Skip to main content
Log in

Modern Methods and Software Systems of Molecular Modeling and Application of Behavior Algebra

  • SOFRWARE–HARDWARE SYSTEMS
  • Published:
Cybernetics and Systems Analysis Aims and scope

Abstract

The main methods of molecular modeling and specialized software for the development and analysis of molecular models are considered. In particular, the article describes the results of the first stage of developing an environment for the analysis of the molecular and biomolecular interaction that is based on the formalism of behavior algebra and insertion modeling. The results of the experiment of applying the proposed approach to modeling the covalent nonpolar bond are given.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. A. Letichevsky, O. Letychevskyi, and V. Peschanenko, “Insertion modeling and its applications,” Comput. Sci. J. Mold., Vol. 24, No. 3 (72), 357–370 (2016).

    MathSciNet  MATH  Google Scholar 

  2. T. Lengauer and M. Rarey, “Computational methods for biomolecular docking,” Current Opinion in Structural Biology, Vol. 6, No. 3, 402–406 (1996). https://doi.org/10.1016/s0959-440x(96)80061-3.

    Article  Google Scholar 

  3. DOCK. URL: http://dock.compbio.ucsf.edu.

  4. GOLD. URL: http://www.ccdc.cam.ac.uk/products/life_sciences/gold/.

  5. FLEXX. URL: http://www.biosolveit.de/FlexX/.

  6. FRED. URL: http://www.eyesopen.com/products/applications/fred.html.

  7. AUTODOCK. URL: http://autodock.scripps.edu.

  8. DOCKINGSHOP. URL: http://vis.lbl.gov/~scrivelli/Publicsilvia_page/DockingShop/html.

  9. J. M. Haile, Molecular Dynamics Simulation: Elementary Methods, John Wiley & Sons, New York (1992).

    Google Scholar 

  10. D. C. Rapaport, The Art of Molecular Dynamics Simulation, 2nd ed., Cambridge University Press, Cambridge (2004). https://doi.org/10.1017/CBO9780511816581.

    Book  MATH  Google Scholar 

  11. K. Binder (ed.), Monte Carlo Methods in Statistical Physics, Springer, Berlin–Heidelberg (1986).

    MATH  Google Scholar 

  12. K. Binder and D. W. Heerman, Monte Carlo Simulation in Statistical Physics: An Introduction, Springer-Verlag, Berlin (1992).

    Book  Google Scholar 

  13. N. S. V. Barbosa, E. R. A. Lima, and F. W. Tavares, Molecular Modeling in Chemical Engineering. Elsevier Reference Collection in Chemistry, Molecular Sciences and Chemical Engineering, Elsevier (2017). https://doi.org/10.1016/B978-0-12-409547-2.13915-0.

  14. A. E. Doroshenko, V. D. Havruchenko, V. I. Egorov, and L. N. Suslovà, “The modeling of the results of quantum-chemical calculations in the Visual Quantum System,” Upr. Sist. Mash., No. 5, 83–87 (2012).

  15. J. R. Faeder, M. L. Blinov, and W. S. Hlavacek, “Rule-based modeling of biochemical systems with BioNetGen,” in: I. Maly (ed.), Systems Biology. Methods in Molecular Biology, Vol. 500, Humana Press (2009), pp. 113–167. https://doi.org/10.1007/978-1-59745-525-1_5.

  16. T. Sneha and J. J. Georrge, “In silico protein engineering: Methods and Tools,” in: Proc. of 10th National Sci. Symp. on Recent Trends in Science and Technology, Christ Publications, Gujarat (2018), pp. 73–80.

  17. E. Chovancova, A. Pavelka, P. Benes, O. Strnad, J. Brezovsky, B. Kozlikova, A. Gora, V. Sustr, M. Klvana, P. Medek, L. Biedermannova, J. Sochor, and J. Damborsky, “CAVER 3.0: A tool for the analysis of transport pathways in dynamic protein structures,” PLoS Comput. Biol. Vol. 8(10), e1002708 (2012). https://doi.org/10.1371/journal.pcbi.1002708.

    Article  Google Scholar 

  18. J. Smadbeck, M. B. Peterson, G. A. Khoury, M. S. Taylor, and C. A. Floudas, “Protein WISDOM: A workbench for in silico de novo design of biomolecules,” J. Vis. Exp., Iss. 77, e50476 (2013). https://doi.org/10.3791/50476.

