ESyPred3D蛋白质三级结构预测

BIOINFORMATICS

Vol.18no.92002Pages

1250–1256

ESyPred3D:Prediction of proteins 3D structures

Christophe Lambert ?,Nadia L′eonard,Xavier De Bolle and Eric Depiereux

Facult′es Universitaires Notre-Dame de la Paix,Unit′e de Recherche en Biologie Mol′eculaire,Rue de Bruxelles,61,B-5000Namur,Belgium

Received on February 1,2002;revised on March 7,2002;accepted on March 18,2002

ABSTRACT

Motivation:Homology or comparative modeling is cur-rently the most accurate method to predict the three-dimensional structure of proteins.It generally consists in four steps:(1)databanks searching to identify the struc-tural homolog,(2)target–template alignment,(3)model building and optimization,and (4)model evaluation.The target–template alignment step is generally accepted as the most critical step in homology modeling.

Results:We present here ESyPred3D,a new automated homology modeling program.The method gets bene?t of the increased alignment performances of a new alignment strategy.Alignments are obtained by combining,weighting and screening the results of several multiple alignment programs.The ?nal three-dimensional structure is build using the modeling package MODELLER.ESyPred3D was tested on 13targets in the CASP4experiment (C ritical A ssessment of Techniques for Proteins S tructural P rediction).Our alignment strategy obtains better results compared to PSI-BLAST alignments and ESyPred3D alignments are among the most accurate compared to those of participants having used the same template.

Availability:ESyPred3D is available through its web site at http://www.fundp.ac.be/urbm/bioinfo/esypred/.

Contact:https://www.sodocs.net/doc/2b862219.html,mbert@fundp.ac.be;http://www.fundp.ac.be/～lambertc

INTRODUCTION

Three-dimensional (3D)protein structure is an important source of information to better understand the function of a protein,its interactions with other compounds (ligands,proteins,DNA,...)and to understand phenotypical effects of mutations (Tramontano,1998).The 3D protein struc-ture can be predicted according to three main categories of methods (Rost and O’Donoghue,1997):(1)homology or comparative modeling (described below);(2)fold recogni-tion (predicting the global fold of a protein);(3)ab initio techniques (trying to model the 3D structure of proteins using only the sequence and a force ?eld).

?To whom correspondence should be addressed.

Homology modeling is historically the ?rst (Browne et al.,1969)and the most accurate method (Sanchez and Sali,1997).It was shown during the last CASP experiment (Venclovas et al.,1999)(C ritical A ssessment of Techniques for Proteins S tructural P rediction)that the critical steps are:(1)template selection,(2)target–template alignment step,(3)modeling of regions not present or signi?cantly different from those in template and (4)modeling of side chains.Among these critical steps,it is commonly accepted that the target–template alignment step is the most critical (Mosimann et al.,1995;Martin et al.,1997).

It is known that above 50%of identity rate between target and template,pairwise alignments provide accurate models.Between 30%and 50%of identity,multiple align-ments between target,template and similar proteins must be used and the pairwise alignments between target and template must be extracted from this multiple alignment.Below 30%of identity rate,only heuristic combinations of multiple alignments,experimental data and know-how of an expert are able to generate an accurate model.

A large number of techniques have been developed to predict 3D structures of proteins by homology modeling.For the target–template alignment step,most of them use PSI-BLAST (Altschul et al.,1997),PileUp (Wisconsin Package Version 9.1,Genetics Computer Group (GCG),Madison,Wisc.),ClustalW (Thompson et al.,1994),3D-PSSM (Fischer et al.,1999),SAMT99(Karplus et al.,1997),or also the alignment producing the best model out of a collection computed from various alignment programs (Yang and Honig,1999).

Our laboratory developed the MATCHBOX multiple sequence alignment software in the early 1990s (De-piereux and Feytmans,1992)and it has proved to be one of the most accurate in terms of speci?city (Depiereux et al.,1997).Much effort has been consented into im-proving alignment accuracy by adding information such as secondary structure predictions,solvent accessibility predictions,speci?c scoring matrices and combination with ClustalW.In all cases,it was only possible to slightly improve multiple alignment accuracy (unpublished re-1250

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ESyPred3D:Prediction of proteins3D structures

sults).Meanwhile,no signi?cant improvements in align-ment performance have been published by other groups. Furthermore,no alignment method can be quali?ed as the absolute most reliable one.Indeed,benchmarks(Briffeuil et al.,1998;Thompson et al.,1999)have shown that comparative performances of alignment programs are deeply dependent on the set of aligned sequences.

