An ensemble approach for large-scale identification of proteinprotein interactions using the alignments of multiple sequences

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1 /, 217, Vol. 8, (No. 3), pp: An ensemble pproch for lrge-scle identifiction of proteinprotein interctions using the lignments of multiple sequences ei Wng 1,5,*, Zhu-Hong You 2,*, Xing Chen 3, Jin-Qing i 4, Xin Yn 6, Wei Zhng 5, Yu-An Hung 4 1 School of Computer Science nd Technology, Chin University of Mining nd Technology, Xuzhou , Chin 2 Xining Technicl Institute of Physics nd Chemistry, Chinese Acdemy of Science, Urumqi 8311, Chin 3 School of Informtion nd Electricl Engineering, Chin University of Mining nd Technology, Xuzhou , Chin 4 College of Computer Science nd Softwre Engineering, Shenzhen University, Shenzhen, Gungdong 5186, Chin 5 College of Informtion Science nd Engineering, Zozhung University, Zozhung, Shndong 2771, Chin 6 School of Foreign nguges, Zozhung University, Zozhung, Shndong 2771, Chin * Joint First Authors Correspondence to: Zhu-Hong You, emil: zhuhongyou@gmil.com Xing Chen, emil: xingchen@mss.c.cn Keywords: disese, position-specific scoring mtrix, multiple sequences lignments, cncer Received: October 11, 216 Accepted: November 15, 216 Published: December 22, 216 Reserch Pper ABSTRACT Protein Protein Interctions (PPI) is not only the criticl component of vrious biologicl processes in cells, but lso the key to understnd the mechnisms leding to helthy nd disesed sttes in orgnisms. However, it is time-consuming nd costintensive to identify the interctions mong proteins using biologicl experiments. Hence, how to develop more efficient computtionl method rpidly becme n ttrctive topic in the post-genomic er. In this pper, we propose novel method for inference of protein-protein interctions from protein mino cids sequences only. Specificlly, protein mino cids sequence is firstly trnsformed into Position- Specific Scoring Mtrix (PSSM) generted by multiple sequences lignments; then the Pseudo PSSM is used to extrct feture descriptors. Finlly, ensemble Rottion Forest (RF) lerning system is trined to predict nd recognize PPIs bsed solely on protein sequence feture. When performed the proposed method on the three benchmrk dt sets (Yest, H. pylori, nd independent dtset) for predicting PPIs, our method cn chieve good verge ccurcies of 98.38%, 89.75%, nd 96.25%, respectively. In order to further evlute the prediction performnce, we lso compre the proposed method with other methods using sme benchmrk dt sets. The experiment results demonstrte tht the proposed method consistently outperforms other stte-of-the-rt method. Therefore, our method is effective nd robust nd cn be tken s useful tool in exploring nd discovering new reltionships between proteins. A web server is mde publicly vilble t the UR for cdemic use. INTRODUCTION Protein Protein Interctions (PPIs) ply n importnt role in lmost every cellulr process [1, 2]. A vriety of biochemicl ctivities performed by PPIs re the foundtion of life, such s immune response, regultion of trnscription nd trnsltion, DNA repliction, nd endocrine function [3]. In recent decdes, in order to understnd the mechnisms of ll kinds of biochemicl ctivities, vriety of biologicl experimentl methods hve 5149 been designed to detect the interctions between proteins, for exmple, two-hybrid systems [4, 5], mss spectrometry [6, 7], immunoprecipittion [8], protein chip technology [9], etc. However, it is time-consuming, cost-intensive nd smll-scle to identify the interctions mong proteins using biologicl experiments only. Therefore, there is n urgent need to use computtionl methods to predict protein-protein interctions efficiently nd mssively. So fr, number of computtionl methods hve been proposed to predict protein-protein interctions. These

