Native structure-based modeling and simulation of biomolecular systems per mouse click

BMC Bioinformatics

Benjamin Lutz²  Claude Sinner²  Stefan Bozic¹  Ivan Kondov¹  Alexander Schug¹ 

[1] Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

[2] Department of Physics, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

关键词: GridBeans;    Molecular dynamics;    Native structure-based model;    RNA folding;    Protein folding;   

Others  :  1086285
DOI  :  10.1186/1471-2105-15-292

 received in 2014-06-02, accepted in 2014-08-22,  发布年份 2014

【 摘 要 】

Background

Molecular dynamics (MD) simulations provide valuable insight into biomolecular systems at the atomic level. Notwithstanding the ever-increasing power of high performance computers current MD simulations face several challenges: the fastest atomic movements require time steps of a few femtoseconds which are small compared to biomolecular relevant timescales of milliseconds or even seconds for large conformational motions. At the same time, scalability to a large number of cores is limited mostly due to long-range interactions. An appealing alternative to atomic-level simulations is coarse-graining the resolution of the system or reducing the complexity of the Hamiltonian to improve sampling while decreasing computational costs. Native structure-based models, also called Gō-type models, are based on energy landscape theory and the principle of minimal frustration. They have been tremendously successful in explaining fundamental questions of, e.g., protein folding, RNA folding or protein function. At the same time, they are computationally sufficiently inexpensive to run complex simulations on smaller computing systems or even commodity hardware. Still, their setup and evaluation is quite complex even though sophisticated software packages support their realization.

Results

Here, we establish an efficient infrastructure for native structure-based models to support the community and enable high-throughput simulations on remote computing resources via GridBeans and UNICORE middleware. This infrastructure organizes the setup of such simulations resulting in increased comparability of simulation results. At the same time, complete workflows for advanced simulation protocols can be established and managed on remote resources by a graphical interface which increases reusability of protocols and additionally lowers the entry barrier into such simulations for, e.g., experimental scientists who want to compare their results against simulations. We demonstrate the power of this approach by illustrating it for protein folding simulations for a range of proteins.

Conclusions

We present software enhancing the entire workflow for native structure-based simulations including exception-handling and evaluations. Extending the capability and improving the accessibility of existing simulation packages the software goes beyond the state of the art in the domain of biomolecular simulations. Thus we expect that it will stimulate more individuals from the community to employ more confidently modeling in their research.

