Team:NYMU-Taipei/modelling-protein-structure-champ-design

From 2011.igem.org

(Difference between revisions)
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    <p id="n1288">&nbsp; </p>
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<div class="clearfix grpelem" id="n1156"><!-- group -->
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    <p id="n1290">References</p>
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      <div class="grpelem" id="n1157"><!-- content -->
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    <p>&nbsp; </p>
+
      <p id="n1159">CHAMP Protein Modelling</p>
-
    <p id="n1294">* Hang Yin, Joanna S. Slusky, Bryan W. Berger, Robin S. Walters, Gaston Vilaire, Rustem I. Litvinov, James D. Lear, Gregory A. Caputo, Joel S. Bennett, William F. DeGrado (2007). Supporting Online Material for Computational Design of Peptides That Target Transmembrane Helices. Science 315, 1817.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1296">&nbsp; <span id="n1297"></span></p>
+
      <p>CHAMP, the computed helical anti&#45;membrane protein, is one of the computational and genetic methods available to engineer antibody&#45;like molecules that target the water&#45;soluble regions of tansmembrane (TM) proteins. (Hang Yin, et al., 2007)</p>
-
    <p id="n1298">*J. S. Slusky, H. Yin and W. F. DeGrado (2009). Understanding Membrane Proteins. How to Design Inhibitors of Transmembrane Protein—Protein Interactions. PROTEIN ENGINEERING Nucleic Acids and Molecular Biology 22, 315&#45;337.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1300">&nbsp; <span id="n1301"></span></p>
+
      <p>&nbsp;  </p>
-
    <p id="n1302">* Lo A., Chiu Y.Y., Rødland E.A., Lyu P.C., Sung T.Y., and Hsu W.L. (2009) Predicting helix&#45;helix interactions from residue contacts in membrane proteins. Bioinformatics 25, 996&#45;1003.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1304">&nbsp; <span id="n1305"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1306">* Lukasz Jaroszewski, Zhanwen Li, Xiao&#45;hui Cai, Christoph Weber, and Adam Godzik. (2011) FFAS server: novel features and applications. Nucleic Acids Res. 39, W38–W44.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1308">&nbsp; <span id="n1309"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1310">*Krieger E, Vriend G. (2002) Models@Home: distributed computing in bioinformatics using a screensaver based approach. Bioinformatics 18(2), 315&#45;8.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1312">&nbsp; <span id="n1313"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1314">*Krieger E, Koraimann G, Vriend G. (2002) Increasing the precision of comparative models with YASARA NOVA&#45;&#45;a self&#45;parameterizing force field. Proteins 47(3):393&#45;402.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1316">&nbsp; <span id="n1317"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1318">*Krieger E, Nabuurs SB, Vriend G. (2003) Homology modeling. Methods Biochem Anal.44:509&#45;23.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1320">&nbsp; <span id="n1321"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1322">*Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, Tyka M, Baker D, Karplus K. (2009) Improving physical realism, stereochemistry, and side&#45;chain accuracy in homology modeling: Four approaches that performed well in CASP8. Protein 77 Suppl 9:114&#45;22.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1324">&nbsp; <span id="n1325"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1326">*Venselaar H, Joosten RP, Vroling B, Baakman CA, Hekkelman ML, Krieger E, Vriend G. (2010) Homology modelling and spectroscopy, a never&#45;ending love story. Eur Biophys J. 39(4):551&#45;63.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1328">&nbsp; <span id="n1329"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1330">*Joosten RP, te Beek TA, Krieger E, Hekkelman ML, Hooft RW, Schneider R, Sander C, Vriend G. (2011) A series of PDB related databases for everyday needs. Nucleic Acids Res. 39(Database issue):D411&#45;9.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1332">&nbsp; <span id="n1333"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1334">*William Humphrey, Andrew Dalke, and Klaus Schulten. (1996) VMD – Visual Molecular Dynamics. Journal of Molecular Graphics 14:33&#45;38.