2 methods cn be roughly divided into three types: structurebsed methods [1 13], sequence-bsed methods [14 25] nd function-nnottion-bsed methods [26 29]. Among them, there is no need to know protein structure informtion nd pre-knowledge using the sequence-bsed pproches, which hs roused more nd more interests in reserchers. For exmple, Mrtin et l. developed computtionl model to identify the interctions mong proteins by using the signture descriptor [3]. This model chieved n ccurcy of 7% nd 8% when testing on the H. pylori nd Yest dt sets by 1-fold cross-vlidtion. Shen et l. proposed the conoint trid pproch to predict humn PPIs considering the locl environments of residues [16]. In the experiment, the ccurcy of this model reched 83.9%. Ahmd et l. proposed n lgorithm to predict the DNA-binding sites bsed on the neurl network, which dopted mino cid sequences evolutionry informtion in terms of their position specific-scoring mtrices [31]. In this pper, we propose novel sequence-bsed computtionl method for predicting potentil proteinprotein interctions. Specificlly, we first convert the protein mino cids sequence into the Position Specific Scoring Mtrix (PSSM) [32] tht contins the informtion of evolution; Then use the Pseudo Position-Specific Score Mtrix (PsePSSM) [33 35] lgorithm to extrct fetures expecting more informtion. Finlly, the Rottion Forest (RF) [36, 37] clssifier is pplied to determine whether the proteins re relted or not. In the experiment, the proposed method is implemented on the Yest dt set, nd the ccurcy of five-fold cross-vlidtion is 98%. At the sme time, we lso verified on the Helicobcter. pylori, C.elegns, E.coli, H.spiens nd M.musculus dt sets, nd yielded the ccurcy of 89.75%, 98.5%, 91.%, 97.45% nd 98.8%, respectively. In order to further evlute the prediction performnce, we lso compre the proposed method with other excellent methods. Comprison results show tht the proposed method consistently outperforms other stte-of-the-rt methods. RESUTS AND DISCUSSIONS Evlution mesures Four stndrd criteri re used to evlute the performnce of our pproch, including ccurcy (Accu.), sensitivity (Sen.), precision (Prec.) nd Mtthews correltion coefficient (MCC). MCC represents the correltion coefficient between the observed nd the predicted clss. It rnges from -1 (the best predictive model) to 1 (the worst predictive model). These mesures re defined s follows: MCC = TP + TN Accu. = (1) TP + TN + FP + FN Sen. TP = TP + FN (2) Prec. TP = TP + FP (3) TP TN FP FN (4) TP + FP TP + FN TN + FP ( TN + FN ) ( )( )( ) where TP denotes the number of positive smples to be correctly predicted; FP denotes the number of negtive smples to be incorrectly predicted; TN denotes the number of negtive smples to be correctly predicted; FN denotes the number of positive smples to be incorrectly predicted, respectively. In ddition, the receiver operting chrcteristic (ROC) [38] curve is used to ccess the performnce of clssifier. In the ROC curve, the defult Figure 1: Accurcy surfce obtined of rottion forest for optimizing regulriztion prmeters K nd. 515

3 Tble 1: 5-fold cross-vlidtion results obtined by using proposed method on Yest dt set Testing set Accu.(%) Prec.(%) Sen.