In this work,we tackle the target–template alignment problem by developing a speci?c program to align target and template sequences in homology modeling.Matching of homologous segments is improved by incorporation of the results of several multiple alignment programs.Results are scored to optimize the performances and screened to remove incompatible matches.Several algorithmic prob-lems have required speci?c developments in order to gen-erate and ef?ciently screen the database of the various and often incompatible alignments proposed by the different algorithms.This new alignment strategy is included into our ESyPred3D program(http://www.fundp.ac.be/urbm/ bioinfo/esypred/)that predicts the3D structure of proteins using the homology modeling approach.

SYSTEM AND METHODS

Our automatic program(ESyPred3D)implements the four steps of the homology modeling approach(Eisen-haber et al.,1995):(1)databanks searching to identify the structural homolog,(2)target–template alignment, (3)model building and optimization and(4)model eval-uation(not implemented at the time of the CASP4ex-periment).ESyPred3D was run on an SGI Octane Dual processor225MHz workstation under IRIX6.5. Identifying the structural homolog

To?nd homologs to the target sequence,PSI-BLAST2.0.14 (downloaded from NCBI and run locally)is run using the latest possible version of the NR databank(NCBI).The chosen template is the sequence from the latest version of the PDB databank with the lowest expected value after four iterations.The cutoff for the expected value is0.0001 (-h?ag).If no template is found with these criteria,the program stops.

Aligning sequences and constructing the3D model According to Thompson et al.(1999),the quality of the alignment of sequences highly depends on the context of the alignment.The results obtained for a given pair of sequences may be different depending on the set of sequences submitted to multiple alignment programs.So, after fetching all sequences retrieved by PSI-BLAST,two sets of sequences are generated in order to create two different computational conditions for running multiple alignment programs:the set A contains the50best hits including the target and the template(the number of sequences is limited to50to reduce computing time).The set B is a subset of at least seven sequences,including the target and the template,produced by dropping too redundant sequences with the PURGE program(provided with the Gibbs package(Neuwald et al.,1995)).The BLAST score(using the-b?ag)to select or eliminate sequences during the PURGE operation is250.

The building of the target–template alignment is per-formed in these steps(see Figure1):

(a)Matching.Both sets of sequences(A and B)are

aligned by?ve alignment programs emerging from

two benchmarks(Briffeuil et al.,1998;Thompson et

al.,1999).These programs are:ClustalW,Dialign2

(Morgenstern,1999),Match-Box(Depiereux et al.,

1997),Multalin(Corpet,1988)and PRRP(Gotoh,

1996).Ten multiple alignments are generated,each

one including the target and template sequences.

Then,the pairwise alignments between the target

and template sequences are extracted,leading to ten

different pairwise alignments between the target and

the template.

(b)Database building.Each position of the alignments

is stored in a database,all the redundant results,i.e.

the same amino acid placed at the same position

by different programs,being scored in a frequency

table.

(c)Screening.The position with the highest score is

taken as the?rst anchor point to build the?nal

target–template alignment.Incompatible results(see

Figure2.),aligning regions located up-and down-

stream anchor points,are removed from the database.

The process is pursued,new anchor positions being

determined,and incompatible regions being elimi-

nated,until all results are selected or removed.The

?nal target–template alignment is thus composed

by the most frequent aligned positions,under the

condition of compatibility.

This?nal pairwise alignment is used by the MODEL homology modeling routine of MODELLER release4 (Sali and Blundell,1993;Sali et al.,1997)to build a 3D model of the target protein.This routine includes the satisfaction of spatial and geometric restraints and a very fast molecular dynamic annealing:no other re?nements were applied.

Participation to the CASP4experiment

ESypred3D server participated to the CASP4experiment (see complete results at https://www.sodocs.net/doc/2b862219.html,/ casp4/;group218:LAMBERT-CHRISTOPHE).All the models generated by MODELLER were submitted to the CASP4contest without any geometric or energetic evaluation.The number of homology modeling targets used during the CASP4competition was too small to take

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ESyPred3D target-template alignment

Fig.1.Flowchart of the ESyPred3D target –template alignment method.See the text for details.