【 授权许可】

   
2014 Lutz et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The protein data bank. Nucleic Acids Res 2000, 28:235-242. [http://dx.doi.org/10.1093/nar/28.1.235 webcite]
• [2]Whitford PC, Geggier P, Altman RB, Blanchard SC, Onuchic JN, Sanbonmatsu KY: Accommodation of aminoacyl-tRNA into the ribosome involves reversible excursions along multiple pathways. RNA 2010, 16(6):1196-1204.
• [3]Bock LV, Blau C, Schröder GF, Davydov II, Fischer N, Stark H, Rodnina MV, Vaiana AC, Grubmüller H: Energy barriers and driving forces in tRNA translocation through the ribosome. Nat Struct Mol Biol 2013, 20:1390-1396. [http://dx.doi.org/10.1038/nsmb.2690 webcite]
• [4]Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan Y, Wriggers W: Atomic-level characterization of the structural dynamics of proteins. Science 2010, 330(6002):341-346.
• [5]Voth GA (Ed): Coarse-Graining of Condensed Phase and Biomolecular Systems. CRC Press; 2008.
• [6]Schug A, Hyeon C, Onuchic JN: Coarse-grained structure-based simulations of proteins and RNA. In Coarse-Graining of Condensed Phase and Biomolecular Systems. Voth GA. Boca Raton: CRC Press Taylor & Francis, Group; 2008:123-140. Chapter 9
• [7]de Jong DH, Singh G, Bennett WFD, Arnarez C, Wassenaar TA, Schäfer LV, Periole X, Tieleman DP, Marrink SJ: Improved parameters for the martini coarse-grained protein force field. J Chem Theor Comput 2013, 9:687-697. [http://dx.doi.org/10.1021/ct300646g webcite]
• [8]Onuchic JN, Wolynes PG: Theory of protein folding. Curr Opin Struct Biol 2004, 14:70-75. [http://www.sciencedirect.com/science/article/pii/S0959440X04000107 webcite]
• [9]Noel JK, Onuchic JN: The many faces of structure-based potentials: from protein folding landscapes to structural characterization of complex biomolecules. In Computational Modeling of Biological Systems, Biological and Medical Physics, Biomedical Engineering. Edited by Dokholyan NV. US: Springer; 2012:31-54. [http://dx.doi.org/10.1007/978-1-4614-2146-7_2 webcite]
• [10]Whitford PC, Sanbonmatsu KY, Onuchic JN: Biomolecular dynamics order-disorder transitions and energy landscapes. Rep Progr Phys 2012, 75(7):076601. [http://stacks.iop.org/0034-4885/75/i=7/a=076601 webcite]
• [11]Schug A, Onuchic JN: From protein folding to protein function and biomolecular binding by energy landscape theory. Curr Opin Pharmacol 2010, 10(6):709-714.
• [12]Bryngelson JD, Onuchic JN, Socci ND, Wolynes PG: Funnels, pathways, and the energy landscape of protein folding: a synthesis. Protein Struct Funct Bioinformatics 1995, 21(3):167-195. [http://dx.doi.org/10.1002/prot.340210302 webcite]
• [13]Go N: Protein folding as a stochastic process. J Stat Phys 1983, 30(2):413-423. [http://dx.doi.org/10.1007/BF01012315 webcite]
• [14]Clementi C, Nymeyer H, Onuchic JN: Topological and energetic factors what determines the structural details of the transition state ensemble and “en-route” intermediates for protein folding? An investigation for small globular proteins. J Mol Biol 2000, 298(5):937-953. [http://www.sciencedirect.com/science/article/pii/S0022283600936933 webcite]
• [15]Oliveira LC, Schug A, Onuchic JN: Geometrical features of the protein folding mechanism are a robust property of the energy landscape: a detailed investigation of several reduced models. J Phys Chem B 2008, 112(19):6131-6136.
• [16]Whitford PC, Noel JK, Gosavi S, Schug A, Sanbonmatsu KY, Onuchic JN: An all-atom structure-based potential for proteins: bridging minimal models with all-atom empirical forcefields. Protein Struct Funct Bioinformatics 2009, 75(2):430-441. [http://dx.doi.org/10.1002/prot.22253 webcite]
• [17]Sinner C, Lutz B, John S, Reinartz I, Verma A, Schug A: Simulating biomolecular folding and function by native-structure-based/go-type models. Isr J Chem 2014. [http://dx.doi.org/10.1002/ijch.201400012 webcite]
• [18]Rey-Stolle MF, Enciso M, Rey A: Topology-based models and NMR structures in protein folding simulations. J Comput Chem 2009, 30(8):1212-1219. [http://dx.doi.org/10.1002/jcc.21149 webcite]
• [19]Chavez LL, Onuchic JN, Clementi C: Quantifying the roughness on the free energy landscape: entropic bottlenecks and protein folding rates. J Am Chem Soc 2004, 126(27):8426-8432. [http://dx.doi.org/10.1021/ja049510+ webcite]