(%) MCC(%) Averge 98.38± ± ± ±.66 threshold for the clssifier is.5. The threshold will be chnged with the true positive rte versus the flse positive rte when new set of prediction result is ccepted; this chnge will be expressed through grphics. Assessment of prediction bility In order to chieve the best performnce of the rottion forest, we use the grid serch method to dust the corresponding prmeters. In this study, PCA [36] ws chosen s rottion forest trnsformtion method nd the J48 decision tree [39] derived from the WEKA mchine lerning workbench ws selected s the bse clssifier. Figure 1 shows the ccurcy of the clssifier under different prmeter vlues. From the Figure 1 we cn see tht our method performs well, the verge prediction ccurcy is rpidly incresing with the increse of the vlue of t the beginning nd increse rte becomes slow when the vlue of is greter thn 5. However, the ccurcy lwys presents fluctution stte with the increse of the vlue of the prmeter K. After comprehensive ssessment, we choose the optiml prmeters of K=8 nd =5 ultimtely. In this pper, 5-fold cross-vlidtion technique is used s mens to evlute our model. More specificlly, the entire feture dt set is rndomly divided into five pproximtely equl subsets. Four of these subsets re used for trining nd the rest of the subset for testing. The cross-vlidtion process is repeted 5 times so tht ech dt set cn be used for testing once. Tble 1 lists the results of our predictions on Yest dt set, the vlue of verge ccurcy, precision, sensitivity, nd MCC re 98.38%, 99.92%, 96.84%, nd 96.82%, respectively. The prediction ccurcy of the five models re ll greter thn 98.17%, the precisions re greter thn 99.62%, the sensitivities re greter thn 96.32%, nd the MCC re greter thn 96.4%. The ROC curves performed on Yest dt set is shown in Figure 2. In this figure, X-ry depicts Figure 2: ROC curves performed by proposed method on Yest dt set. 5151

4 Tble 2: 5-fold cross-vlidtion results obtined by using proposed method on H. pylori dt set Testing set Accu.(%) Prec.(%) Sen.(%) MCC(%) Averge 89.75± ± ± ±2.69 flse positive rte (FPR) while y-ry depicts true positive rte (TPR). The performnce of the proposed method on the H. pylori dt set To better evlute the performnce of the proposed model in PPIs prediction, we focused on the testing of H. pylori dt set. We use the sme feture extrction method nd the sme RF prmeters to verify its effect, the results chieved s shown in Tble 2. On the H. pylori dt set we obtin the ccurcy of the 5 models re 92.45%, 88.16%, 9.5%, 89.37%, nd 88.7%, respectively. We cn see from Tble 2 tht the excellent prediction performnce of our model with n verge precision vlue of 89.75%, precision vlue of 9.18%, sensitivity vlue of 89.12%, nd MCC vlue of 81.62%. Additionlly, it cn lso be seen from Tble 2 tht the stndrd devition of ccurcy, precision, sensitivity nd MCC is s low s.167,.274,.183 nd.269. The ROC curves re shown in Figure 3. Comprison with previous method In recent yers, mny reserchers hve proposed vrious models to predict the PPIs nd chieved good results. In order to further evlute the prediction performnce, we compre the proposed method with these excellent methods in the sme benchmrk dt sets. In ddition, s the stte-of-the-rt clssifiction lgorithm, SVM hs been successfully used to predict PPIs. In this experiment, we lso compre the clssifiction performnce between Rottion Forest clssifier nd SVM clssifier on the Yest dt set. The corresponding Figure 3: ROC curves performed by the proposed method on H. pylori dt set. 5152

5 Tble 3: Performnce comprison of different models on Yest dt set Model Test set Accu.(%) Prec.(%) Sen.(%) MCC(%) Guos work [17] ACC 89.33± ± ±3.68 N/A AC 87.36± ± ±4.68 N/A Zhous work [4] SVM + D 88.56± ± ± ±.68 Yngs work [41] Cod1 75.8± ± ±1.2 N/A Cod2 8.4± ± ±.69 N/A Cod3 8.41± ± ±.9 N/A Cod ± ± ±1.74 N/A Yous work [42] PCA-EEM 87.± ± ± ±.44 Our method SVM+PSSM 95.19± ± ± ±.75 RF + PSSM 98.38± ± ± ±.66 Tble 4: Performnce comprison of different models on H. pylori dt set Model Accu.(%) Prec.(%) Sen.(%) MCC(%) Phylogentic bootstrp [43] N/A HKNN [44] N/A Signture products [3] N/A Ensemble of HKNN [45] N/A Boosting [46] Ensemble EM [42] Our method Tble 5: Prediction results on four species bsed on our model Species Test pirs Accu.