  19. P. Mitra, D. Shultis, and Y. Zhang, “EvoDesign: De novo protein design based on structural and evolutionary profiles,” Nucleic Acids Research, Vol. 41, Iss. W1, W273–W280 (2013). https://doi.org/10.1093/nar/gkt384.

    Article  Google Scholar 

  20. A. Chatterjee, J. Guedj, and A. S. Perelson, “Mathematical modeling of HCV infection: What can it teach us in the era of direct-acting antiviral agents?” Antivir Ther., Vol. 17, 1171–1182 (2012). https://doi.org/10.3851/IMP2428.

    Article  Google Scholar 

  21. The BioSPI PROJECT. URL: http://www.wisdom.weizmann.ac.il/~biospi/index_main.html.

  22. M. R. Lakin, L. Paulevé, and A. Phillips, “Stochastic simulation of multiple process calculi for biology,” Theor. Comput. Sci., Vol. 431, 181–206 (2012). https://doi.org/10.1016/j.tcs.2011.12.057.

    Article  MathSciNet  MATH  Google Scholar 

  23. Chemcraft. URL: http://www.chemcraftprog.com/ru/.

  24. Avogadro. URL: https://avogadro.ru.malavida.com/#gref.

  25. ChemDrawPro. URL: https://www.cambridgesoft.com/Ensemble_for_Chemistry/ChemDraw/ChemDrawPro.

  26. Home Page for RasMol and OpenRasMol. URL: http://www.openrasmol.org/.

  27. PyMOL. URL: https://pymol.org/2/.

  28. T. D. Goddard, C. C. Huang, E. C. Meng, E. F. Pettersen, G. S. Couch, J. H. Morris, and T. E. Ferrin, “UCSF ChimeraX: Meeting modern challenges in visualization and analysis,” Protein Sci., Special Issue on Tools for Protein Science, Vol. 27, Iss. 1, 14–25 (2018). https://doi.org/10.1002/pro.3235.

    Article  Google Scholar 

  29. M. W. Schmidt, K. K. Baldridge, J. A. Boatz, S. T. Elbert, M. S. Gordon, J. H. Jensen, S. Koseki, N. Matsunaga, K. A. Nguyen, S. Su, T. L. Windus, M. Dupuis, and J. A. Montgomery Jr, “General atomic and molecular electronic structure system,” J. Comput. Chem., Vol. 14, Iss. 11, 1347–1363 (1993). https://doi.org/10.1002/jcc.540141112.

    Article  Google Scholar 

  30. Program for Mathematical Modeling of Kinetics of Complex Chemical Reactions KINET. URL: http://www.icho39.chem.msu.ru/downloads/kinet.pdf.

  31. ISB. URL: http://labs.systemsbiology.net/bolouri/software/Dizzy/.

  32. BOSS (Biochemical and Organic Simulation System). URL: http://zarbi.chem.yale.edu/software.html.

  33. M. L. Blinov, J. R. Faeder, B. Goldstein, W. S. Hlavacek, “BioNetGen: Software for rule-based modeling of signal transduction based on the interactions of molecular domains,” Bioinformatics, Vol. 20, Iss. 17, 3289–3291 (2004). https://doi.org/10.1093/bioinformatics/bth378.

    Article  Google Scholar 

  34. L. A. Harris, J. S. Hogg, J.-J. Tapia, J. A. P. Sekar, S. Gupta, I. Korsunsky, A. Arora, D. Barua, R. P. Sheehan, and J. R. Faeder, “BioNetGen 2.2: Advances in rule-based modeling,” Bioinformatics, Vol. 32, Iss. 21, 3366–3368 (2016). https://doi.org/10.1093/bioinformatics/btw469.

    Article  Google Scholar 

  35. TinkerTools. URL: https://tinkertools.org/.

  36. Spartan (Chemistry Software). URL: https://www.wavefun.com/.

  37. F. Mohamadi, N. G. J. Richards, W. C. Guida, R. Liskamp, M. Lipton, C. Caufield, and W. C. Still, “Macromodel — an integrated software system for modeling organic and bioorganic molecules using molecular mechanics,” J. Comput. Chem., Vol. 11, Iss. 4, 440–467 (1990).