Fig.2.Example of compatible and incompatible results on two hypothetical sequences.Three cases are reported:(a)Alignments I –I and I –L are not compatible because the same amino acid in sequence 1is aligned to two amino acids in sequence 2.(b)Alignments P –P and A –A are not compatible.P in sequence 1is at the right of A but P in the second sequence is the left of A.(c)Alignment I –I and P –P are compatible.The prolines are both at the right of the isoleucines.

a very robust statistical conclusion about the performances of our method.However,results obtained provide a ?rst estimation of performances.For more statistical results,see the continuous evaluation of servers performed by EV A (Eyrich et al.,2001)(https://www.sodocs.net/doc/2b862219.html,/eva/).

For the purpose of the CASP4experience,two models were built for 13comparative modeling targets (Table 1.)for which ESyPred3D was able to predict a 3D structure:(1)The ?rst model was built using the complete strategy

described above

(ESyPred3D)(models T0xxxTS2181).(2)The second model was built using the same strategy

as ESyPred3D but by using the rough sequence-structure alignment provided by PSI-BLAST (models T0xxxTS2182).

Scoring schemes used to compare target structures to models

To compare ESyPred3D models to PSI models and ESyPred3D models to models of other CASP4partici-pants,the AL0and the GDT TS scores were chosen.Both scores where calculated using the LGA (Local –Global Alignment)program (Zemla,2000).

AL0

AL0is the number of correctly aligned residues in the target –template alignment.This score is very signi ?cant in this case because our method was designed to generate op-timal alignment performances.This number is evaluated by,at ?rst,making a structural alignment of the prediction and the target structure with the DALI-server,and then,counting the number of residues in the model for which the closest residue in the target is the correct one (the distance

between their α-carbons being less than 3.8?A).

GDT TS

The Global Distance Test,G DT d i ,is the number of α-carbons of a prediction not deviating from more than d i ?A

from the α-carbons of the targets,after optimal super-imposition.This optimal superimposition is computed in such a way that the number of residues (α-carbons)that can ?t under the distance cutoff d i is maximum.

If NT is the total number of residues of the target,GDT TS (GDT Total Score)computed according to the formula given below is the mean fraction of residues of the target not deviating from the prediction after four

optimal superimpositions with 1.0,2.0,4.0,8.0?A α-carbon distance cutoffs.The GDT TS score represents the overall quality of the model.This score was used to evaluate the complete procedure of ESyPred3D:identify-ing the structural homolog,aligning target to template and building the 3D model.

G DT T S =100?

d i

G DT d i

N T

4

d i ∈{1.0,2.0,4.0,8.0}

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ESyPred3D:Prediction of proteins3D structures Table1.Homology modeling targets for the CASP4experiment

Target Description PDB code

T0090ADP-ribose pyrophosphatase,E.coli1g0s,1g9q,1ga7 T0092Hypothetical protein HI0319,H.In?uenzae

T0099No description

T0103Pepstatin insensitive carboxyl proteinase,Pseudomonas sp.1ga6

T0111Enolase,E.coli

T0112Ketose Reductase/Sorbitol Dehydrogenase,B.argentifolii1e3j

T0113Short chain3-hydroxyacyl-coa dehydrogenase,rat1e3w,1e3s,1e6w T0117Deoxyribonucleoside kinase,D.Melanogaster

T0121MalK,T.litoralis1g29

T0122Tryptophan Synthase alpha subunit,P.furiosus1geq

T0123Beta-lactoglobulin,pig1exs

T0125Sp18protein,H.fulgens1gak

T0128Manganese superoxide dismutase homolog,P.aerophilum

RESULTS AND DISCUSSION

The performance of our homology modeling server is analyzed in three steps.In the?rst section,ESyPred3D alignments are compared to PSI-BLAST alignments.In the second section,ESyPred3D alignments are compared to those of other participants having used the same template.Since our alignment method is speci?cally designed for homology modeling,in the third section, ESyPred3D models are compared to those of other CASP4 competitors in order to evaluate the global performance of our homology modeling strategy.

Alignment performances of ESyPred3D models compared to those of PSI-BLAST models

Table2contains AL0scores for all models.Out of the 13models,ESyPred3D obtains nine AL0scores greater than PSI-BLAST and only one AL0score signi?cantly lower(more than two amino acids incorrectly aligned) than PSI-BLAST,for T0112.Two reasons explain the poor alignment for T0112:

(1)The number of homologs found by PSI-BLAST was

so large that the non-redundant set could not be

computed with PURGE.

(2)Four regions of T0112shared only a very low sim-

ilarity with homologues.So the different alignment

programs produced contradictory results in these re-

gions,and only a poor alignment could be estab-

lished by our method.