(%) C.elegns E.coli H.spiens M.musculus prmeters of the SVM were selected by the grid serch method, nd finlly we set c=.1 nd g=.2, respectively. The IBSVM tools we dopted re downloded t Tble 3 nd Tble 4 summrize the results of these comprisons. Tble 3 shows the verge prediction results of the different models on the Yest dt set, we cn see tht the ccurcy obtined by other methods re between 75.8% nd 89.33%, the verge ccurcy obtined by our method is 98.38%. In the comprison of clssifiers, the ccurcy obtined on the rottion forest clssifier is higher thn those obtined on the support vector mchine clssifier. Tble 4 shows the performnce of different methods on the H. pylori dt sets. We cn see from the Tble 4 tht the ccurcies of the other six methods re 75.8%, 84.%, 83.4%, 86.6%, 79.52% nd 87.5%, while our method is 89.75%; the precisions of the other six methods re 8.2%, 84.%, 85.7%, 85.%, 81.69% nd 86.15%, while our method is 9.18%; the sensitivity of the other six methods re 69.8%, 86.%, 79.9%, 86.7%, 5153

6 Tble 6: The newly confirmed PPIs with high possibility in the Yest dt set Protein ID Protein ID The probbility of proteinprotein interctions Evidence DIP:1113N DIP:655N.9917 DIP sw:p29295 sw:p MINT sw:p4754 sw:p IntAct DIP:14N DIP:2463N.9891 DIP sw:p45 sw:p MINT DIP:288N DIP:6282N.9854 DIP DIP:148N DIP:6416N.9848 DIP DIP:1558N DIP:237N.9846 DIP DIP:537N DIP:799N.984 DIP sw:q12176 sw:q MINT, IntAct DIP:1364N DIP:2483N.9836 DIP DIP:1726N DIP:834N.9833 DIP DIP:2417N DIP:563N.9831 DIP sw:p18888 sw:p MINT, IntAct sw:q467 sw:p MINT, IntAct 8.37% nd 88.95%, while our method is 89.12%. The results obtined by these methods re significntly lower thn ours. Performnce on independent dt sets After completing the experiment on the Yest nd H. pylori dt sets, we continue to test the performnce of the proposed method on the independent dt sets (C.elegns, E.coli, H. spiens nd M.musculus). In the experiment, we tke ll the Yest dt set s trining set, independent dt sets s the test set to predict protein-protein interctions. Tble 5 lists the ccurcy of our method on four dt sets. It cn be seen from the tble tht the highest ccurcy of the proposed method is 98.5% on the C.elegns dt set, nd even the lowest ccurcy chieved on the E.coli dt Figure 4: The schemtic digrm of the prediction model. 5154

7 set reched 91.%. It is demonstrtes tht our method hs good ccurcy in predicting the interction of other species. Vlidte potentil protein-protein interctions from the PPIs dtbse After evluting the effectiveness of the proposed model by using the 5-fold cross vlidtion method, we here clculte the interction probbility for ll potentil protein-protein pirs in the dtsets of Yest. Specificlly, the whole negtive nd positive dt explored in 5-fold cross vlidtion experiments re used for trining nd ll the unknown protein-protein pirs re used s testing set. The predicted protein pirs with top-1 rnks in the potentil PPI lists re considered s highly potentil protein-protein interctions nd further verified by three public dtbses (i.e. DIP [47], MINT [48] nd IntAct [49]). These dtbses hve been supplemented by some newly detected protein-protein interctions since the gold stndrd dt explored in this study were collected in 27. All the predicted possibilities for top 1 potentil PPIs in Yest cn be obtined in Supplementry Tble S1. As shown in Tble 6, 15 new protein-protein interctions re finlly confirmed. Note tht the high-rnked interctions tht re not reported yet my lso exist in relity. Bsed on these results, we nticipte tht the proposed model is fesible to predict new protein-protein interctions. MATERIAS AND METHODS Dt sources We evlute our model focus on publicly vilble Scchromyces cerevisie dt set introduced by Guo et l. [17]. The PPIs dt were extrcted from Scchromyces cerevisie core