About SMARTCyp

History of SMARTCyp

The work that has lead to the development of SMARTCyp started in 2006 with investigations showing that simple fragment rules could be devised for P450 oxidations at aliphatic carbon sites from DFT calculations of transition states using a porphyrin model [1]. For a set of relatively simple model compounds the fragment rules were shown to be as good as AM1 or B3LYP calculations using smaller model systems. In 2009, the aliphatic oxidations were combined with data from DFT calculations on aromatic sites [2] as well as new data. It was shown that a model for carbon atoms could be combined with docking into a crystal structure to get good accuracy for the prediction of metabolites from the CYP1A2 isoform [3]. In 2010, the SMARTCyp method [4] was created. SMARTCyp uses all data from DFT calculations on aliphatic and aromatic hydroxylations as well as on hetero-atom oxidations [5] and lots of new data. Data from 211 transition state calculations have been used to create the fragment based energy rules in SMARTCyp 1.5 [6]. In 2011 and 2012, models for prediction of CYP2D6 and CYP2C9 specific metabolism were included in SMARTCyp [7,8]. This was released with version 2.0 and 2.1 of SMARTCyp. Also in 2012, improvements for N-oxidations of tertiary amines were included, specifically an empirical corrections to unlikely oxidations of tertiary alkylamines [9]. This was released with version 2.3 of SMARTCyp. Finally, in 2013, the solvent accessible surface area was included in the scoring function through the novel 2DSASA algorithm that estimates the atomic SASA from the 2D structure [10].

Current version of SMARTCyp

SMARTCyp was developed by Patrik Rydberg and Lars Olsen. The program was a Java program using the CDK library. The original SMARTCyp webserver calling the java version is available at: http://smartcyp.sund.ku.dk. The current version of SMARTCyp has been implemented in Python 3 and uses RDKit to handle the molecules, e.g. for the SMARTS macthing. A number of the rules have been revised, e.g. because the perception of the aromatic atoms is different in RDKit. A few new rules have also been implemented, e.g. the possible hydrogen abstraction of phenolic H atoms [11] and new aromatic C oxidation rules [12].

Developers

The current Python version of SMARTCyp has been developed by Marco Montefiori, Khanhvi Phuc Tran, Flemming S Jørgensen, and Lars Olsen.

SMARTCyp references

1. L. Olsen, P. Rydberg, T. H. Rod, U. Ryde. Prediction of Activation Energies for Hydrogen Abstraction by Cytochrome P450 J. Med. Chem., 2006, 49, 6489-6499
2. P. Rydberg, U. Ryde, L. Olsen. Prediction of Activation Energies for Aromatic Oxidation by Cytochrome P450 J. Phys. Chem. A, 2008, 112, 13058-13065
3. P. Rydberg, P. Vasanthanathan, C. Oostenbrink, L. Olsen. Fast Prediction of Cytochrome P450 Mediated Drug Metabolism ChemMedChem, 2009, 4, 2070-2079
4. P. Rydberg, D. E. Gloriam, J. Zaretzki, C. Breneman, L. Olsen. SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism ACS Med. Chem Lett., 2010, 1, 96-100
5. P. Rydberg, U. Ryde, L. Olsen. Sulfoxide, Sulfur, and Nitrogen Oxidation and Dealkylation by Cytochrome P450 J. Chem. Theory Comput., 2008, 4, 1369-1377
6. P. Rydberg, D.E. Gloriam, L. Olsen. The SMARTCyp cytochrome P450 metabolism prediction server Bioinformatics, 2010, 26, 2988-2989
7. P. Rydberg and L. Olsen. Ligand-based Site of Metabolism Prediction for Cytochrome P450 2D6 ACS Med. Chem Lett., 2012, 3, 69-73
8. P. Rydberg and L. Olsen. Predicting Drug Metabolism by Cytochrome P450 2C9 - Comparison to the 2D6 and 3A4 Isoforms ChemMedChem, 2012, 7, 1202-1209
9. P. Rydberg et al.,Nitrogen Inversion Barriers Affect the N-Oxidation of Tertiary Alkylamines by Cytochromes P450 Angew. Chem, Int. Ed. 2013, 52, 993-997
10. P. Rydberg et al., The Contribution of Atom Accessibility to Site of Metabolism Models for Cytochromes P450 Mol. Pharmaceutics 2013, 10, 1216-1223
11. R. Leth, F. S. Jorgensen, L. Olsen Density functional theory study on the formation of reactive benzoquinone imines by hydrogen abstraction J. Chem. Inf. Model. 2015, 55, 660-666
12. S. Bonomo, F. S. Jorgensen, L. Olsen Dissecting the Cytochrome P450 1A2- and 3A4-Mediated Metabolism of Aflatoxin B1 in Ligand and Protein Contribution Chem. Eur. J. 2017, 23, 2884-2893