From this?rst evaluation,we can conclude that the quality of the target–template alignment is generally better by using ESyPred3D alignment methodology than using the target–template alignment provided by PSI-BLAST. Comparison of ESyPred3D with those of the participants having used the same template

The number of groups that have used the same templates as ESyPred3D is strongly variable(from two to65groups, Figure3).In Figure3,using AL0scores,ESyPred3D models obtained one time the?rst place,?ve times the second place,three times the third place and one times the fourth place.ESyPred3D models are then ten times in the top four places out of the13targets.Taking into account that a group that performs better than ESyPred3D model for one target is rarely the same that performs better for another target,one can conclude that our methodology is among the most ef?cient.

Comparison of ESyPred3D models with those of all CASP4participants

In this section,the complete strategy of ESyPred3D is evaluated and the performances are compared to those of other CASP4participants using the GDT TS score. The number of models submitted for each target was always above200.So,to enable a rapid interpretation of the distributions of scores,we have computed the third quartile of these distributions.The third quartile(Q3)of a distribution is the value such that75%of values in the list are less or equal to it.All information provided in Figure4has been normalized by the Q3value,for each target.For each target,Figure4contains:(1)the GDT TS score of ESyPred3D models;(2)the GDT TS score of the

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Table2.Scores for ESyPred3D and PSI-BLAST models.The last column shows templates that lead to the best models presented at CASP4 Model RMSD(allαcarbons)GDT TS AL0Template Templates leading to the best models

T0090TS2181 6.5230.15411tum1mut

T0090TS2182 6.4423.37301tum1mut

T0092TS218114.7035.69731d2g1xva,1d2h

T0092TS2182 5.2234.69661d2g1xva,1d2h

T0099TS2181 5.5452.23261qly1a0n,1ad5,2hck,1qcf,2src

T0099TS2182 5.5650.00211qly1a0n,1ad5,2hck,1qcf,2src

T0103TS218111.9538.591281sbh1mee,1sup

T0103TS218212.4133.761131sbh1mee,1sup

T0111TS2181 2.2983.553831one1pdz,1pdy,1ykf,4-7enl

T0111TS2182 2.2682.393811one1pdz,1pdy,1ykf,4-7enl

T0112TS2181 5.3554.311741hdy1teh,1ykf

T0112TS2182 4.1359.191971hdy1teh,1ykf

T0113TS2181 3.3281.862141hdc1hdc,2hsd

T0113TS2182 3.6880.492071hdc1hdc,2hsd

T0117TS21818.2456.851141qhi1e2k,1kim,1ki2-7

T0117TS2182 3.8755.711091qhi1e2k,1kim,1ki2-7

T0121TS2181 3.3541.941431b0u1b0u

T0121TS2182 3.3840.931411b0u1b0u

T0122TS2181 2.4379.152031cw21a5a,1a5b,1beu,1cw2

T0122TS2182 2.4174.581901cw21a5a,1a5b,1beu,1cw2

T0123TS2181 4.1563.911022a2u2a2g,1beb

T0123TS2182 3.7565.471022a2u2a2g,1beb

T0125TS2181 4.1561.13743lyn2lis,3lyn

T0125TS2182 4.0760.40753lyn2lis,3lyn

T0128TS2181 1.7486.731851abm1b06,1sss

T0128TS2182 1.6587.321871abm1b06,1sss

best model received by CASP4organizers and(3)the third quartile is equal to1.0because of the normalization. Figure4shows that ESyPred3D built three models with scores close to the best model,indeed the second place was obtained for targets T0103,T0121and T0122 (see full tables at https://www.sodocs.net/doc/2b862219.html,/casp4/). ESyPred3D predicted eight models above Q3values,i.e. in the top25%of participants.Further analysis of the data at the CASP4web site shows that there are few groups that have reached such a number of scores values above the Q3. It is also important to note that the group that obtained the best model for one target is rarely the same that submitted the best model for another target.

The analysis of GDT TS scores(Figure4)showed that seven targets(T0090,T0092,T0099,T0112,T00117,T0123and T0128)obtained values signi?cantly lower than those of the best models.For targets T0090,T0092, T0099,T0117,T0123and T0128the low values of GDT TS are due to the selection of a template that was not fully adequate.Indeed,for these targets,the alignment performances remain good when comparing only to groups that used the same template,as shown by the AL0score in Figure3.Although in our methodology the template selection process has to be improved,it is important to note that a completely inadequate template was never chosen.The result of T0112is due to the quality of the alignment as you can see in Figure3.