subset of dtbse of intercting proteins (DIP) [47], version DIP_ Through the two lgorithms, prlogous verifiction method (PVM) nd expression profile relibility (EPR) [5], the core subset of relibility is tested. And less thn 5 residues of the protein of protein pirs re removed. In order to reduce pirwise sequence redundncy, multiple sequence lignment tool, CD-Hit [51, 52], ws dopted with threshold of 4% identity. Eventully the 5594 proteins re left to form the positive dt set. The negtive dtset consists of 5594 dditionl protein pirs, which re selected t different subcellulr locliztion. Therefore, the positive nd negtive dt set ech ccounted for hlf of the protein pirs constitute the finl dt set. As comprison, we further ssess the cpbilities of our model in the H. pylori dt set, which ws described by Rin et l. [53]. It cn be downloded t cs.sndi.gov/~smrtin/softwre.html. This dt set contins 2916 protein pirs which include hlf intercting pirs nd hlf non-intercting pirs. It provides pltform for compring different methods [3, 42, 43, 45, 46]. Position-specific scoring mtrix Position-Specific Scoring Mtrix (PSSM) is used to detect the distntly relted proteins, nd initilly introduced by Gribskov et l. [32]. It hs mde outstnding chievements in these res: protein secondry structure prediction [54], prediction of disordered regions [55], nd protein binding site prediction [56]. A PSSM is n 2 mtrix, which cn be denoted { } s PSSM = : i = 1 nd = 1 2, where i, denotes protein sequence length nd the number of 2 is due to 2 mino cids. Ech element PSSM (i, ) of the mtrix is defined s follows: PSSM = 1,1 1,2 1,2 2,1 2,2 2,2 M M M M,1,2,2 where i, in the i row of PSSM mens tht the probbility of the ith residue being mutted into type of 2 ntive mino cids during the procession of evolutionry in the protein from multiple sequence lignments. In order to extrct the evolutionry informtion, ech protein sequence in the dt set is used to lign nd serch homogenous sequences from SwissProt dtbse by the Position Specific Iterted BAST (PSI-BAST) [57] tool. PSI-BAST will return 2-dimensionl vector which indictes the probbilities of conservtion ginst muttions to 2 different mino cids including its own. To get brod nd high homologous sequences, we select in this study the vlue of e-vlue is.1 nd the vlue of itertions is 3, respectively. Applictions of PSI-BAST nd SwissProt dtbse cn be downloded t ncbi.nlm.nih.gov/blst.cgi. Pseudo position-specific score mtrix In order to reduce the probbility of missing sequence-order informtion, we introduced the concept of pseudo mino cid composition by Chou et l. [58]. In this rticle the smple of protein sequence PSSM is represented by Eqution 5 nd PsePSSM obtined from the following Eqution: i, = i, k= 1 ik, u= 1 iu, k= 1 ik, (5) 2 i=1 2, =1 2 (6) 5155

8 where i, represents the originl scores directly generted by the PSI-BAST, nd its vlue is typiclly positive integers or negtive integers. This is not wht we wnt stndrdized scores, which my hve zero mens if more thn 2 mino cids nd my remin unchnged if it continues through the sme conversion progrm. The positive score implies tht the corresponding muttion ppers more frequently in the lignment thn expected by chnce, nd the negtive score, on the contrry, implies tht the corresponding muttion ppers less frequently in the lignment thn expected by chnce. However, ccording to the definition of PSSM, different lengths of proteins will correspond to different rows number in mtrices. Eqution 7 is employed to express the protein smple PSSM, so tht the PSSM descriptor cn be represented s uniform pttern. nd P = PSSM (7) 1 = = 1..2 i= 1 i, where denotes the verge score when the mino cid residues in protein P in the process of running the lgorithm ws evolved into mino cid type. However, if