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1樓 2013-06-28 00:05:32

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★ ★ ★
感謝參與，應(yīng)助指數(shù) +1
wizardfan: 金幣+2, 謝謝參與。不過你的第一段描述更接近于BLAST算法，而不是PSM 2013-06-28 05:39:19
夢在農(nóng)大: 金幣+1, ★有幫助, 謝謝你水平太高了 2013-06-28 23:20:35

你的圖我看不太清楚但是知道是在擬南芥的表達(dá)的蛋白組數(shù)據(jù)庫中搜索目的蛋白質(zhì)的最高分值的匹配信息。

我從和你的文字?jǐn)⑹鑫乙呀?jīng)知道大概怎么回事了，大概就是用MALDI-TOF-MS測定出多肽段或者序列比對是將同源蛋白質(zhì)或者基因序列位點(diǎn)上的匹配位點(diǎn)（相同或者相似殘基）與不匹配位點(diǎn)（不相似殘基）按照一定的記分規(guī)則轉(zhuǎn)化為序列間相似性或者差異性的數(shù)值來加以比較，相似性最大的比對結(jié)果具有最多的匹配位點(diǎn)，從數(shù)學(xué)上講，應(yīng)該是最優(yōu)的比對結(jié)果；但是從數(shù)學(xué)模型或算法得出的最優(yōu)結(jié)果在多大程度上反映了序列之間的相似性以及它們的生物學(xué)特征之間的關(guān)系，將取決于將生物學(xué)問題簡化成數(shù)學(xué)問題的過程，而這一過程也是生物信息處理最難解決的問題。

你給出的文章的作者是分別用質(zhì)譜測定了一系列的未知蛋白質(zhì)（34-53種蛋白質(zhì)）的序列的某些有限酶解肽段或者全蛋白質(zhì)的分子量（你的材料沒說質(zhì)譜測定的斑點(diǎn)回收物是什么以及怎么測定，我是按照一般用質(zhì)譜確定蛋白質(zhì)的方法猜測的）可能是用有限酶水解制備的多肽片段，從蛋白質(zhì)數(shù)據(jù)庫中搜索蛋白質(zhì)完全沒有進(jìn)行蛋白質(zhì)的全序列測定-----除非是數(shù)據(jù)庫中沒有的全新蛋白質(zhì)！“The 53 variable spots were analyzed by MALDI-TOF-MS.”沒有上下文，我不知道此句話的確切的意思，這句話可能說：質(zhì)譜之前的雙向電泳的凝膠上的蛋白質(zhì)斑（回收的蛋白質(zhì)斑的樣品用于MALDI-TOF-MS）有34個(gè)在擬南芥的表達(dá)的蛋白質(zhì)數(shù)據(jù)庫中能夠?qū)ふ移ヅ涠葮O高的信息-----就是大概確定34個(gè)已知蛋白質(zhì)。

你給出的文章的確定基因的原理是：根據(jù)MALDI-TOF-MS測定的一組蛋白質(zhì)（從你的文章內(nèi)容可知：共有53個(gè)電泳膠上的斑塊回收物進(jìn)行了MALDI-TOF-MS測定）的特征數(shù)據(jù)，在擬南芥的表達(dá)的蛋白質(zhì)數(shù)據(jù)庫中尋找匹配信息，以便確定MALDI-TOF-MS的數(shù)據(jù)的蛋白質(zhì)歸屬。如此一旦確定目的蛋白質(zhì)，那么根據(jù)蛋白質(zhì)的序列，然后反推成核苷酸序列，再到進(jìn)行基因數(shù)據(jù)庫的序列比對，很容易確定基因。也可能擬南芥的蛋白質(zhì)組的數(shù)據(jù)庫中的每個(gè)蛋白質(zhì)就對應(yīng)了具體的基因，那么知道了蛋白質(zhì)也就知道了基因。

你聽懂將我說的內(nèi)容了嗎？（我說的已經(jīng)很詳細(xì)了吧，你的文章也沒有給出，我還要從蛋白質(zhì)組學(xué)的一般操作來猜你的文章的上下文內(nèi)容和你可能被絆倒的地方。

）如果沒有懂，把該論文提出來，我給你解釋，如果這幾天我不忙的話。

贊一下(2人)

2樓2013-06-28 02:19:34

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這個(gè)問題倒是我自從注冊以來第一次回答蛋白質(zhì)組學(xué)的應(yīng)用問題，坦率地講：我認(rèn)為難度不大，但是比較有意思）。