• [20]Shental-Bechor D, Levy Y: Effect of glycosylation on protein folding: a close look at thermodynamic stabilization. Proc Natl Acad Sci 2008, 105(24):8256-8261. [http://www.pnas.org/content/105/24/8256.abstract webcite]
• [21]Klein P, Mattoon D, Lemmon MA, Schlessinger J: A structure-based model for ligand binding and dimerization of EGF receptors. Proc Natl Acad Sci U S A 2004, 101(4):929-934. [http://www.pnas.org/content/101/4/929.abstract webcite]
• [22]Lammert H, Schug A, Onuchic JN: Robustness and generalization of structure-based models for protein folding and function. Protein Struct Funct Bioinformatics 2009, 77(4):881-891. [http://dx.doi.org/10.1002/prot.22511 webcite]
• [23]Clementi C, Jennings PA, Onuchic JN: Prediction of folding mechanism for circular-permuted proteins. J Mol Biol 2001, 311(4):879-890.
• [24]Schug A, Whitford PC, Levy Y, Onuchic JN: Mutations as trapdoors to two competing native conformations of the Rop-dimer. Proc Natl Acad Sci 2007, 104(45):17674-17679.
• [25]Li L, Mirny LA, Shakhnovich EI: Kinetics, thermodynamics and evolution of non-native interactions in a protein folding nucleus. Nat Struct Biol 2000, 7(4):336-342. [http://dx.doi.org/10.1038/74111 webcite]
• [26]Karanicolas J, Brooks CL III: Improved go-like models demonstrate the robustness of protein folding mechanisms towards non-native interactions. J Mol Biol 2003, 334(2):309-325. [http://www.sciencedirect.com/science/article/pii/S0022283603011999 webcite]
• [27]Clementi C, Plotkin SS: The effects of non-native interactions on protein folding rates: theory and simulation. Protein Sci 2004, 13(7):1750-1766. [http://dx.doi.org/10.1110 webcite]
• [28]Clementi C: Coarse-grained models of protein folding: toy models or predictive tools? Curr Opin Struct Biol 2008, 18:10-15. [http://www.sciencedirect.com/science/article/pii/S0959440X07001753 webcite] [Folding and Binding/Protein-nucleic acid interactions]
• [29]Lindorff-Larsen K, Piana S, Dror RO, Shaw DE: How fast-folding proteins fold. Science 2011, 334(6055):517-520. [http://www.sciencemag.org/content/334/6055/517.abstract webcite]
• [30]Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan Y, Wriggers W: Atomic-level characterization of the structural dynamics of proteins. Science 2010, 330(6002):341-346.
• [31]Best RB, Hummer G, Eaton WA: Native contacts determine protein folding mechanisms in atomistic simulations. Proc Natl Acad Sci 2013, 110(44):17874-17879. [http://www.pnas.org/content/110/44/17874.abstract webcite]
• [32]Plotkin SS: Speeding protein folding beyond the go model: how a little frustration sometimes helps. Protein Struct Funct Bioinformatics 2001, 45(4):337-345. [http://dx.doi.org/10.1002/prot.1154 webcite]
• [33]Ferreiro DU, Hegler JA, Komives EA, Wolynes PG: Localizing frustration in native proteins and protein assemblies. Proc Natl Acad Sci 1981, 104(50):9-19824. [http://www.pnas.org/content/104/50/19819.abstract webcite]
• [34]Dago AE, Schug A, Procaccini A, Hoch JA, Weigt M, Szurmant H: Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis. Proc Natl Acad Sci 2012, 109(26):E1733-E1742.
• [35]Nechushtai R, Lammert H, Michaeli D, Eisenberg-Domovich Y, Zuris JA, Luca MA, Capraro DT, Fish A, Shimshon O, Roy M, Schug A, Whitford PC, Livnah O, Onuchic JN, Jennings PA: Allostery in the ferredoxin protein motif does not involve a conformational switch. Proc Natl Acad Sci 2011, 108(6):2240-2245.
• [36]Mickler M, Dima RI, Dietz H, Hyeon C, Thirumalai D, Rief M: Revealing the bifurcation in the unfolding pathways of GFP by using single-molecule experiments and simulations. Proc Natl Acad Sci 2026, 104(51):8-20273.
• [37]Noel JK, Whitford PC, Sanbonmatsu KY, Onuchic JN: SMOG@ctbp: simplified deployment of structure-based models in GROMACS. Nucleic Acids Res 2010, 38(suppl 2):W657-W661. [http://nar.oxfordjournals.org/content/38/suppl_2/W657.abstract webcite]
• [38]Lutz B, Sinner C, Heuermann G, Verma A, Schug A: eSBMTools 1.0 enhanced native structure-based modeling tools. Bioinformatics 2013, 29(21):2795-2796. [http://dx.doi.org/10.1093/bioinformatics/btt478 webcite]
• [39]Schug A, Weigt M, Onuchic JN, Hwa T, Szurmant H: High-resolution protein complexes from integrating genomic information with molecular simulation. Proc Natl Acad Sci 2009, 106(52):22124-22129.
