Skip to main content
SpringerLink
Log in

InterCriteria Analysis Implementation for Exploration of the Performance of Various Docking Scoring Functions

  • Conference paper
  • First Online:
Advances in High Performance Computing (HPC 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 902))

Abstract

The present study describes an implementation of InterCriteria Analysis (ICrA) in the field of the computer-aided drug design and computational toxicology. ICrA strives to go beyond the nature of the criteria involved in a process of evaluation of multiple objects against multiple criteria, and, thus to discover some dependencies between the criteria themselves. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. In this study new software capabilities, implemented in order to apply ICrA to in silico drug design, are presented. As a case study, ICrA is implemented to explore the performance of various scoring functions in docking. Docking, which is the most commonly used structure-based drug design method, has been applied to predict the binding mode and to provide a measure for the ligand binding affinity to the protein. In particular, ligands of the peroxisome proliferator-activated nuclear receptor gamma (PPAR\(\gamma \)), involved in the control of a number of physiologically significant processes, have been investigated towards prediction of their binding to the protein. A dataset of 160 tyrosine-based PPAR\(\gamma \) agonists with experimentally determined binding affinities has been used in this investigation. Docking combined with the in-house developed pharmacophore model as a filter has been applied. Various scoring functions and docking protocols have been tested in the molecular modelling platform MOE (v. 2019.01). ICrA has been applied to assess dependencies among the scoring functions. The analysis has demonstrated high positive consonance for two of the scoring functions – London dG and Alpha HB. None of the functions could be distinguished as a good predictor of the experimental binding affinity.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    On the most abstract level these computed values may be of any type. The only restriction posed on them is that they should be in some sense partitionable in at least two groups - comparable and non-comparable. In practice, the current implementation works only with real numerical values or ordered pairs of real numerical values (in particular IFPs).

  2. 2.

    In what follows we provide the description for ordered pairs of real numerical values as they include the real numerical values as a particular case. In future work, tolerance thresholds and other ways to provide more user control are planned to be implemented.

References

  1. Al Sharif, M., Tsakovska, I., Pajeva, I.: The application of molecular modelling in the safety assessment of chemicals: a case study on ligand-dependent PPAR\(\gamma \) dysregulation. Toxicology 392, 140–154 (2017)

    Article  Google Scholar 

  2. Angelova, N., Atanassov, K., Riecan, B.: InterCriteria analysis of the intuitionistic fuzzy implication properties. Notes IFSs 21(5), 20–23 (2015)

    MATH  Google Scholar 

  3. Atanassov, K.: Intuitionistic fuzzy sets, VII ITKR Session, Sofia, 20–23 June 1983, Reprinted: Int. J. Bioautom. 20(S1), S1–S6 (2016)

    MathSciNet  Google Scholar 

  4. Atanassov, K.: On Intuitionistic Fuzzy Sets Theory. Springer, Cham (2012)

    Book  Google Scholar 

  5. Atanassov, K.: Index Matrices: Towards An Augmented Matrix Calculus. Springer, Berlin (2014)

    MATH  Google Scholar 

  6. Atanassov, K., Atanassova, V., Gluhchev, G.: InterCriteria analysis: ideas and problems. Notes IFSs 21(1), 81–88 (2015)

    MATH  Google Scholar 

  7. Atanassov, K., Mavrov, D., Atanassova, V.: Intercriteria decision making: a new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues IFSs GNs 11, 1–8 (2014)

    Google Scholar 

  8. Atanassov, K., Szmidt, E., Kacprzyk, J.: On intuitionistic fuzzy pairs. Notes IFSs 19(3), 1–13 (2013)

    MATH  Google Scholar 

  9. Atanassova, V.: Interpretation in the intuitionistic fuzzy triangle of the results, obtained by the InterCriteria analysis. In: Proceedings of 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), pp. 1369–1374 (2015)

    Google Scholar 

  10. Atanassova, V., Doukovska, L., Atanassov, K., Mavrov, D.: InterCriteria decision making approach to EU member states competitiveness analysis. In: Proceedings of the International Symposium on Business Modeling and Software Design – BMSD 2014, pp. 289–294 (2014)

    Google Scholar 

  11. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucl. Acids Res. 28, 235–242 (2000)

    Article  Google Scholar 

  12. Bureva, V., Sotirova, E., Sotirov, S., Mavrov, D.: Application of the InterCriteria decision making method to Bulgarian universities ranking. Notes IFSs 21(2), 111–117 (2015)

    Google Scholar 

  13. Cobb, J.E., Blanchard, S.G., Boswell, E.G., et al.: N-(2-Benzoylphenyl)-L-tyrosine PPAR\(\gamma \) agonists. 3. Structure-activity relationship and optimization of the N-aryl substituent. J. Med. Chem. 41, 5055–5069 (1998)

    Google Scholar 

  14. Collins, J.L., Blanchard, S.G., Boswell, E.G., et al.: N-(2-Benzoylphenyl)-L-tyrosine PPAR\(\gamma \) agonists. 2. Structure-activity relationship and optimization of the phenyl alkyl ether moiety. J. Med. Chem. 41, 5037–5054 (1998)