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1336">&nbsp; <span id="n1337"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1338">* R. Sharma, T. S. Huang, V. I. Pavlovic, K. Schulten, A. Dalke, J. Phillips, M. Zeller, W. Humphrey, Y. Zhao, Z. Lo, and S. Chu. (1996) Speech/gesture interface to a visual computing environment for molecular biologists. In Proceedings of 13th ICPR 96, volume 3, pp. 964&#45;968.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1340">&nbsp; <span id="n1341"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1342">* Rajeev Sharma, Michael Zeller, Vladimir I. Pavlovic, Thomas S. Huang, Zion Lo, Stephen Chu, Yunxin Zhao, James C. Phillips, and Klaus Schulten. (2000) Speech/gesture interface to a visual&#45;computing environment.&nbsp; IEEE Computer Graphics and Applications 20:29&#45;37.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1344">&nbsp; <span id="n1345"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1346">* David J. Hardy, John E. Stone, and Klaus Schulten. Multilevel summation of electrostatic potentials using graphics processing units. Journal of Parallel Computing 35:164&#45;177.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1348">&nbsp; <span id="n1349"></span></p>
+
      <p id="n1215">&nbsp; </p>
-
    <p id="n1350">* Hessa, T., Meindl&#45;Beinker, N., Bernsel, A., Kim, J., Sato, Y., Lerch, M., Lundin, C., Nilsson, I., White, SH. and von Heijne, G. (2007) Molecular code for transmembrane&#45;helix recognition by the Sec61 translocon. Nature. 450, 1026&#45;1030.</p>
+
      <p id="n1217">Why We Use the CHAMP Design?</p>
-
    <p id="n1352">&nbsp; <span id="n1353"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1354">* Hessa, T., Kim, H., Lundin, C., Boekel, J., Andersson, H., Nilsson, I., White, SH. and von Heijne, G. (2005) Recognition of transmembrane helices by the endoplasmic reticulum translocon. Nature 433, 377&#45;381.</p>
+
      <p id="n1221">The general purpose of CHAMP design is that though Transmembrane(TM) helices usually play essential roles in biological processes, companion methods to target the TM regions are lacking. The CHAMP design of TM helices that specifically recognize membrane proteins would advance the understanding of sequence&#45;specific recognition in membranes and simultaneously would provide new approaches to modulate protein&#45;protein interactions in membranes. (Hang Yin, et al., 2007)</p>
-
    <p id="n1356">&nbsp; <span id="n1357"></span></p>
+
      <p id="n1223">&nbsp; </p>
-
    <p id="n1358">* Dieter Langosch, Jana R. Herrmann, Stephanie Unterreitmeier and Angelika Fuchs. (2009) Structural Bioinformatics of Membrane Proteins&#45; Helix&#45;helix interaction patterns in membrane proteins. </p>
+
      <p id="n1225">Now we want to let the CHAMP design play a pivotal role in our project to modulate the protein&#45;protein interactions in membranes. But why is the CHAMP design so important in our iGEM project this year? In this year's [[link here|Optomagnetic design]], we want to use the designed peptide, CHAMP sequence, to inhibit the tight interaction between the two helices of membrane protein Mms13. Then we can successfully use mechanical force to change the conformation of Mms13 to induce the BiFC&#45;based BRET phenomenon. If we do not have the target peptides, CHAMP, the two helices of Mms13 would tightly &quot;stick&quot; together and we can predict the results easily by the fundamental knowledge of physics that the magnetic force applied to the bacteria would not make any change to the conformation of Mms13 protein. If the two helices of Mms13 have tight interaction all the time, the wobbling light we expect in our project derived from BiFC&#45;based BRET would not be excited. As the consequence of lacking the modulation of CHAMP, we would not get our final wobbling fluorescence but the constant light derived from a luciferase reaction; which means that we can only “turn on” neurons with constant light while the wobbling system can excite or inhibit the neurons for both “on and off” functions.</p>
-
    <p id="n1360">&nbsp; <span id="n1361"></span></p>
+
      <p>&nbsp; </p>
-
     <p id="n1362">* Dieter Langosch, Isaiah T. Arkin. (2009) Interaction and conformational dynamics of membrane&#45;spanning protein helices. Protein Science 18: 1343–1358.</p>
+
      <p>&nbsp; </p>
