BuildBeta—A system for automatically constructing beta sheets†
Nelson Max
Department of Computer Science, University of California, Davis, California 95616
Institute for Data Analysis and Visualization, University of California, Davis, California 95616
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
Search for more papers by this authorChengCheng Hu
Department of Computer Science, University of California, Davis, California 95616
Institute for Data Analysis and Visualization, University of California, Davis, California 95616
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
Search for more papers by this authorOliver Kreylos
Department of Computer Science, University of California, Davis, California 95616
Institute for Data Analysis and Visualization, University of California, Davis, California 95616
Search for more papers by this authorCorresponding Author
Silvia Crivelli
Department of Computer Science, University of California, Davis, California 95616
Institute for Data Analysis and Visualization, University of California, Davis, California 95616
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M/S 50F, Berkeley, CA 94720===Search for more papers by this authorNelson Max
Department of Computer Science, University of California, Davis, California 95616
Institute for Data Analysis and Visualization, University of California, Davis, California 95616
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
Search for more papers by this authorChengCheng Hu
Department of Computer Science, University of California, Davis, California 95616
Institute for Data Analysis and Visualization, University of California, Davis, California 95616
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
Search for more papers by this authorOliver Kreylos
Department of Computer Science, University of California, Davis, California 95616
Institute for Data Analysis and Visualization, University of California, Davis, California 95616
Search for more papers by this authorCorresponding Author
Silvia Crivelli
Department of Computer Science, University of California, Davis, California 95616
Institute for Data Analysis and Visualization, University of California, Davis, California 95616
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M/S 50F, Berkeley, CA 94720===Search for more papers by this authorThis article is a US government work and, as such, is in the public domain in the United States of America.
Abstract
We describe a method that can thoroughly sample a protein conformational space given the protein primary sequence of amino acids and secondary structure predictions. Specifically, we target proteins with β-sheets because they are particularly challenging for ab initio protein structure prediction because of the complexity of sampling long-range strand pairings. Using some basic packing principles, inverse kinematics (IK), and β-pairing scores, this method creates all possible β-sheet arrangements including those that have the correct packing of β-strands. It uses the IK algorithms of ProteinShop to move α-helices and β-strands as rigid bodies by rotating the dihedral angles in the coil regions. Our results show that our approach produces structures that are within 4–6 Å RMSD of the native one regardless of the protein size and β-sheet topology although this number may increase if the protein has long loops or complex α-helical regions. Proteins 2010. © Published 2009 Wiley-Liss, Inc.
Supporting Information
Additional Supporting Information may be found in the online version of this article.
| Filename | Description |
|---|---|
| PROT_22578_sm_suppinfo.doc49.5 KB | Supporting Information. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- 1 AnfinsenCB,HaberE,SelaM,WhiteFH,Jr. The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc Natl Acad Sci USA 1961; 47: 1309–1314.
- 2 BradleyP,MisuraKMS,BakerD. Toward high-resolution de novo structure prediction for small proteins. Science 2005; 309: 1868–1871.
- 3 RuczinskiI,KooperbergC,BonneauR,BakerD. Distributions of beta sheets in proteins with applications to structure prediction. Proteins: Struct Funct Genet 2002; 48: 85–97.
- 4 DasR,QianB,RamanS,VernonR,ThompsonJ,BradleyP,KhareS,TykaMD,BhatD,ChivianD,KimDE,ShefflerWH,MalmströmL,WollacottAM,WangC,AndreI,BakerD. Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home. Proteins 2007; 69: 118–128.
- 5 ZhouH,PanditSB,LeeS,BorreguerroJ,ChenH,WroblewskaL,SkolnickJ. Analysis of TASSER based CASP7 protein structure prediction results. Proteins 2007; 69: 90–97.
- 6 ZhangY. Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 2007; S8: 108–117.
- 7 KarplusK,KatzmanS,ShacklefordG,KoevaM,DraperJ,BarnesB,SorianoM,HugheyR. SAM-T04: what's new in protein-structure prediction for CASP6. Proteins 2005; 61: 135–142.
- 8 JonesDT,BrysonK,ColemanA,McGuffinLJ,SadowskiMI,SodhiJS,WardJJ. Prediction of novel and analogous folds using fragment assembly and fold recognition. Proteins 2005; 61: 143–151.