3樓2013-06-28 02:21:32

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★ ★
感謝參與，應(yīng)助指數(shù) +1
137167741: 金幣+1, 小木蟲鼓勵(lì)交流~~ 2013-06-28 08:49:35
夢在農(nóng)大: 金幣+1, ★★★很有幫助, 非常感謝 2013-06-28 23:15:53

arabidopsis是一個(gè)被研究的很透徹的基因組，在用tair作為目標(biāo)蛋白質(zhì)數(shù)據(jù)庫的時(shí)候，可以很輕松的得到對應(yīng)的基因信息�？床坏絪core的來源，一般猜測mascot會給出一個(gè)score，分?jǐn)?shù)越高，可靠性就越高（就是被鑒定出來的蛋白質(zhì)就是真實(shí)的蛋白質(zhì)）。倍數(shù)關(guān)系同樣不清楚，有定量蛋白組學(xué)，可以估算蛋白質(zhì)的含量，可能這個(gè)倍數(shù)的變化就是指treated/control之間蛋白質(zhì)濃度的變化。

4樓2013-06-28 05:37:36

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★
137167741: 金幣+1, 小木蟲鼓勵(lì)交流~~ 2013-06-28 20:07:11

更正：蛋白質(zhì)組的質(zhì)譜數(shù)據(jù)庫搜索原理與基因組不一樣，我在二樓的第一段原理答錯(cuò)了，因?yàn)楫?dāng)時(shí)第一次看圖時(shí)我以為是搜索基因，后來打完了也沒有改過來，Sorry！
蛋白質(zhì)組的質(zhì)譜數(shù)據(jù)庫搜索原理也有很多算法，我只舉其中一種，有些代表性。一般蛋白質(zhì)組的質(zhì)譜數(shù)據(jù)庫搜索原理第一步是將所得所測的肽質(zhì)量數(shù)與蛋白質(zhì)組的質(zhì)譜數(shù)據(jù)庫中的每一個(gè)蛋白質(zhì)的理論肽譜進(jìn)行比較。當(dāng)計(jì)算值落在誤差設(shè)定范圍以內(nèi)時(shí)，就記作一個(gè)匹配。與計(jì)算匹配的肽段數(shù)量不同，MOlecular Weight SEarh(MOWSE)使用經(jīng)驗(yàn)因子來為每一個(gè)肽匹配設(shè)定一個(gè)“權(quán)重”。該權(quán)重因子矩陣在構(gòu)建數(shù)據(jù)庫時(shí)產(chǎn)生，方法是：
先產(chǎn)生一個(gè)頻率因子矩陣F，在此矩陣中，每一行代表肽質(zhì)量數(shù)相差100道爾頓，每一列代表蛋白質(zhì)的質(zhì)量數(shù)相差100kD道爾頓。當(dāng)分析每一個(gè)序列時(shí)，設(shè)定合適的矩陣元素f(i,j)步長以便將肽質(zhì)量的大小作為蛋白質(zhì)的質(zhì)量的函數(shù)進(jìn)行統(tǒng)計(jì)分析。矩陣F中的每一列的元素除以該列中的最大值，從而使矩陣F歸一化，并且得到MOlecular Weight SEarh(MOWSE)因子矩陣。在用肽質(zhì)量實(shí)測值對理論肽質(zhì)量數(shù)據(jù)庫檢索后，按下式計(jì)算每一次檢索的應(yīng)得的分?jǐn)?shù)（score）：
score=50000/[M(protein)×∏m(i,j)],M(protein)是蛋白質(zhì)的分子量，∏m(i,j)為MOlecular Weight SEarh(MOWSE)因子矩陣中的元素的乘積。

6樓2013-06-28 11:01:08

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提醒：在2樓的我回答的第一段描述就是錯(cuò)了！

我是用BLAST算法講的，序列比對的方法很多，蛋白質(zhì)組的數(shù)組庫搜索也有很多不同的算法，我不可能都說出來，圖看不清，開始我以為是直接比對核酸序列。發(fā)帖前忘了改了。

wizardfan: 金幣+2, 謝謝參與。不過你的第一段描述更接近于BLAST算法，而不是PSM 。wizardfan的提示是對的，

不然，我還糾正不過來！

要按照BLAST算法去蛋白質(zhì)組學(xué)的質(zhì)譜數(shù)據(jù)庫搜索匹配信息，那就太累了。

9樓2013-06-28 23:23:12

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夢在農(nóng)大: 金幣+1, ★有幫助, 非常感謝啊 2013-06-29 22:10:25