only P PSSM is used to represent the protein P, ll the sequence informtion will be lost during evolution. In order to prevent the occurrence of missing ll informtion of sequence-order, the thought of pseudo mino cid ws introduced to improve the Eqution 7. Hence, bsed on the Eqution 9 segmented PsePSSM fetures cn be obtined: = 1 i= 1 i, = 1..2, ε = 1 ε 2 ( ) i= 1 i, i+ ε, ε = 1..2, ε < where is the correltion fctors of mino cid type. Although the vlue llowed for e cn be, 1, 2,, or 49, considering the time costs nd efficiency fctors, we took e to,1,2,3,4, so totl of 2-dimensionl vectors re eventully used in this study. Rottion forest Rottion Forest (RF) is novel proposed ensemble clssifier tht uses independently trined decision trees. The min ide of the Rottion Forest simultneously encourges individul ccurcy nd diversity within the ensemble. In order to generte the trining smples of the bse clssifier, the feture set is rndomly divided into K subsets. The liner trnsformtion method is pplied to ech subset, nd retins ll the principl components to (8) (9) mintin the precision of dt. The rottion formed the trining smple of new fetures to ensure the diversity of dt. Hence the rottion forest cn enhnce the ccurcy of individul clssifier nd the diversity in the ensemble t the sme time. Suppose tht { x, y i i} contins N trining smples, where xi = ( xi 1, xi2,, xid ) be D-dimensionl feture T vector, X=( x1, x2,, x n ) be the trining smple set (n D mtrix), which is composed of n observtion T feture vector composition, Y= ( y, y,, y ) be the 1 2 n corresponding lbels, nd S be the feture set. Assuming tht the number of decision trees in the rottion forest is, expressed s R, R,, R, respectively, nd the feture 1 2 set is rndomly divided into K subsets of equl size. The preprocessing steps for n individul clssifier is: the first select the pproprite prmeters K which is fctor of n, nd S rndomly divided into K disoint subsets, so the number of fetures contined in ech feture subset is C = n ; the second from the trining dtset X to select k the corresponding column of the feture in the subset R i,, form new mtrix X i,. Then the bootstrp subset of obects extrcts three-qurters the size of the dt set from ' X to construct new trining subset X i ; The third mtrix, ' X i is used s the feture trnsform for producing the, coefficients in mtrix M i,, which th column coefficient s the chrcteristic component th; nd the finl sprse rottion mtrix Mt i is formed, nd its coefficients in mtrix M i, is expressed s Eqution 1: Mti = (1),, ( C1 ) i,1 i,1 (1) C,, ( 2 ) i,2 i,2 M M M O (1) ( Ck ) ik,,, ik, In the prediction phse, given test smple x, let d ( XMt ) be the probbility produced by the clssifier i, i R i to the hypothesis in which x belongs to clss y i. Then the confidence for clss cn be computed ccording to the verge combined method shown in Eqution 11: µ ()= x 1 d XMt i= 1 i, i ( ) (11) Therefore, the test smple x esily ssigned to the clsses with the gretest possible. The schemtic digrm of the prediction model is shown in Figure 4. CONCUSION (1) In this pper, we proposed novel method to predict protein-protein interctions using the Rottion Forest 5156

9 combine with Pseudo Position-Specific Score Mtrix. In order to preserve s much informtion s possible, we first convert the protein mino cids sequences into the PSSM mtrix, nd then extrct the fetures using the PsePSSM lgorithm, finlly determine whether there is n interction between protein pirs through the RF clssifier. To evlute the performnce of the proposed method, we implement it on the Yest, H. pylori nd independent dt sets. In ddition, we lso compre the proposed method with other excellent methods. Excellent experimentl results demonstrte tht the proposed method is fesible nd effective