The fact that eight models from13are above the Q3 shows that our alignment method combined with the PSI-BLAST template selection and the use of MODELLER

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102030405060708090100T 0090T 0092T 0099T 0103T 0111T 0112T 0113T 0117T 0121T 0122T 0123T 0125T 0128

Targets

A L 0 (i n % o f t h e l e n g t h )

Fig.3.AL0scores for targets studied in this work.Two series are reported for each target:the score of ESyPred3D models (black bullets)and the scores of models of other CASP4participants having used the same template (blank bullets).AL0scores are expressed as a fraction of the length of the

target.

T 0090T 0092T 0099T 0103T 0111T 0112T 0113T 0117T 0121T 0122T 0123T 0125T 0128

Targets

G D T _T S (i n % o f Q 3)

Fig.4.GDT TS scores for targets studied in this work.Three points are reported for each target:the score of the model that obtain the best score (bold line),the ESyPred3D model (box)and the third quartile value (dotted line).All values are expressed as a fraction of the third quartile value.The group IDs of best predictors with their selected templates are also reported.

to obtain the 3D model is a good strategy.Even if the template selection or the alignment quality is not optimal,the global quality of the ESyPred3D modeling strategy remains good.

CONCLUSION

A new alignment methodology for homology modeling of proteins has been developed.The program has been tested on 13targets of the CASP4for its alignment performances and for the general quality of the provided models.

Our alignment strategy produced better results com-pared to PSI-BLAST alignments and ESyPred3D align-

ments are among the most accurate comparing to partic-ipants having used the same template.Furthermore,our ESyPred3D program provides models that are among the best of the CASP4experiment.

Nevertheless,our alignment methodology could be im-proved.Thompson et al.(1999)and Briffeuil et al.(1998)benchmarks showed that all alignment programs have different level of performance.We plan to use this in-formation to improve the computing of the alignment,by weighting each multiple alignment method with the numeric representation of the mean performance of the method.Additional information such as secondary struc-ture predictions can also be used in the box selection in order to improve the alignment quality.

The template problem remains troublesome in homol-ogy modeling,especially when the target and template sequences are sharing a low identity rate.To improve the template selection,the use of better parameters or better scoring matrices for PSI-BLAST (like the one de-scribed in Kann et al.(2000))need to be investigated.In the same way,PSI-BLAST can also be replaced by SAM-T99(Karplus et al.,1998)or other programs.The intrinsic quality of the possible template structures (NMR,resolution,...)and the selection of multiple templates will also be taken into account to improve our modeling strategy.

The model evaluation step of our homology modeling methodology has not yet been developed.Geometric and energetic evaluation of the model can be done using ANOLEA (Melo and Feytmans,1997),PROCHECK (Laskowski et al.,1993)or Verify3D (Luthy et al.,1992).The results of these evaluations will be used to change our target –template alignment or to select a more appropriate template.The process will be iterated in order to ?nd the template that provides the best evaluated model.A similar iteration procedure has been used by the Blundell group in the CASP4.

ACKNOWLEDGMENTS

We thank the organizers and assessors of the CASP4experiment for their valuable contributions to the structure prediction ?eld.Christophe Lambert holds a specialized

grant from the ‘Fonds pour la Formation `a

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用DiscoverStuido所提供的蛋白质三级结构预测之同源模拟

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多離子通道已成為製藥界開發新藥的目標，離子通道的研究還有非常大的潛力和應用空間。 由於生物資訊近幾年來發展迅速，尤其基因體計劃的進行更增加了資料庫中的數量，包括核酸、蛋白質及結構等資料庫。一般說來，蛋白質三維結構主要以實驗方法來決定，例如X-射線繞射法或NMR光譜法。事實上，技術上的困難使得蛋白質三維結構決定的速度相當地緩慢，因此發展出利用電腦並依據蛋白質的序列來預測其三級結構的方法。這些方法包含以物理化學原理的ab-initio methods 及以資料庫提供的序列和結構知識衍生的蛋白質摺疊認識fold- recognition methods（亦稱threading 穿針引線法），和同源模擬法（homology modeling）。 我們想透過Discover Stuido中的homology modeling方法來預測離子通道蛋白經胺基酸點突變之後可能的構型變化，以及預測相關藥物與通道蛋白之間可能的作用位置，並且可以有效率的篩選出可能作用的關鍵胺基酸，再透過電生理的方式來探測藥物對離子通道的行為模式，有助於進一步且有效率的了解離子通道可能的分子機制。(三)研究方法與步驟： 同源模擬法五個主要的步驟： (1)資料庫搜尋及選擇模版（templates）

