QSPR modeling of optical rotation of amino acids using specific quantum chemical descriptors
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Many chemical phenomena occur in solution. Different solvents can change the optical activity of chiral molecules. The optical rotation angles of solutes of 75 amino acids in dimethylformamide, water and methanol were analyzed using the quantitative structure–activity relationships approach. For an accurate description of chirality, we used specific quantum chemical descriptors, which reflect the properties of a chiral center, and continuous symmetry measures. The set of specific quantum chemical descriptors for atoms located near the chiral center, such as Mulliken charges, the sum of Mulliken charges on an atom (with the hydrogen charges summed up with the adjacent non-hydrogen atoms), and nuclear magnetic resoncance tensors was applied. To represent solvent effects, we used mixture-like structural simplex descriptors and quantum chemical descriptors obtained for structures optimized for specified solvent using PBE1PBE/6-31G** level of theory with the polarizable continuum model. Multiple linear regression, M5P, and locally weighted learning techniques were used to achieve accurate predictions. The specific quantum chemical descriptors proposed here demonstrated high specificity in the majority of the developed models and established direct quantitative structure–property relationships.
KeywordsAmino acid QSPR Optical rotation Chirality NMR tensors Quantum chemical descriptors Molecular descriptors
The authors gratefully acknowledge the funding of this research by the Ministry of Education and Science of Ukraine (Project #0116U001520), National Science Foundation (NSF/CREST HRD-1547754) and PREM (DMR-1205194) grants. This work also used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation (grant #ACI-1053575).
- 6.Maury G (2000) The enantioselectivity of enzymes involved in current antiviral therapy using nucleoside analogues: a new strategy? Antivir Chem Chemother 165–189Google Scholar
- 8.Dearden JC (2016) The history and development of quantitative structure-activity relationships (QSARs). Oncol Break Res Pract Break Res Pract 67. https://doi.org/10.4018/IJQSPR.2016010101
- 10.Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Asteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977–5010. https://doi.org/10.1021/jm4004285 CrossRefGoogle Scholar
- 22.Eliel EL, Wilen SH (1994) Stereochemistry of organic compounds. Wiley, New YorkGoogle Scholar
- 25.Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenb DJ (2009) Gaussian 09, Gaussian, Inc., Wallingford CTGoogle Scholar
- 30.Kuz’min V, Artemenko A, Muratov E (2008) Hierarchical QSAR technology based on the Simplex representation of molecular structure. J Comput Aided Mol De. 22:403–421Google Scholar
- 32.KNIME.com (2017) KNIME https://www.knime.com/
- 34.Frank E, Hall M, Pfahringer B (2003) Locally weighted naive bayes. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence. Kaufmann, San Francisco, pp 249–256Google Scholar
- 42.Lutz O, Jirgensons B (1931) New method for the grouping of optically active a-amino acids in the dextro-or levo-series. II. Abhandlungen 64:1221–1232Google Scholar