• [40]Lutz B, Faber M, Verma A, Klumpp S, Schug A: Differences between cotranscriptional and free riboswitch folding. Nucleic Acids Res 2013, 42(4):2687-2696.
• [41]Streit A, Bala P, Beck-Ratzka A, Benedyczak K, Bergmann S, Breu R, Daivandy J, Demuth B, Eifer A, Giesler A, Hagemeier B, Holl S, Huber V, Lamla N, Mallmann D, Memon A, Memon M, Rambadt M, Riedel M, Romberg M, Schuller B, Schlauch T, Schreiber A, Soddemann T, Ziegler W: UNICORE 6 — recent and future advancements. Ann Telecommunications 2010, 65:757-762. [http://dx.doi.org/10.1007/s12243-010-0195-x webcite]
• [42]Foster I, Kesselman C: Globus: a metacomputing infrastructure toolkit. Int J High Perform Comput Appl 1997, 11(2):115-128. [http://hpc.sagepub.com/content/11/2/115.abstract webcite]
• [43]Ellert M, Grønager M, Konstantinov A, Kónya B, Lindemann J, Livenson I, Nielsen J, Niinimäki M, Smirnova O, Wäänänen A: Advanced resource connector middleware for lightweight computational grids. Future Generat Comput Syst 2007, 23(2):219-240. [http://www.sciencedirect.com/science/article/pii/S0167739X06001178 webcite]
• [44]Pérez-Sánchez H, Kondov I, García JM, Klenin K, Wenzel W: A pipeline pilot based SOAP implementation of FlexScreen for high-throughput virtual screening. In Proceedings of the 3rd International Workshop on Science Gateways for Life Sciences (IWSG-Life 2011), London, United Kingdom, June 8–10, 2011, Volume 819 of CEUR-WS.org Edited by Kiss T. CEUR, Terstyanszky G. 2011, 9-9. [http://ceur-ws.org/Vol-819/ webcite]
• [45]Kondov I, Maul R, Bozic S, Meded V, Wenzel W: UNICORE-based integrated application services for multiscale materials modelling. In UNICORE Summit 2011 Proceedings, 7–8 July 2011, Torun, Poland, Volume 9 of IAS Series. Edited by Romberg M, Bala P, Müller-Pfefferkorn R, Mallmann D. Jülich: Forschungszentrum Jülich GmbH Zentralbibliothek; 2011:1-10. [http://hdl.handle.net/2128/4518 webcite]
• [46]Schneider O, Fogh RH, Sternberg U, Klenin K, Kondov I: Structure simulation with calculated NMR parameters — integrating COSMOS into the CCPN framework. In HealthGrid Applications and Technologies Meet Science Gateways for Life Sciences, Volume 175 of Studies in Health Technology and Informatics. Edited by Gesing S, Glatard T, Krüger J, Olabarriaga SD, Solomonides T, Silverstein JC, Montagnat J, Gaignard A, Krefting D. IOS Press; 2012:162-172. [http://dx.doi.org/10.3233/978-1-61499-054-3-162 webcite]
• [47]Bozic S, Kondov I, Meded V, Wenzel W: UNICORE-based workflows for the simulation of organic light-emitting diodes. In UNICORE Summit 2012 Proceedings, May 30–31, 2012, Dresden, Germany, Volume 15 of IAS Series. Edited by Huber V, Müller-Pfefferkorn R, Romberg MR. Jülich: Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag; 2012:15-25. [http://hdl.handle.net/2128/4705 webcite]
• [48]Bender A, Poschlad A, Bozic S, Kondov I: A service-oriented framework for integration of domain-specific data models in scientific workflows. Procedia Comput Sci 2013, 18:1087-1096. [http://dx.doi.org/10.1016/j.procs.2013.05.274 webcite], [2013 International Conference on Computational Science]
• [49]Wassenaar TA, van Dijk M, Loureiro-Ferreira N, Schot G, Vries SJ, Schmitz C, Zwan J, Boelens R, Giachetti A, Ferella L, Rosato A, Bertini I, Herrmann T, Jonker HRA, Bagaria A, Jaravine V, Güntert P, Schwalbe H, Vranken WF, Doreleijers JF, Vriend G, Vuister GW, Franke D, Kikhney A, Svergun DI, Fogh RH, Ionides J, Laue ED, Spronk C, Jurkša S, et al.: WeNMR: Structural Biology on the Grid. J Grid Comput 2012, 10(4):743-767. [http://dx.doi.org/10.1007/s10723-012-9246-z webcite]
• [50]van Dijk M, Wassenaar TA, Bonvin AM: A flexible, grid-enabled web portal for GROMACS molecular dynamics simulations. J Chem Theor Comput 2012, 8(10):3463-3472. [http://pubs.acs.org/doi/abs/10.1021/ct300102d webcite]
• [51]Birkenheuer G, Blunk D, Breuers S, Brinkmann A, dos Santos Vieira I, Fels G, Gesing S, Grunzke R, Herres-Pawlis S, Kohlbacher O, Kruger J, Lang U, Packschies L, Muller-Pfefferkorn R, Schafer P, Steinke T, Warzecha KD, Wewior M: MoSGrid: efficient data management and a standardized data exchange format for molecular simulations in a grid environment. J Cheminformatics 2012, 4(Suppl 1):P21. [http://dx.doi.org/10.1186/1758-2946-4-S1-P21 webcite]