-
     <p id="n1364">&nbsp; <span id="n1365"></span></p>
+
      <p>&nbsp; </p>
-
    <p id="n1366">* Hirokawa T., Boon&#45;Chieng S., and Mitaku S. (1998) SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics, 14:378&#45;9.</p>
+
      <p id="n1233">&nbsp; </p>
-
     <p id="n1368">&nbsp; <span id="n1369"></span></p>
+
      </div>
-
    <p id="n1370">*Mitaku S., Hirokawa T. (1999) Physicochemical factors for discriminating between soluble and membrane proteins: hydrophobicity of helical segments and protein length. Protein Eng. 11.</p>
+
      <div class="grpelem" id="n1235"><!-- image -->
-
     <p id="n1372">&nbsp; <span id="n1373"></span></p>
+
      <img id="n1235_img" src="image/f1.png" alt="" width="824" height="333"/>
-
    <p id="n1374">* Mitaku S., Hirokawa T., and Tsuji T. (2002) Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane&#45;water interfaces. Bioinformatics, 18:608&#45;16.</p>
+
      </div>
-
     <p id="n1376">&nbsp; <span id="n1377"></span></p>
+
    </div>
-
     <p id="n1378">*M. Cserzo, E. Wallin, I. Simon, G. von Heijne and A. (1997) Elofsson: Prediction of transmembrane alpha&#45;helices in procariotic membrane proteins: the Dense Alignment Surface method. Prot. Eng. vol. 10, no. 6: 673&#45;676.</p>
+
     <div class="grpelem" id="n1237"><!-- rasterized frame -->
-
     <p id="n1380">&nbsp; <span id="n1381"></span></p>
+
      <img id="n1237_img" src="image/modelling-protein-structure-champ-design_n1237.png" alt="" width="1" height="1"/>
-
    <p id="n1382">*Krogh A, Larsson B, von Heijne G, Sonnhammer EL. (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 305(3):567&#45;80.</p>
+
    </div>
-
    <p id="n1384">&nbsp; <span id="n1385"></span></p>
+
     <div class="clearfix grpelem" id="n1238"><!-- content -->
-
    <p id="n1386">*Laszlo Kajan, Yachdav, G., Burkhard, R. (2011). High&#45;throughput protein feature prediction in the cloud. Bioinformatics (submitted).</p>
+
      <p id="n1240">Fig. 1a: Close&#45;up of the predicted tightly packed interface between designed peptides and target protein. The target protein is represented by a red surface with a blue “hot spot”. Fig. 1b: The CHAMP backbone is depicted in ribbon representation with key positions designated for computational design shown in green (Hang Yin, et al., 2007)</p>
-
    <p id="n1388">&nbsp; <span id="n1389"></span></p>
+
     </div>
-
     <p id="n1390">*K. Hofmann &amp; W. Stoffel. (1993)TMbase &#45; A database of membrane spanning proteins segments. Biol. Chem. Hoppe&#45;Seyler:374,166.</p>
+
    <div class="grpelem" id="n1242"><!-- image -->
-
    <p id="n1392">&nbsp; <span id="n1393"></span></p>
+
      <img id="n1242_img" src="image/f2.jpg" alt="" width="310" height="296"/>
-
     <p id="n1394">*G.E Tusnády and I. Simon (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics 17:849&#45;850.</p>
+
    </div>
-
    <p id="n1396">&nbsp; <span id="n1397"></span></p>
+
     <div class="clearfix grpelem" id="n1244"><!-- content -->
-
     <p id="n1398">* Nugent, T. &amp; Jones, D.T. (2009) Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics. 10:159. Epub.</p>
+
      <p id="n1246">Fig. 2: The figure in the left is our initial design by using BRET phenomenon to generate the light. As for the right one, we make a little change using BiFC&#45;based BRET with CHAMP, our designed peptides to make the light wobbling and induce the on&#45;and&#45;off neural control system.</p>
-
     <p id="n1400">&nbsp; <span id="n1401"></span></p>
+
     </div>
-
    <p id="n1402">*Jones DT. (2007) Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics. 23: 538&#45;544.</p>
+
    <div class="grpelem" id="n1248"><!-- image -->
-
     <p id="n1404">&nbsp; <span id="n1405"></span></p>
+
      <img id="n1248_img" src="image/f6.png" alt="" width="299" height="547"/>
-
    <p id="n1406">*Jones DT, Taylor WR, Thornton JM. (1994) A Model Recognition Approach to the Prediction of All&#45;Helical Membrane Protein Structure and Topology. Biochem. 33: 3038&#45;3049.</p>
+
    </div>
-
     <p id="n1408">&nbsp; <span id="n1409"></span></p>
+
     <div class="clearfix grpelem" id="n1250"><!-- content -->
-
    <p id="n1410">* Gunnar von Heijne. (1992) Membrane Protein Structure Prediction, Hydrophobicity Analysis and the Positive&#45;inside Rule. J. Mol. Biol. 225:487&#45;494.</p>
+