- 9 FangQ,ShortleD. Prediction of protein structure by emphasizing local side-chain/backbone interactions in ensembles of turn fragments. Proteins 2003; 53: 486–490.
- 10 BradleyP,BakerD. Improved beta-protein structure prediction by multilevel optimization of non-local strand pairings and local backbone conformation. Proteins 2006; 65: 922–929.
- 11 ZhuH,BraunW. Sequence specificity, statistical potentials, and three-dimensional structure prediction with self-correcting distance geometry calculations of beta-sheet formation in proteins. Protein Sci 1999; 8: 326–342.
- 12 McGuffinLJ,BrysonK,JonesDT. The PSIPRED protein structure prediction server. Bioinformatics 2000; 16: 404–405.
- 13 KarplusK,BarrettC,HugheyR. Hidden Markov models for detecting remote protein homologies. Bioinformatics 1998; 14: 846–856.
- 14 MeilerJ,MuellerM,ZeidlerA,SchmaeschkeF. JUFO: secondary structure prediction for proteins. Available at: http://www.jens-meiler.de.
- 15 RuczinskiI. Logic regression and statistical issues related to the protein folding problem, Ph.D. thesis, University of Washington, Seattle, WA, 2000, Chapter 9. Available at: http://biostat.jhsph.edu/∼iruczins/sheets/sheets.html.
- 16 CrivelliS,KreylosO,HamannB,MaxN,BethelW. ProteinShop: a tool for interactive protein manipulation and steering. J Comput-Aided Mol Des 2004; 18: 271–285.
- 17 CrivelliS,EskowE,BaderB,LambertiV,ByrdR,Head-GordonT. A physical approach to protein structure prediction. Biophys J 2003; 82: 36–49.
- 18 WelmanC. Inverse kinematics and geometric constraints for articulated figure manipulation, Master's Thesis, Simon Fraser University, Vancouver, Canada, 1993.
- 19 SalemmeFR,WeatherfordDW. Conformational and geometrical properties of beta-sheets in proteins. I. Parallel beta-sheets. J Mol Biol 1981; 146: 101–117.
- 20 SalemmeFR,WeatherfordDW. Conformational and geometrical properties of beta-sheets in proteins. II. Antiparallel and mixed beta-sheets. J Mol Biol 1981; 146: 101–117.
- 21 SalemmeFR. Conformational and geometrical properties of beta-sheets in proteins. III. Isotropically stressed configurations. J Mol Biol 1981; 146: 101–117.
- 22 KrivovGG,ShapovalovMV,DunbrackRL,Jr. Improved prediction of protein side-chain conformations with SCWRL4. Proteins: Struct Funct Bioinform doi: 10.1002/prot.22488.
- 23 RichardsonJ. Handedness of crossover connections in beta sheets. Proc Natl Acad Sci USA 1976; 73: 2619–2623.
- 24 MurzinAG,LeskAM,ChothiaC. Principles determining the structure of beta-sheet barrels in proteins. I. A theoretical analysis. J Mol Biol 1994; 236: 1369–1381.
- 25 MurzinAG,LeskAM,ChothiaC. Principles determining the structure of beta-sheet barrels in proteins. II. The observed structures. J Mol Biol 1994; 236: 1382–1400.
- 26 LastersI,WodakSJ,PioF. The design of idealized α/β-barrels: analysis of β-sheet closure requirements. Proteins: Struct Funct Genet 1990; 7: 249–256.
- 27 SotoCS,FasnachtM,ZhuJ,ForrestL,HonigB. Loop modeling: sampling, filtering and scoring. Proteins 2008; 70: 834–843.
- 28 FiserA,DoRK,SaliA. Modeling of loops in protein structures. Protein Sci 2000; 9: 1753–1773.
- 29Seventh Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction, Protein Structure Prediction Center, U. California, Davis, http://predictioncenter.org/casp7/targets/.
- 30 KreylosO,MaxN,HamannB,CrivelliS,BethelEW. Interactive protein manipulation. Proceedings of IEEE Visualization Conference, 2003. pp 581–588.
- 31 BonneauR,RuckzinskiI,TsaiJ,BakerD. Contact and ab initio protein structure prediction. Protein Sci 2002; 11: 1937–1944.
Citing Literature