    Article  Google Scholar 

  38. S. Stoma, M. Fröhlich, S. Gerber, and E. Klipp, “STSE: Spatio-temporal simulation environment dedicated to biology,” BMC Bioinformatics, Vol. 12, Article number 126 (2011).

  39. Abalone II. URL: http://www.biomolecular-modeling.com/Abalone/.

  40. Molecular Forecaster. URL: https://www.molecularforecaster.com/.

  41. R. Salomon-Ferrer, D. A. Case, and R. C. Walker, “An overview of the Amber biomolecular simulation package,” WIREs Comput. Mol. Sci., Vol. 3, Iss. 2, 198–210 (2013).

    Article  Google Scholar 

  42. Integrated Computer-Aided Molecular Design Platform. URL: https://www.chemcomp.com/Products.htm.

  43. B. R. Brooks, C. L. Brooks III, A. D. Mackerell Jr, L. Nilsson, R. J. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels, S. Boresch, A. Caflisch, L. Caves, Q. Cui, A. R. Dinner, M. Feig, S. Fischer, J. Gao, M. Hodoscek, W. Im, K. Kuczera, T. Lazaridis, J. Ma, V. Ovchinnikov, E. Paci, R. W. Pastor, C. B. Post, J. Z. Pu, M. Schaefer, B. Tidor, R. M. Venable, H. L. Woodcock, X. Wu, W. Yang, D. M. York, and M. Karplus, “CHARMM: The biomolecular simulation program,” J. Comput. Chem., Vol. 30, Iss. 10, 1545–1614 (2009). https://doi.org/10.1002/jcc.21287.

    Article  Google Scholar 

  44. APS & IMS. URL: http//www.apsystem.org.ua.

  45. O. Letychevskyi, V. Peschanenko, M. Poltoratskyi, and Y. Tarasich, “Platform for modeling of algebraic behavior: Experience and conclusions,” CEUR Workshop Proc., Vol. 2732, 42–57 (2020).

    Google Scholar 

  46. A. Letichevsky and D. Gilbert, “A model for interaction of agents and environments,” in: D. Bert, C. Choppy, and P. D. Mosses (eds.), Recent Trends in Algebraic Development Techniques, WADT 1999; Lecture Notes in Computer Science, Vol. 1827, Springer, Berlin–Heidelberg (2000), pp. 311–328. https://doi.org/10.1007/978-3-540-44616-3_18.

  47. S. Baranov, C. Jervis, V. Kotlyarov, A. Letichevsky, T. Weigert, “Leveraging UML to deliver correct telecom applications,” in: L. Lavagno, G. Martin, and B. Selic, (eds.), UML for Real: Design of Embedded Real-Time Systems, Amsterdam, Kluwer Acad. Publ. (2003), pp. 323–342.

    Google Scholar 

  48. A. Ad. Letichevsky, Yu. V. Kapitonova, V. A. Volkov, A. A. Letichevsky, S. N. Baranov, V. P. Kotlyarov, and T. Weigert, “System specification with basic protocols,” Cybern. Syst. Analysis, Vol. 41, No. 4, 479–493 (2005). https://doi.org/10.1007/s10559-005-0083-y.

    Article  MathSciNet  MATH  Google Scholar 

  49. A. Letichevsky, J. Kapitonova, A. Letichevsky Jr, V. Volkov, S. Baranov, V. Kotlyarov, and T. Weigert, “Basic protocols, message sequence charts, and the verification of requirements specifications,” Computer Networks, Vol. 49, Iss. 5, 661–675 (2005).

    Article  Google Scholar 

  50. R. F. W. Bader, Atoms in Molecules: A Quantum Theory, Oxford, Oxford University Press (1990).

    Google Scholar 

  51. P. S. V. Kumar, V. Raghavendra, and V. Subramanian, “Bader’s theory of atoms in molecules (AIM) and its applications to chemical bonding,” J. Chem. Sci., Vol. 128, No. 1, 1527–1536 (2016).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to O. Letychevskyi.

Additional information

Translated from Kibernetyka ta Systemnyi Analiz, No. 3, May–June, 2022, pp. 150–163.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Letychevskyi, O., Volkov, V., Tarasich, Y. et al. Modern Methods and Software Systems of Molecular Modeling and Application of Behavior Algebra. Cybern Syst Anal 58, 454–464 (2022). https://doi.org/10.1007/s10559-022-00482-x

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-022-00482-x

Keywords

Navigation