in the prediction of protein interctions. The low stndrd devition of these criterion vlues indictes tht our method is stble nd robust. In future studies, we will focus on improving the clssifiction lgorithm to expect higher predictive ccurcy nd less time consumption in predicting protein-protein interctions. WEBSERVER In order to fcilitte the use of reserchers, we hve built web server to implement the proposed prediction model. The web server provides the source code nd the Yest dt sets used in this rticle for users to downlod. It cn be ccessed to t PsePSSM/. Users cn query the predicted results of the Yest dt sets through the webpge nd receive the predict results by e-mil. ACKNOWEDGMENTS This work is supported in prt by the Ntionl Nturl Science of Foundtion of Chin, under Grnt No , , , , in prt by the Reserch Innovtion Progrm for College Grdutes of Jingsu Province, under Grnt No. KYX16_535, in prt by the Pioneer Hundred Tlents Progrm of Chinese Acdemy of Sciences, nd in prt by the Nturl Science foundtion of Gungdong Province, under Grnt No. 214A , nd in prt by the Technology Plnning Proect from Gungdong Province, under Grnt No. 214B CONFICTS OF INTEREST The uthors declre no conflicts of interest. Author contributions W nd ZHY conceived the lgorithm, crried out nlyses, prepred the dt sets, crried out experiments, nd wrote the mnuscript; XC, JQ, XY, WZ nd YH designed, performed nd nlyzed experiments nd wrote the mnuscript; All uthors red nd pproved the finl mnuscript. REFERENCES 1. Brun P, Gingrs A-C. History of protein-protein interctions: From egg-white to complex networks. Proteomics. 212; 12: Ehrenberger T, Cntley C, Yffe MB. Computtionl prediction of protein-protein interctions. Methods in moleculr biology (Clifton, NJ). 215; 1278: Alon U. Biologicl networks: The tinkerer s n engineer. Science. 23; 31: Fields S, Song O. A novel genetic system to detect proteinprotein interctions. Nture. 1989; 34: Ito T, Chib T, Ozw R, Yoshid M, Httori M, Skki Y. A comprehensive two-hybrid nlysis to explore the yest protein interctome. Proceedings of the Ntionl Acdemy of Sciences of the United Sttes of Americ. 21; 98: Gvin AC, Bosche M, Kruse R, Grndi P, Mrzioch M, Buer A, Schultz J, Rick JM, Michon AM, Crucit CM, Remor M, Hofert C, Schelder M, Brenovic M, Ruffner H, Merino A, et l. Functionl orgniztion of the yest proteome by systemtic nlysis of protein complexes. Nture. 22; 415: Ho Y, Gruhler A, Heilbut A, Bder GD, Moore, Adms S, Millr A, Tylor P, Bennett K, Boutilier K, Yng Y, Wolting C, Donldson I, Schndorff S, Shewnrne J, Vo M, et l. Systemtic identifiction of protein complexes in Scchromyces cerevisie by mss spectrometry. Nture. 22; 415: Willims NE. Immunoprecipittion procedures. Methods in Cell Biology, Vol 62. 2; 62: Zhu H, Bilgin M, Bnghm R, Hll D, Csmyor A, Bertone P, n N, Jnsen R, Bidlingmier S, Houfek T, Mitchell T, Miller P, Den RA, Gerstein M, Snyder M. Globl nlysis of protein ctivities using proteome chips. Science. 21; 293: Aloy P, Russell RB. Interrogting protein interction networks through structurl biology. Proceedings of the Ntionl Acdemy of Sciences of the United Sttes of Americ. 22; 99: Aloy P, Russell RB. InterPreTS: protein Interction Prediction through Tertiry Structure. Bioinformtics. 23; 19: Bock JR, Gough DA. Predicting protein-protein interctions from primry structure. Bioinformtics. 21; 17: Zhng QC, Petrey D, Deng, Qing, Shi Y, Thu CA, Bisikirsk B, efebvre C, Accili D, Hunter T, Mnitis T, Clifno A, Honig B. Structure-bsed prediction of proteinprotein interctions on genome-wide scle. Nture. 212; 49: Hung TW, Tien AC, ee YCG, Hung WS, Peng C, Tseng HH, Ko CY, Hung CYF. POINT: dtbse for the prediction of protein-protein interctions bsed 5157

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