內(nèi)容已刪除

13樓2013-06-29 05:43:12

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普通回帖

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137167741: 金幣+1, 小木蟲鼓勵(lì)交流~~ 2013-06-28 20:06:57

我回答的第一段描述就是用BLAST算法講的，序列比對的方法很多，蛋白質(zhì)組的數(shù)組庫搜索也有很多不同的算法，我不可能都說出來，圖看不起出，開始我以為是直接比對核酸序列。

5樓2013-06-28 10:29:16

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★ ★ ★
137167741: 金幣+1, 小木蟲鼓勵(lì)交流~~ 2013-06-28 20:07:27
夢在農(nóng)大: 金幣+2, ★★★很有幫助, 謝謝你耽誤你時(shí)間啦 2013-06-28 23:17:22

再舉一個(gè)蛋白質(zhì)組的質(zhì)譜數(shù)據(jù)庫搜索的例子：
1.“A lgorithms and Software Tools for Id entifying Proteins from ESI
Ta ndem MS Data: Sequest
The firs t algorithm/pr ogram to identify proteins by matching MS-MS data to database sequences is Sequest, which was introduced  by John Yates and Jimmy Eng in 1995. Several similar software tools
Prot ei n  Id entification with MS Data  101 have  been  introduced and these will be discussed below. However,
Seques t  will be described in greatest detail as representative of this class  of tools. The value of programs such as Sequest is that they provide a relatively rapid assignment of MS-MS spectra to specific peptid e sequences in databases. This allows fast reduction of large volumes of LC-MS-MS data in pr oteomics an alyses. However, it
is important to emphas ize that Sequest and similar programs do not  actually perform de novo  interpretation of the spectra per se .Consequent ly , the output of these programs depends on the quality
of the  MS-MS data obtained and the completeness and accuracy of the  database used.
Here’s how Sequest works. When the MS instrument obtains an MS-MS scan, it not only records the MS-MS scan itself, but also the m/z  value of th e precursor ion. This information is stored together
with the scan  data. After the analysis is complete, the user sits at the computer and opens the Sequest program. The user then selects the datafile containing the MS-MS scans to be analyzed. The user can tell
Sequest what enzyme (e.g., trypsin) was used to digest the protein sample and also specifie s whether singly or doubly charged ions were subjected to MS-MS. Finally, the user selects a database against which
the  MS-MS data are to be compared.
Once the program starts, all of the proteins in the database are subjected to a virtual digestion with the enzyme specified by the user (e.g., trypsin) . This generates a master list of possible peptides for co mparison to the MS-MS scans. Then each MS-MS scan is analyzed as foll ows：
• The precursor  m/z for each MS-MS scan is used to select peptides
from the database with the same mass (within a defined mass
tolerance). If no digestion enzyme was specified, the program
simply select s all possible peptide sequences that correspond to
the mass of the pe ptide ion analyzed in that MS-MS scan.
•  Theoretical MS-MS spectra are generated from each of the selected
peptides.
•  The MS-MS spectrum being analyzed is compared with each of
the theoretical  MS-MS spectra generated from the database.
•  A correlation score is calculated for each match between the
MS-MS scan and the theoretical MS-MS spectra.”

2."Soft ware Tool s for Peptide Mass
Fingerprinting: Scoring the Results
In  MALDI- TOF spectra from real samples, there are typically dozens
of m/z  si gnal s. Peptide mass fingerprinting software can usually
match just about all of these to some entry in a database. However,
given  errors in  m/z m e a s u r e m e nt,  f r e q u e nt  s a mpl e  c o nt a m i n at i o n ,  a n d
the  presence of unanticipated posttranslational modifications, not all of  th e  matc hes  will point to the same proteins. So how do we score the hits  to determine which protein best matches the data?
The  simplest approach is to assign the highest score to proteins whose predicted tryptic peptides match the greatest number of  m/z signals in th e MS data. If we search only one  m/z value, then several
proteins could be equally good matches. Howe ver, as we search a  greater number of m/z values, mo re matches correspond to a particular protein and lead to a greater score for that protein vs others. This
fairly simple approach works reas onably well with very good MS data. However, it tends to  assign higher scores to larger proteins.
As  note d earlier, larger proteins yield more tryptic peptides, so the chances of a match to one of these is greater for larger proteins than fo r smaller proteins.
To  add r e s s  t hese  problems, several of the available peptide mass fingerprinting programs use more sophisticated scoring algorithms.
Thes e algorithms correct for scoring bias due to protein size, in which larger proteins give rise to greater numbers of peptides. They also correct for the tendency of smaller peptides in databases to have a
greate r number of matches with searched  m/z  values. Finally, some of these algori thms also apply pr obability-based statistics to better define the significance of protein identifications. At the time of this
writing, the principal tools available for peptide mass fingerprinting can be grouped into  three categories:
• First-generation freeware and subscription software tools that as sign scores based on the number of m/z values in a spectrum 86  To ol s of P r o t e om ic s
that match  database values within a given mass tolerance. These
programs include PepSea (http://www.protana.com) and
Pept Ident/MultIdent (http://www.expasy.ch/tools/peptident.html).
•  Second-generation freeware and subscription software tools that
employ scoring algo rith ms that take into account the effects
of protein size and peptide length on the probabilities of match-ing. These include MOWSE (http://srs.hgmp.mrc.ac.uk/cgi-bin/mowse) and MS-Fit (http://prospector.ucsf.edu/).
• Third-generation so ftware that employs more extensive probability-based scoring to provide a statistical basis for scores and also to estimate the probabilities that matches may reflect random events, rather than true identities. These programsinclude  ProFound (http://prowl.rockefeller.edu/cgi-bin/Pro
Found) an d Mascot (http://www.matrixscience.com/)."

