QUANTILE TRANSFORM IN STRUCTURAL BIOINFORMATICS PROBLEMS
( Pp. 29-43)
More about authors
Poluyan Sergey Vladimirovich
assistent, kafedra raspredelennyh informacionno-vychislitelnyh sistem
Dubna State University Ershov Nikolay M. Cand. Sci. (Phys.-Math.); senior research at the Faculty of Computational Mathematics and Cybernetics
Lomonosov Moscow State University
Moscow, Russian Federation
Dubna State University Ershov Nikolay M. Cand. Sci. (Phys.-Math.); senior research at the Faculty of Computational Mathematics and Cybernetics
Lomonosov Moscow State University
Moscow, Russian Federation
Abstract:
In this paper we study features of the multivariate empirical quantum function implementation for which sample is distributed at the mesh points of the regular grid. We present an algorithm for continuous and discrete quantile transform based on recursive definition of the multivariate quantile function. We perform numerical study of the presented algorithm and demonstrate it computational complexity according to representation of the sample. We present the results of using evolutionary optimization algorithm with quantile transform for solving the problems in structural bioinformatics: protein structure prediction from amino acid sequence and protein-peptide docking with known binding site and linear peptide structure.
How to Cite:
Poluyan S.V., Ershov N.M., (2019), QUANTILE TRANSFORM IN STRUCTURAL BIOINFORMATICS PROBLEMS. Computational Nanotechnology, 4 => 29-43.
Reference list:
Poluyan S.V., Ershov N.M. Primenenie mnogomernoy kvantil noy funktsii v zadache peptid-belok dokinga // Vestnik YUzhno-Ural skogo gosudarstvennogo universiteta. Seriya Vychislitel naya matematika i informatika . 2019. T. 8, № 2. S. 63-75. Poluyan S.V., Ershov N.M. The use of multidimensional quantile function in the peptide-protein docking problem. Bulletin of the South Ural State University. Series quot;Computational Mathematics and Computer Science quot;. 2019. Vol. 8, No. 2. P. 63-75.
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Rentzsch R., Renard B.Y. Docking small peptides remains a great challenge: an assessment using AutoDock Vina. Briefings in Bioinformatics. 2015. Vol. 16. No. 6. P. 1045-1056.
Einmahl J.H.J., Mason D.M. Generalized Quantile Processes. The Annals of Statistics. 1992. Vol. 20. No. 2. P. 1062-1078.
Repozitorii GitHub. URL: https://github.com/poluyan (data obrashcheniya: 10.12.2019). GitHub repositories. URL: https://github.com/ poluyan (accessed: 12/10/2019).
Poluyan S., Ershov N. Parallel evolutionary optimization algorithms for peptide-protein docking. EPJ Web of Conferences. 2018. Vol. 173. P. 06010-06010.
Daciuk J., Mihov S.W., Watson B.W., Watson R.E. Incremental Construction of Minimal Acyclic Finite-State Automata. Computational Linguistics. 2000. Vol. 26. No. 1. P. 3-16.
Ferr ndez A., Peral J. MergedTrie: Efficient textual indexing. PLoS One. 2019. Vol. 14. No. 4.
Kjos-Hanssen B., Liu L. The number of languages with maximum state complexity. Theory and Applications of Models of Computation. TAMC 2019. Lecture Notes in Computer Science. 2019. Vol. 11436. P. 394-409.
Alford R.F., Leaver-Fay A., et al. The Rosetta all-atom energy function for macromolecular modeling and design. Journal of Chemical Theory and Computation. 2017. Vol. 13. No. 6. P. 3031-3048.
Poluyan S.V., Ershov N.M. Primenenie parallel nykh evolyutsionnykh algoritmov optimizatsii v zadachakh strukturnoy bioinformatiki // Vestnik UGATU. 2017. T. 21, № 4. S. 143-152. Poluyan S.V., Ershov N.M. Application of parallel evolutionary optimization algorithms in problems of structural bioinformatics. Bulletin of USATU. 2017. Vol. 21. No. 4. P. 143-152.
Buchan D., Jones D. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Research. 2019. Vol. 47. No. W1. P. W402-W407.
Zhang J., Sanderson A. JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation. 2009. Vol. 13. No. 5. P. 945-958.
Shapovalov M., Dunbrack R.L. A Smoothed Backbone-Dependent Rotamer Library for Proteins Derived from Adaptive Kernel Density Estimates and Regressions. Structure. 2011. Vol. 19. No. 6. P. 844-858.
Berman H.M., Westbrook J. et al. The Protein Data Bank. Nucleic Acids Research. 2000. Vol. 28. No. 1. P. 235-242.