      <p>Fig. 6: The critical G&#45;X3&#45;G motifs on helix1 of the Mms13</p>
-
    <p id="n1412">&nbsp; <span id="n1413"></span></p>
+
     </div>
-
    <p id="n1414">* William P Russ, Donald M Engelman. (2000) The GxxxG motif: A framework for transmembrane helix&#45;helix association. Journal of Molecular Biology 296, Issue 3: 911&#45;919</p>
+
    <div class="clearfix grpelem" id="n1254"><!-- content -->
-
    <p id="n1416">* Alessandro Senes, Iban Ubarretxena&#45;Belandia, and Donald M. Engelman. (2001) The Cα—H⋅⋅⋅O hydrogen bond: A determinant of stability and specificity in transmembrane helix interactions. Proc Natl Acad Sci USA.31; 98(16): 9056–9061.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1418">&nbsp; <span id="n1419"></span></p>
+
      <p id="n1258">Selection of a nativelike helical&#45;pair backbone within the chosen structural motif</p>
-
    <p id="n1420">* R. F. S. Walters and W. F. DeGrado. (2006) Helix&#45;packing motifs in membrane proteins. PNAS vol. 103 no. 37:13658&#45;13663.</p>
+
      <p>&nbsp; </p>
-
    <p id="n1422">&nbsp; <span id="n1423"></span></p>
+
      <p id="n1262">After the selection of a helical&#45;pair structural motif, we have to select a nativelike helical&#45;pair backbone as the templates for our CHAMP design. First, we follow the instructions to determine the template cluster. (R. F. S. Walters, et al., 2006) A library of 445 helical pairs from 31 proteins in the paper was clustered into groups based on their three&#45;dimensional similarity. Different clusters have specific features and also show varying degrees of homogeneity. We finally choose cluster 2 as our candidates because of the antiparallel helices on Mms13 and the traits which belong to right&#45;handed crossing angle on helix1. (For examples, the helix1 have small residues at the helix–helix interface, and they are also spaced at four&#45;residue intervals.) The pie chart (See Figure 7) shows the fraction of the total number of pairs that fall within a given cluster and Table 1 (See Figure 8) shows some characteristics of the top 14 clusters.</p>
-
    <p id="n1424">* Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, Tyka M, Baker D, Karplus K Proteins. (2009) Improving physical realism, stereochemistry, and side&#45;chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins.77 Suppl 9:114&#45;22.</p>
+
      <p>&nbsp; </p>
 +
    </div>
 +
     <div class="grpelem" id="n1266"><!-- image -->
 +
      <img id="n1266_img" src="image/f7.png" alt="" width="297" height="296"/>
 +
    </div>
 +
     <div class="grpelem" id="n1268"><!-- image -->
 +
      <img id="n1268_img" src="image/f8.png" alt="" width="679" height="291"/>
 +
     </div>
 +
     <div class="clearfix grpelem" id="n1270"><!-- content -->
 +
      <p id="n1272">Fig. 11a: The whole 1iwg protein. Figure 11b: The selected template pairs of 1iwg. Figures are graphed using YASARA View (Krieger E., et al., 2011)</p>
 +
     </div>
 +
    <div class="clearfix grpelem" id="n1274"><!-- content -->
 +
      <p id="n1276">Fig. 19: Parts of cross&#45;ranking results.</p>
 +
     </div>
 +
    <div class="clearfix grpelem" id="n1278"><!-- content -->
 +
      <p id="n1280">How to Design the CHAMP Peptides?</p>
 +
      <p>&nbsp; </p>
 +
      <p id="n1284">We followed the design protocols in the paper&#45; ''Supporting Online Material for Computational Design of Peptides That Target Transmembrane Helices'' (Hang Yin, et al., 2007) and &quot;Understanding Membrane Proteins. How to Design Inhibitors of Transmembrane Protein–Protein Interactions&quot; (J.S. Slusky, et al., 2009). However, because of the limited resources, we made little adjustments by using different programs, such as TMhit (Lo A., et al., 2009), ProtMod (Godzik A., et al., 2011), YASARA (Krieger E., et al., 2011), VMD (Klaus Schulten., et al., 2011), ΔG prediction server v1.0 (Hessa, T., et al., 2007) to finalize our CHAMP design. The study by &quot;Interaction and conformational dynamics of membrane&#45;spanning protein helices'&quot; (D Langosch, et al., 2009) and &quot;Helix&#45;helix interaction patterns in membrane proteins&quot; (D Langosch, et al., 2010) gave us more knowledge about protein&#45;protein interactions in transmembrane domains, and helped us during the CHAMP designing process. </p>
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Revision as of 10:02, 4 October 2011

