Abstract
Artificial neural networks (ANNs) are used for classification and prediction of enzymatic activity of ethylbenzene dehydrogenase from EbN1 Azoarcus sp. bacterium. Ethylbenzene dehydrogenase (EBDH) catalyzes stereo-specific oxidation of ethylbenzene and its derivates to alcohols, which find its application as building blocks in pharmaceutical industry. ANN systems are trained based on theoretical variables derived from Density Functional Theory (DFT) modeling, topological descriptors, and kinetic parameters measured with developed spectrophotometric assay. Obtained models exhibit high degree of accuracy (100% of correct classifications, correlation between predicted and experimental values of reaction rates on the 0.97 level). The applicability of ANNs is demonstrated as useful tool for the prediction of biochemical enzyme activity of new substrates basing only on quantum chemical calculations and simple structural characteristics. Multi Linear Regression and Molecular Field Analysis (MFA) are used in order to compare robustness of ANN and both classical and 3D-quantitative structure–activity relationship (QSAR) approaches.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Abbreviations
- ANN:
-
Artificial Neural Network
- DFT:
-
Density Functional Theory
- EBDH:
-
Ethylbenzene Dehydrogenase
- G/PLS:
-
Genetic Partial Least Square
- LFER:
-
Linear Free Energy Relationship
- MLP:
-
Multi-Layer Perceptron
- MLR:
-
Multiple Linear Regression
- MFA:
-
Molecular Field Analysis
- QSAR:
-
Linear Quantitative Structure–Activity Relationship
- SNN:
-
Statistica Neural Networks
- 3D-QSAR:
-
Three-Dimensional Linear Quantitative Structure–Activity Relationship
References
Kniemeyer O, Heider J (2001) J Biol Chem 276:21381
Johnson HA, Pelletier DA, Spormann AM (2001) J Bacteriol 183:4536
Hammett LP (1936) J Chem Phys 4:613
Taft RW, Lewis IC (1958) J Am Chem Soc 80:2436
Shorter J, Chapman NB (ed) (1978) Correlation analysis in chemistry recent advances. Plenum Press, New York and London
Funar-Timofei S, Suzuki T, Paier JA, Steinreiber A, Faber K, Fabian WMF (2003) J Chem Inf Comput Sci 43:934
Bravo S, Diez MC, Shene C (2004) Braz J Chem Eng 21:509
Mager PP, Weber A (2003) Drug Des Discov 18:127
Bucinski A, Nasal A, Kaliszan R (2000) Comb Chem High Throughput Screen 3:525
Nasal A, Bucinski A, Baczek T, Wojdelko A (2004) Comb Chem High Throughput Screen 7:313
Fukui K (1982) Science (Washington DC) 218:747
Singh PP, Srivastava HK, Pash FA (2004) Bioorgan Med Chem 12:171
Nalewajski R (2001) Podstawy i metody chemii kwantowej. Wydawnictwo Naukowe PWN, Warszawa
Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA,␣Cheeseman JR, Montgomery JA Jr, Vreven T, Kudin KN, Burant JC, Millam JM, Iyengar SS, Tomasi J, Barone V,␣Mennucci B, Cossi M, Scalmani G, Rega N, Petersson GA, Nakatsuji H, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Klene M, Li X, Knox JE, Hratchian HP, Cross JB, Bakken V,␣Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Ayala PY, Morokuma K, Voth GA, Salvador P, Dannenberg JJ, Zakrzewski VG, Dapprich S, Daniels AD, Strain MC, Farkas O, Malick DK, Rabuck AD, Raghavachari K, Foresman JB, Ortiz JV, Cui Q, Baboul AG, Clifford S, Cioslowski J, Stefanov BB, Liu G, Liashenko A, Piskorz P, Komaromi I, Martin RL, Fox DJ, Keith T, Al-Laham MA, Peng CY, Nanayakkara A, Challacombe M, Gill PMW, Johnson B, Chen W, Wong MW, Gonzalez C, Pople JA (2004) Gaussian 03, Revision D.01. Gaussian, Inc., Wallingford, CT
Becke ADJ (1993) Chem Phys 98:5648
Mulliken RS (1955) J Chem Phys 23:1833
Reed AE, Curtiss LA, Weinhold F (1988) Chem Rev 88:899
Wolinski K, Hilton JF, Pulay P (1990) J Am Chem Soc 112:8251
Hansch C, Leo A (1995) Exploring QSAR. Fundamentals and application in chemistry and biology. ACS Processional Reference Book, American Chemical Society, Washington DC
Accelrys Inc. (2005) Cerius2 modeling environment, release 4.8. Accelrys Software Inc., San Diego
Gasteiger J, Marsili M (1980) Tetrahedron 36:3219
Rappe AK, Casewit CJ, Colwell KS, Goddard WA, Skiff WM (1992) J Am Chem Soc 114:10024
Rogers D, Hopfinger AJ (1994) J Chem Inf Comput Sci 34:854
Glen WG, Dunn WJ III, Scott DR (1989) Tetrahedron Comput Methodol 2:349
Hunter A, Kennedy L, Henry J, Ferguson RI (2000) Comput Methods Prog Biomed 62:11
Cramer RD III, Patterson DE, Bunce JD (1988) J Am Chem Soc 110:5959
Horzyk A, Tadeusiewicz R (2005) In: Mira J, Alvarez JT (eds) Mechanism, symbols, and models underlying cognition. Lecture Notes in Computer Science, vol 3561, Part I. Springer-Verlag, Berlin Heidelberg New York, pp 156–165
Braspenning PJ, Thuijsman F, Weijters AJMM (ed) (1995) Artificial neural networks an introduction to ANN theory and practice. Lecture Notes in Computer Science, vol 931. Springer Verlag
Acknowledgments
State Committee for Scientific Research (KBN) has supported this research under grant KBN/SGI2800/PAN/037/2003 and 3T09A06228. Maciej Szaleniec acknowledges a PhD grant of Polish Academy of Sciences.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Szaleniec, M., Witko, M., Tadeusiewicz, R. et al. Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase. J Comput Aided Mol Des 20, 145–157 (2006). https://doi.org/10.1007/s10822-006-9042-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10822-006-9042-6