• [52]Grunzke R, Breuers S, Gesing S, Herres-Pawlis S, Kruse M, Blunk D, de la Garza L, Packschies L, Schäfer P, Schärfe C, Schlemmer T, Steinke T, Schuller B, Müller-Pfefferkorn R, Jäkel R, Nagel WE, Atkinson M, Krüger J: Standards-based metadata management for molecular simulations. Concurrency Comput Pract Ex 2013. [http://dx.doi.org/10.1002/cpe.3116 webcite]
• [53]Fogh RH, Boucher W, Vranken WF, Pajon A, Stevens TJ, Bhat TN, Westbrook J, Ionides JMC, Laue ED: A framework for scientific data modeling and automated software development. Bioinformatics 2005, 21(8):1678-1684. [http://bioinformatics.oxfordjournals.org/content/21/8/1678.abstract webcite]
• [54]Vranken WF, Boucher W, Stevens TJ, Fogh RH, Pajon A, Llinas M, Ulrich EL, Markley JL, Ionides J, Laue ED: The CCPN data model for NMR spectroscopy: development of a software pipeline. Protein Struct Funct Bioinformatics 2005, 59(4):687-696. [http://dx.doi.org/10.1002/prot.20449 webcite]
• [55]Grunzke R, Birkenheuer G, Blunk D, Breuers S, Brinkmann A, Gesing S, Herres-Pawlis S, Kohlbacher O, Krüger J, Kruse M, Müller-Pfefferkorn R, Schäfer P, Schuller B, Steinke T, Zink A: A data driven science gateway for computational workflows. In UNICORE Summit 2012, Dresden, Germany, Volume 15 of IAS Series. Jülich: Forschungszentrum, Jülich GmbH Zentralbibliothek; 2012:35-49.
• [56]Service-oriented architecture (SOA) Published online [http://en.wikipedia.org/wiki/Service-oriented_architecture webcite] Accessed 2012-12-07
• [57]Demuth B, Schuller B, Holl S, Daivandy J, Giesler A, Huber V, Sild S: The UNICORE Rich Client: facilitating the automated execution of scientific workflows. In 2010 IEEE Sixth International Conference on e-Science (e-Science), Brisbane, QLD. IEEE; 2010:238-245.
• [58]Ratering R, Lukichev A, Riedel M, Mallmann D, Vanni A, Cacciari C, Lanzarini S, Benedyczak K, Borcz M, Kluszcynski R, Bala P, Ohme G: GridBeans: supporting e-science and grid applications. In Second IEEE International Conference on e-Science and Grid Computing, 2006 (e-Science ‘06), Amsterdam. IEEE; 2006:45-52.
• [59]Foster I: Globus toolkit version 4: software for service-oriented systems. In Network and Parallel Computing, Volume 3779 of Lecture Notes in Computer Science. Edited by Jin H, Reed D, Jiang W. Springer; 2005:2-13. [http://dx.doi.org/10.1007/11577188_{2} webcite]
• [60]Liu B, Madduri RK, Sotomayor B, Chard K, Lacinski L, Dave UJ, Li J, Liu C, Foster IT: Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses. J Biomed Inform 2014, 49(0):119-133. [http://www.sciencedirect.com/science/article/pii/S1532046414000070 webcite]
• [61]Krabbenhöft HN, Möller S, Bayer D: Integrating ARC grid middleware with Taverna workflows. Bioinformatics 2008, 24(9):1221-1222. [http://bioinformatics.oxfordjournals.org/content/24/9/1221.abstract webcite]
• [62]Pronk S, Páll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Hess B, Lindahl E: GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics (Oxford, England) 2013, 29(7):845-854. [http://www.ncbi.nlm.nih.gov/pubmed/23407358 webcite]
• [63]Noel JK, Whitford PC, Sanbonmatsu KY, Onuchic JN: SMOG@ctbp simplified deployment of structure-based models in GROMACS. Nucleic Acids Res 2010, 38:W657-W661. [http://dx.doi.org/10.1093/nar/gkq498 webcite]
• [64]Jmol: an open-source Java viewer for chemical structures in 3D [http://jmol.sourceforge.net/ webcite] Accessed 2014-08-07
• [65]Yates A, Bliven SE, Rose PW, Jacobsen J, Troshin PV, Chapman M, Gao J, Koh CH, Foisy S, Holland R, Rimša G, Heuer ML, Brandstätter-Müller H, Bourne PE, Willis S, Prlić A: BioJava: an open-source framework for bioinformatics in 2012. Bioinformatics 2012, 28:2693-2695.
• [66]Schuller B, Demuth B, Mix H, Rasch K, Romberg M, Sild S, Maran U, Bala P, del Grosso E, Casalegno M, Piclin N, Pintore M, Sudholt W, Baldridge K: Chemomentum - UNICORE 6 based infrastructure for complex applications in science and technology. In Euro-Par 2007 Workshops: Parallel Processing, Volume 4854 of Lecture Notes in Computer Science. Edited by Boug’e L, Forsell M, Träff J, Streit A, Ziegler W, Alexander M, Childs S. Springer; 2008:82-93. [http://dx.doi.org/10.1007/978-3-540-78474-6_12 webcite]