Modelling Protein Structure: CHAMP design

CHAMP Protein Modelling

 

CHAMP, the computed helical anti-membrane protein, is one of the computational and genetic methods available to engineer antibody-like molecules that target the water-soluble regions of tansmembrane (TM) proteins. (Hang Yin, et al., 2007)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Why We Use the CHAMP Design?

 

The general purpose of CHAMP design is that though Transmembrane(TM) helices usually play essential roles in biological processes, companion methods to target the TM regions are lacking. The CHAMP design of TM helices that specifically recognize membrane proteins would advance the understanding of sequence-specific recognition in membranes and simultaneously would provide new approaches to modulate protein-protein interactions in membranes. (Hang Yin, et al., 2007)

 

Now we want to let the CHAMP design play a pivotal role in our project to modulate the protein-protein interactions in membranes. But why is the CHAMP design so important in our iGEM project this year? In this year's [[link here|Optomagnetic design]], we want to use the designed peptide, CHAMP sequence, to inhibit the tight interaction between the two helices of membrane protein Mms13. Then we can successfully use mechanical force to change the conformation of Mms13 to induce the BiFC-based BRET phenomenon. If we do not have the target peptides, CHAMP, the two helices of Mms13 would tightly "stick" together and we can predict the results easily by the fundamental knowledge of physics that the magnetic force applied to the bacteria would not make any change to the conformation of Mms13 protein. If the two helices of Mms13 have tight interaction all the time, the wobbling light we expect in our project derived from BiFC-based BRET would not be excited. As the consequence of lacking the modulation of CHAMP, we would not get our final wobbling fluorescence but the constant light derived from a luciferase reaction; which means that we can only “turn on” neurons with constant light while the wobbling system can excite or inhibit the neurons for both “on and off” functions.

 

 

 

 

Fig. 1a: Close-up of the predicted tightly packed interface between designed peptides and target protein. The target protein is represented by a red surface with a blue “hot spot”. Fig. 1b: The CHAMP backbone is depicted in ribbon representation with key positions designated for computational design shown in green (Hang Yin, et al., 2007)

Fig. 2: The figure in the left is our initial design by using BRET phenomenon to generate the light. As for the right one, we make a little change using BiFC-based BRET with CHAMP, our designed peptides to make the light wobbling and induce the on-and-off neural control system.

Fig. 6: The critical G-X3-G motifs on helix1 of the Mms13

 

Selection of a nativelike helical-pair backbone within the chosen structural motif

 

After the selection of a helical-pair structural motif, we have to select a nativelike helical-pair backbone as the templates for our CHAMP design. First, we follow the instructions to determine the template cluster. (R. F. S. Walters, et al., 2006) A library of 445 helical pairs from 31 proteins in the paper was clustered into groups based on their three-dimensional similarity. Different clusters have specific features and also show varying degrees of homogeneity. We finally choose cluster 2 as our candidates because of the antiparallel helices on Mms13 and the traits which belong to right-handed crossing angle on helix1. (For examples, the helix1 have small residues at the helix–helix interface, and they are also spaced at four-residue intervals.) The pie chart (See Figure 7) shows the fraction of the total number of pairs that fall within a given cluster and Table 1 (See Figure 8) shows some characteristics of the top 14 clusters.

 

Fig. 11a: The whole 1iwg protein. Figure 11b: The selected template pairs of 1iwg. Figures are graphed using YASARA View (Krieger E., et al., 2011)

Fig. 19: Parts of cross-ranking results.

How to Design the CHAMP Peptides?

 

We followed the design protocols in the paper- ''Supporting Online Material for Computational Design of Peptides That Target Transmembrane Helices'' (Hang Yin, et al., 2007) and "Understanding Membrane Proteins. How to Design Inhibitors of Transmembrane Protein–Protein Interactions" (J.S. Slusky, et al., 2009). However, because of the limited resources, we made little adjustments by using different programs, such as TMhit (Lo A., et al., 2009), ProtMod (Godzik A., et al., 2011), YASARA (Krieger E., et al., 2011), VMD (Klaus Schulten., et al., 2011), ΔG prediction server v1.0 (Hessa, T., et al., 2007) to finalize our CHAMP design. The study by "Interaction and conformational dynamics of membrane-spanning protein helices'" (D Langosch, et al., 2009) and "Helix-helix interaction patterns in membrane proteins" (D Langosch, et al., 2010) gave us more knowledge about protein-protein interactions in transmembrane domains, and helped us during the CHAMP designing process.