Schr dinger LLC. The PyMOL Molecular Graphics System. URL: https://pymol.org (data obrashcheniya: 10.12.2019).
Sellers M.S., Hurley M.M. XPairIt Docking Protocol for pepti docking and analysis. Molecular Simulati. 2015. Vol. 42. No. 2. P. 149-161.
Raveh B., London N., et al. Rosetta FlexPepDock ab-initio: Simultaneous Folding, Docking and Refinement of Peptides onto Their Receptors. PLoS ONE. 2011. Vol. 6. No. 4.
Adam G., Bashashin M. et al. IT-ecosystem of the HybriLIT heterogeneous platform for high-performance computing and training of IT-specialists // Selected Papers of the 8th International Conference Distributed Computing and Grid-technologies in Science and Education (GRID 2018). 2018. P. 638-644.
Heterogeneous Computing Cluster HybriLIT. URL: http://hybrilit.jinr. ru/en/ (data obrashcheniya: 10.12.2019).
O Brien G.L. The Comparison Method for Stochastic Processes. The Annals of Probability. 1975. Vol. 3. No. 1. P. 80-88.
Rentzsch R., Renard B.Y. Docking small peptides remains a great challenge: an assessment using AutoDock Vina. Briefings in Bioinformatics. 2015. Vol. 16. No. 6. P. 1045-1056.
Einmahl J.H.J., Mason D.M. Generalized Quantile Processes. The Annals of Statistics. 1992. Vol. 20. No. 2. P. 1062-1078.
Repozitorii GitHub. URL: https://github.com/poluyan (data obrashcheniya: 10.12.2019). GitHub repositories. URL: https://github.com/ poluyan (accessed: 12/10/2019).
Poluyan S., Ershov N. Parallel evolutionary optimization algorithms for peptide-protein docking. EPJ Web of Conferences. 2018. Vol. 173. P. 06010-06010.
Daciuk J., Mihov S.W., Watson B.W., Watson R.E. Incremental Construction of Minimal Acyclic Finite-State Automata. Computational Linguistics. 2000. Vol. 26. No. 1. P. 3-16.
Ferr ndez A., Peral J. MergedTrie: Efficient textual indexing. PLoS One. 2019. Vol. 14. No. 4.
Kjos-Hanssen B., Liu L. The number of languages with maximum state complexity. Theory and Applications of Models of Computation. TAMC 2019. Lecture Notes in Computer Science. 2019. Vol. 11436. P. 394-409.
Alford R.F., Leaver-Fay A., et al. The Rosetta all-atom energy function for macromolecular modeling and design. Journal of Chemical Theory and Computation. 2017. Vol. 13. No. 6. P. 3031-3048.
Poluyan S.V., Ershov N.M. Primenenie parallel nykh evolyutsionnykh algoritmov optimizatsii v zadachakh strukturnoy bioinformatiki // Vestnik UGATU. 2017. T. 21, № 4. S. 143-152. Poluyan S.V., Ershov N.M. Application of parallel evolutionary optimization algorithms in problems of structural bioinformatics. Bulletin of USATU. 2017. Vol. 21. No. 4. P. 143-152.
Buchan D., Jones D. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Research. 2019. Vol. 47. No. W1. P. W402-W407.
Zhang J., Sanderson A. JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation. 2009. Vol. 13. No. 5. P. 945-958.
Shapovalov M., Dunbrack R.L. A Smoothed Backbone-Dependent Rotamer Library for Proteins Derived from Adaptive Kernel Density Estimates and Regressions. Structure. 2011. Vol. 19. No. 6. P. 844-858.
Berman H.M., Westbrook J. et al. The Protein Data Bank. Nucleic Acids Research. 2000. Vol. 28. No. 1. P. 235-242.
Schr dinger LLC. The PyMOL Molecular Graphics System. URL: https://pymol.org (data obrashcheniya: 10.12.2019).
Sellers M.S., Hurley M.M. XPairIt Docking Protocol for pepti docking and analysis. Molecular Simulati. 2015. Vol. 42. No. 2. P. 149-161.
Raveh B., London N., et al. Rosetta FlexPepDock ab-initio: Simultaneous Folding, Docking and Refinement of Peptides onto Their Receptors. PLoS ONE. 2011. Vol. 6. No. 4.
Adam G., Bashashin M. et al. IT-ecosystem of the HybriLIT heterogeneous platform for high-performance computing and training of IT-specialists // Selected Papers of the 8th International Conference Distributed Computing and Grid-technologies in Science and Education (GRID 2018). 2018. P. 638-644.
Heterogeneous Computing Cluster HybriLIT. URL: http://hybrilit.jinr. ru/en/ (data obrashcheniya: 10.12.2019).
Keywords:
empirical quantile function, finite-state automata, global optimization.