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BIOINFORMATICS Vol.18no.92002Pages 1250–1256 ESyPred3D:Prediction of proteins 3D structures Christophe Lambert ?,Nadia L′eonard,Xavier De Bolle and Eric Depiereux Facult′es Universitaires Notre-Dame de la Paix,Unit′e de Recherche en Biologie Mol′eculaire,Rue de Bruxelles,61,B-5000Namur,Belgium Received on February 1,2002;revised on March 7,2002;accepted on March 18,2002 ABSTRACT Motivation:Homology or comparative modeling is cur-rently the most accurate method to predict the three-dimensional structure of proteins.It generally consists in four steps:(1)databanks searching to identify the struc-tural homolog,(2)target–template alignment,(3)model building and optimization,and (4)model evaluation.The target–template alignment step is generally accepted as the most critical step in homology modeling. Results:We present here ESyPred3D,a new automated homology modeling program.The method gets bene?t of the increased alignment performances of a new alignment strategy.Alignments are obtained by combining,weighting and screening the results of several multiple alignment programs.The ?nal three-dimensional structure is build using the modeling package MODELLER.ESyPred3D was tested on 13targets in the CASP4experiment (C ritical A ssessment of Techniques for Proteins S tructural P rediction).Our alignment strategy obtains better results compared to PSI-BLAST alignments and ESyPred3D alignments are among the most accurate compared to those of participants having used the same template. Availability:ESyPred3D is available through its web site at http://www.fundp.ac.be/urbm/bioinfo/esypred/. Contact:https://www.sodocs.net/doc/2b862219.html,mbert@fundp.ac.be;http://www.fundp.ac.be/～lambertc INTRODUCTION Three-dimensional (3D)protein structure is an important source of information to better understand the function of a protein,its interactions with other compounds (ligands,proteins,DNA,...)and to understand phenotypical effects of mutations (Tramontano,1998).The 3D protein struc-ture can be predicted according to three main categories of methods (Rost and O’Donoghue,1997):(1)homology or comparative modeling (described below);(2)fold recogni-tion (predicting the global fold of a protein);(3)ab initio techniques (trying to model the 3D structure of proteins using only the sequence and a force ?eld). ?To whom correspondence should be addressed. Homology modeling is historically the ?rst (Browne et al.,1969)and the most accurate method (Sanchez and Sali,1997).It was shown during the last CASP experiment (Venclovas et al.,1999)(C ritical A ssessment of Techniques for Proteins S tructural P rediction)that the critical steps are:(1)template selection,(2)target–template alignment step,(3)modeling of regions not present or signi?cantly different from those in template and (4)modeling of side chains.Among these critical steps,it is commonly accepted that the target–template alignment step is the most critical (Mosimann et al.,1995;Martin et al.,1997). It is known that above 50%of identity rate between target and template,pairwise alignments provide accurate models.Between 30%and 50%of identity,multiple align-ments between target,template and similar proteins must be used and the pairwise alignments between target and template must be extracted from this multiple alignment.Below 30%of identity rate,only heuristic combinations of multiple alignments,experimental data and know-how of an expert are able to generate an accurate model. A large number of techniques have been developed to predict 3D structures of proteins by homology modeling.For the target–template alignment step,most of them use PSI-BLAST (Altschul et al.,1997),PileUp (Wisconsin Package Version 9.1,Genetics Computer Group (GCG),Madison,Wisc.),ClustalW (Thompson et al.,1994),3D-PSSM (Fischer et al.,1999),SAMT99(Karplus et al.,1997),or also the alignment producing the best model out of a collection computed from various alignment programs (Yang and Honig,1999). Our laboratory developed the MATCHBOX multiple sequence alignment software in the early 1990s (De-piereux and Feytmans,1992)and it has proved to be one of the most accurate in terms of speci?city (Depiereux et al.,1997).Much effort has been consented into im-proving alignment accuracy by adding information such as secondary structure predictions,solvent accessibility predictions,speci?c scoring matrices and combination with ClustalW.In all cases,it was only possible to slightly improve multiple alignment accuracy (unpublished re-1250 c Oxford University Press 2002

