W226

(2005).

8. J. H. Kim, J. Lee, B. Oh et al., Bioinformatics 20 (17), 3179 (2004).

9. R. I. Brinkworth, R. A. Breinl, and B. Kobe, Proc Natl Acad Sci U S A 100 (1), 74 (2003).

10. T. Li, F. Li, and X. Zhang, Proteins (2007).

11. G. Neuberger, G. Schneider, and F. Eisenhaber, Biology direct 2, 1 (2007).

12. Y. H. Wong, T. Y. Lee, H. K. Liang et al., Nucleic Acids Res 35 (Web Server issue), W588 (2007).

13. F. Diella, S. Cameron, C. Gemund et al., BMC Bioinformatics 5 (1), 79 (2004).

14. P. V. Hornbeck, I. Chabra, J. M. Kornhauser et al., Proteomics 4 (6), 1551 (2004).

15. L. M. Iakoucheva, P. Radivojac, C. J. Brown et al., Nucleic Acids Res 32 (3), 1037 (2004).

16. N. Blom, S. Gammeltoft, and S. Brunak, J Mol Biol 294 (5), 1351 (1999).

17. C. R. Ingrell, M. L. Miller, O. N. Jensen et al., Bioinformatics 23 (7), 895 (2007).

18. Y. R. Tang, Y. Z. Chen, C. A. Canchaya et al., Protein Eng Des Sel 20 (8), 405 (2007).

19. R. Linding, L. J. Jensen, G. J. Ostheimer et al., Cell 129 (7), 1415 (2007).

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35

Release Note

1. Jan. 1st, 2008, the online service and the local stand-alone packages of GPS 2.0 were

released. The stand-alone software of GPS 2.0 could support Windows Operating

Systems.

2. Jan. 29th, 2008, a bug was found that the version 2.0 couldn’t be used under

non-English Operating Systems. We fixed the bug and released the version 2.0.1 beta

version. We thank Dr. Miguel Angel Sanchez (Malaga, Spain) and Dr. Gilles Vachon

(Universite J. Fourier, France) to send us feedbacks.

3. Apr. 13th, 2008, The GPS 2.0.1 was released, with online service and local packages,

to support three major Operating Systems, including Windows, Linux/Unix and Mac.

Also, the GPS 2.0.1 manual was updated and included in the packages.

Documents

GPS Manual - gps.biocuckoo.orggps.biocuckoo.org/download/GPS_3.0_Manual_2014.pdf · GPS Manual 1 Index ... XXXXXXXX >protein2 XXXXXXXXXXXXXXXX ... Choose one or more kinases from