In quantum chemistry calculations, energy decomposition analysis (EDA) approaches enable the partitioning of the interaction energy of a complex (such as a protein-ligand complex) into energy components of chemical interest, such as electrostatic, exchange, charge transfer and polarisation terms. In biomolecular association events in particular, the insights that EDA can produce can be invaluable for the fine-tuning of interactions, as is necessary for applications such as drug optimisation. No unique definition exists for the components of EDA, and for this reason a large number of methods have been proposed. A prototypical EDA approach is the popular Kitaura-Morokuma (KM) scheme in which intermediate wave functions that express specific chemical properties are used to partition the supermolecular interaction energy. From this scheme, many other EDA methods have evolved. We have recently assessed these methods in terms of their chemical relevance on protein-drug interaction patterns1. From our investigation, the absolutely localised molecular orbital (ALMO) EDA was found to be particularly well suited for such interactions. So far, EDA calculations have generally been limited to molecules with no more than a few tens of atoms1. We have implemented an EDA scheme in the ONETEP linear-scaling DFT package2 based on the ALMO scheme and the localised molecular orbital (LMO) scheme. Our method provides a detailed decomposition of the interaction energy in terms of electrostatic, exchange, correlation, Pauli-repulsion, polarisation and charge transfer components. We have applied our approach to a test set of small interacting molecular complexes which display interactions typically encountered in protein-ligand systems. Future directions will include applications on entire biomolecules with thousands of atoms. References: [1] M. J. S. Phipps, T. Fox, C. S. Tautermann, C.-K. Skylaris, Chem. Soc. Rev., (2015), 44, 3177-3211. [2] C.-K. Skylaris, P. D. Haynes, A. A. Mostofi, and M. C. Payne. J. Chem. Phys., (2005), 122, 084119.

Energy decomposition analysis for linear-scaling DFT calculations in drug design

251st National Meeting (2016)

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