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1.蛋白质的一级结构（共价结构） 蛋白质的一级结构也称共价结构、主链结构。 1.蛋白质结构层次 一级结构（氨基酸顺序、共价结构、主链结构） ↓是指蛋白质分子中氨基酸残基的排列顺序 二级结构 ↓ 超二级结构 ↓ 构象（高级结构）结构域 ↓ 三级结构(球状结构) ↓ 四级结构(多亚基聚集体) 1.一级结构的要点 ． 1.蛋白质测序的一般步骤 祥见 P116 （1）测定蛋白质分子中多肽链的数目。 （2）拆分蛋白质分子中的多肽链。 （3）测定多肽链的氨基酸组成。 （4）断裂链内二硫键。 （5）分析多肽链的N末端和C末端。 （6）多肽链部分裂解成肽段。 （7）测定各个肽段的氨基酸顺序 （8）确定肽段在多肽链中的顺序。 （9）确定多肽链中二硫键的位置。 1.蛋白质测序的基本策略 对于一个纯蛋白质，理想方法是从N端直接测至C端，但目前只能测60个N端氨基酸。 1.直接法（测蛋白质的序列） 两种以上特异性裂解法 N C A 法裂解 A1 A2 A3 A4 B 法裂解 B1 B2 B3 B4 用两种不同的裂解方法，产生两组切点不同的肽段，分离纯化每一个肽段，分离测定两个肽段的氨基酸序列，拼接成一条完整的肽链。

1. 间接法（测核酸序列推断氨基酸序列） 核酸测序，一次可测600-800bp 1. 测序前的准备工作 1. 蛋白质的纯度鉴定 纯度要求，97%以上，且均一，纯度鉴定方法。（两种以上才可靠） ⑴聚丙烯酰胺凝胶电泳(PAGE)要求一条带 ⑵DNS —cl （二甲氨基萘磺酰氯）法测N 端氨基酸 1. 测定分子量 用于估算氨基酸残基n= 方法：凝胶过滤法、沉降系数法 1. 确定亚基种类及数目 多亚基蛋白的亚基间有两种结合方式： ⑴非共价键结合 8mol/L 尿素，SDS SDS-PAGE 测分子量 ⑵二硫键结合 过甲酸氧化： —S —S —+HCOOOH → SO 3H β巯基乙醇还原： 举例:： 血红蛋白 (α2β2) （注意，人的血红蛋白α和β的N 端相同。） 分子量： M 拆亚基： M 1 、M 2 两条带 拆二硫键： M 1 、M 2 两条带 分子量关系： M = 2M 1 + 2M 2 1. 测定氨基酸组成 主要是酸水解，同时辅以碱水解。氨基酸分析仪自动进行。 确定肽链中各种a.a 出现的频率,便于选择裂解方法及试剂。 ①Trp 测定 对二甲基氨基苯甲醛 590nm 。 ②Cys 测定 5、5/一二硫代双（—2—硝基苯甲酸）DTNB ，412nm 1. 端基分析 ①N 端分析 DNS-cl 法：最常用，黄色荧光，灵敏度极高，DNS-多肽水解后的DNS-氨基酸不需要提取。 DNFB 法：Sanger 试剂，DNP-多肽，酸水解，黄色DNP-氨基酸，有机溶剂（乙酸乙酯） 抽提分离，纸层析、薄层层析、液相等 PITC 法：Edman 法，逐步切下。无色PTH-氨基酸，有机溶剂抽提，层析。 ②C 端分析 110mw

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蛋白质预测在线分析常用软件推荐 蛋白质预测分析网址集锦 物理性质预测： Compute PI/MW http://expaxy.hcuge.ch/ch2d/pi-tool.html Peptidemasshttp://expaxy.hcuge.ch/sprot/peptide-mass.html TGREASE ftp://https://www.sodocs.net/doc/2b862219.html,/pub/fasta/ SAPS http://ulrec3.unil.ch/software/SAPS_form.html 基于组成的蛋白质识别预测 AACompIdent http://expaxy.hcuge.ch ... htmlAACompSim http://expaxy.hcuge.ch/ch2d/aacsim.html PROPSEARCH http://www.e mbl-heidelberg.de/prs.html 二级结构和折叠类预测 nnpredict https://www.sodocs.net/doc/2b862219.html,/~nomi/nnpredict Predictprotein http://www.embl-heidel ... protein/SOPMA http://www.ibcp.fr/predict.html SSPRED http://www.embl-heidel ... prd_info.html 特殊结构或结构预测 COILS http://ulrec3.unil.ch/ ... ILS_form.html MacStripe https://www.sodocs.net/doc/2b862219.html,/ ... acstripe.html 与核酸序列一样，蛋白质序列的检索往往是进行相关分析的第一步，由于数据库和网络技校术的发展，蛋白序列的检索是十分方便，将蛋白质序列数据库下载到本地检索和通过国际互联网进行检索均是可行的。 由NCBI检索蛋白质序列 可联网到：“http://www.ncbi.nlm.ni ... gi?db=protein”进行检索。 利用SRS系统从EMBL检索蛋白质序列 联网到：https://www.sodocs.net/doc/2b862219.html,/”，可利用EMBL的SRS系统进行蛋白质序列的检索。 通过EMAIL进行序列检索 当网络不是很畅通时或并不急于得到较多数量的蛋白质序列时，可采用EMAIL方式进行序列检索。 蛋白质基本性质分析 蛋白质序列的基本性质分析是蛋白质序列分析的基本方面，一般包括蛋白质的氨基酸组成，分子质量，等电点，亲水性，和疏水性、信号肽，跨膜区及结构功能域的分析等到。蛋白质的很多功能特征可直接由分析其序列而获得。例如，疏水性图谱可通知来预测跨膜螺旋。同时，也有很多短片段被细胞用来将目的蛋白质向特定细胞器进行转移的靶标（其中最典型的