    Google Scholar 

  15. Henke, B.R., Blanchard, S.G., Brackeen, M.F., et al.: N-(2-Benzoylphenyl)-L-tyrosine PPAR\(\gamma \) agonists. 1. Discovery of a novel series of potent antihyperglycemic and antihyperlipidemic agents. J. Med. Chem. 41(25), 5020–5036 (1998)

    Google Scholar 

  16. Höltje, H.D., Sippl, W., Rognan, D., Folkers D.: Molecular Modeling: Basic Principles and Applications. 3rd Revised and Expanded edition, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim (2008)

    Google Scholar 

  17. InterCriteria.net – ICrAData software. http://intercriteria.net/software/. Accessed 31 May 2019

  18. Ikonomov, N., Vassilev, P., Roeva, O.: ICrAData - Software for InterCriteria analysis. Int. J. Bioautom. 22(1), 1–10 (2018)

    Article  Google Scholar 

  19. Ilkova, T., Petrov, M.: Application of InterCriteria analysis to the Mesta river pollution modelling. Notes IFSs 21(2), 118–125 (2015)

    Google Scholar 

  20. Ilkova, T., Petrov, M.: InterCriteria analysis for evaluation of the pollution of the Struma river in the Bulgarian section. Notes IFSs 22(3), 120–130 (2016)

    Google Scholar 

  21. Java platform SE 8, API documentation. https://docs.oracle.com/javase/8/docs/api/. Accessed 31 May 2019

  22. Java virtual machine. https://java.com/. Accessed 31 May 2019

  23. Krawczak, M., Bureva, V., Sotirova, E., Szmidt, E.: Application of the InterCriteria decision making method to universities ranking. Adv. Intell. Syst. Comput. 401, 365–372 (2016)

    Google Scholar 

  24. Molecular operating environment. Chemical computing group. http://www.chemcomp.com. Accessed 31 May 2019

  25. Pencheva, T., Angelova, M.: InterCriteria analysis of simple genetic algorithms performance. Stud. Comput. Intell. 681, 147–159 (2017)

    MathSciNet  MATH  Google Scholar 

  26. Pencheva, T., Angelova, M., Vassilev, P., Roeva, O.: InterCriteria analysis approach to parameter identification of a fermentation process model. Adv. Intell. Syst. Comput. 401, 385–397 (2016)

    Google Scholar 

  27. Roeva, O., Fidanova, S., Luque, G., Paprzycki, M.: Intercriteria analysis of ACO performance for workforce planning problem. Stud. Comput. Intell. 795, 47–67 (2019)

    MathSciNet  MATH  Google Scholar 

  28. Roeva, O., Vassilev, P., Ikonomov, N., Angelova, M., Su, J., Pencheva, T.: On different algorithms for InterCriteria relations calculation. Stud. Comput. Intell. 757, 143–160 (2019)

    Google Scholar 

  29. Sotirov, S., Sotirova, E., Melin, P., Castillo, O., Atanassov, K.: Modular neural network preprocessing procedure with intuitionistic fuzzy InterCriteria analysis method. In: Proceedings of Flexible Query Answering Systems, pp. 175–186 (2016)

    Google Scholar 

  30. Todinova, S., Mavrov, D., Krumova, S., Marinov, P., Atanassova, V., Atanassov, K., Taneva, S.G.: Blood plasma thermograms dataset analysis by means of InterCriteria and correlation analyses for the case of colorectal cancer. Int. J. Bioautom. 20(1), 115–124 (2016)

    Google Scholar 

  31. Tsakovska, I., Al Sharif, M., Alov, P., Diukendjieva, A., Fioravanzo, E., Cronin, M., Pajeva, I.: Molecular modelling study of the PPAR\(\gamma \) receptor in relation to the mode of action/adverse outcome pathway framework for liver steatosis. Int. J. Mol. Sci. 15, 7651–7666 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

Funding from the National Science Fund of Bulgaria, under grant DN 17/6 “A New Approach, Based on an Intercriteria Data Analysis, to Support Decision Making in in silico Studies of Complex Biomolecular Systems”, is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Bl. 105, 1113, Sofia, Bulgaria

    Ivanka Tsakovska, Petko Alov, Vassia Atanassova, Peter Vassilev, Olympia Roeva, Dessislava Jereva, Krassimir Atanassov, Ilza Pajeva & Tania Pencheva

  2. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. G. Bonchev Bl. 8, 1113, Sofia, Bulgaria

    Nikolay Ikonomov

Authors
  1. Ivanka Tsakovska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petko Alov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikolay Ikonomov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vassia Atanassova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peter Vassilev
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Olympia Roeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dessislava Jereva
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Krassimir Atanassov
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ilza Pajeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Tania Pencheva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tania Pencheva .

Editor information

Editors and Affiliations

  1. Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Dimov

  2. Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tsakovska, I. et al. (2021). InterCriteria Analysis Implementation for Exploration of the Performance of Various Docking Scoring Functions. In: Dimov, I., Fidanova, S. (eds) Advances in High Performance Computing. HPC 2019. Studies in Computational Intelligence, vol 902. Springer, Cham. https://doi.org/10.1007/978-3-030-55347-0_8

Download citation

Publish with us

Policies and ethics

Search

Navigation