3."A lgorithms and Software Toolsfor Id entifying Proteins from ESI Tandem MS Data: Sequest
The firs t algorithm/pr ogram to identify proteins by matching MS-MS data to database sequences is Sequest, which was introduced by John Yates and Jimmy Eng in 1995. Several similar software tools Protein  Id entification with MS Data  101have  been  introduced and these will be discussed below. However,
Seques t  will be described in greatest detail as representative of this class  of tools. The value of programs such as Sequest is that they provide a relatively rapid assignment of MS-MS spectra to specific peptid e sequences in databases. This allows fast reduction of large volumes of LC-MS-MS data in pr oteomics an alyses. However, it
is important to emphas ize that Sequest and similar programs do not  actually perform de novo  interpretation of the spectra perse .
Consequent ly , the output of these programs depends on the quality of the  MS-MS data obtained and the completeness and accuracy of the  database used.
Here’s how Sequest works. When the MS instrument obtains an MS-MS scan, it not only records the MS-MS scan itself, but also the m/z  value of th e precursor ion. This information is stored together
with the scan  data. After the analysis is complete, the user sits at the computer and opens the Sequest program. The user then selects the datafile containing the MS-MS scans to be analyzed. The user can tell
Sequest what enzyme (e.g., trypsin) was used to digest the protein sample and also specifie s whether singly or doubly charged ions were subjected to MS-MS. Finally, the user selects a database against which
the  MS-MS data are to be compared.
Once the program starts, all of the proteins in the database are subjected to a virtual digestion with the enzyme specified by the user (e.g., trypsin) . This generates a master list of possible peptides for
co mparison to the MS-MS scans. Then each MS-MS scan is analyzed
as foll ows :
• The precursor  m/z for each MS-MS scan is used to select peptides from the database with the same mass (within a defined mass tolerance). If no digestion enzyme was specified, the program
simply select s all possible peptide sequences that correspond to the mass of the pe ptide ion analyzed in that MS-MS scan.
•  Theoretical MS-MS spectra are generated from each of the selected peptides.
•  The MS-MS spectrum being analyzed is compared with each of
the theoretical  MS-MS spectra generated from the database.
•  A correlation score is calculated for each match between the MS-MS scan and the theoretical MS-MS spectra."

-------From

ANIEL C. L IEBLER,INTRODUCTION TO P ROTEOMICS,Humana Press Inc,2002.

7樓2013-06-28 11:15:41

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在7樓，我是新舉出三種蛋白質(zhì)組的質(zhì)譜數(shù)據(jù)庫搜索的算法的例子，不是一種。

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引用回帖:

4樓: Originally posted by wizardfan at 2013-06-28 05:37:36
arabidopsis是一個(gè)被研究的很透徹的基因組，在用tair作為目標(biāo)蛋白質(zhì)數(shù)據(jù)庫的時(shí)候，可以很輕松的得到對應(yīng)的基因信息�？床坏絪core的來源，一般猜測mascot會給出一個(gè)score，分?jǐn)?shù)越高，可靠性就越高（就是被鑒定出來的 ...

版主你好是這樣。我們上生物信息學(xué)課老師讓講文獻(xiàn)，說最好舉一個(gè)例子說明這篇文章的數(shù)據(jù)是怎么弄得出來的。但是我不知道人家最開始測得的AA順序，可不可以從它表里面表的基因反推啊謝謝