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蛋白质分子是由氨基酸首尾相连而成的共价多肽链，天然蛋白质分子有自己特有的空间结构，称为蛋白质构象。 蛋白质结构的不同组织层次：一级结构指多肽链的氨基酸序列。二级结构是指多肽链借助氢键排列成特有的α螺旋和β折叠片段。三级结构是指多肽链借助各种非共价键弯曲、折叠成具有特定走向的紧密球状构象。球状构象给出最低的表面积和体积之比，因而使蛋白质与周围环境的相互作用降到最小。四级结构是指寡居蛋白质中各亚基之间在空间上的相互关系和结合方式。二、三、四级结构为蛋白质的高级结构。蛋白质的天然折叠结构决定于3个因素：1。与溶剂分子（一般是水）的相互作用。2。溶剂的PH值和离子组成。3。蛋白质的氨基酸序列。后一个是最重要的因素。 （一）蛋白质折叠的热力学假说 蛋白质的高级结构由其一级结构决定的学说最初由Christian B. Anfinsen于1954年提出。在1950年之前，Anfinsen一直从事蛋白质结构方面的研究。在进入美国国立卫生研究所（NIH）以后，继续从事这方面的研究。Anfinsen和两个博士后Michael Sela、 Fred White在研究中发现，使用高浓度的巯基试剂——β- 巯基乙醇（β- mercaptoethanol）可将二硫键还原成自由的巯基，如果再加入尿素，进一步破坏已被还原的核糖核酸酶分子内部的次级键，则该酶将去折叠转变成无任何活性的无规卷曲。对还原的核糖核酸酶的物理性质进行分析的结果清楚地表明了它的确采取的是无规卷曲的形状。 在成功得到一种去折叠的核糖核酸酶以后，Anfinsen 着手开始研究它的重折叠过程。考虑到被还原的核糖核酸酶要在已被还原的8个Cys残基上重建4对二硫键共有105 种不同的组合，但只有一种是正确的形式，如果决定蛋白质构象的信息一直存在于氨基酸序列之中，那么，最后重折叠得到的总是那种正确的形式。否则，重折叠将是随机的，最后只能得到少量的正确形式。Anfinsen 的重折叠实验还是比较顺利的，他通过透析的方法除去了导致酶去折叠的尿素和巯基乙醇，再将没有活性的酶转移到其生理缓冲溶液之中，在有氧气的情况下于室温放置，以使巯基能重新氧化成二硫键。经过一段时间以后，发现核糖核酸酶活性得以恢复，这意味着它原来的构象恢复了。由于上述过程没有细胞内任何其他成分的参与，完全是一种自发的过程，因此，有理由相信此蛋白质正确折叠所需要的所有信息全部存在于它的一级结构之中。在此基础上，Anfinsen提出了蛋白质折叠的热力学假说（thermodynamic hypothesis）。根据此假说，一个蛋白质的天然三维构象对应于在生理条件下其所处的热力学最稳定的状态。热力学稳定性由组成的氨基酸残基之间的相互作用决定，于是蛋白质的三维构象直接由它的一级结构决定。 （二）蛋白质高级结构对高级结构形成的影响 1．二级结构 蛋白质的二级结构由氢键维持。包括α螺旋、β折叠、β转角和无规卷等。α螺旋是一种重复性结构，螺旋中每个α-碳的Φ和Ψ分别为-57o和-47o附近。