Nowadays, hybrid QM/MM approaches are widely used to study (supra)molecular systems embedded in complex biological matrices. However, in their common formulation, mutual interactions between the quantum and classical parts are neglected. To go beyond such a picture, a polarizable embedding can be used. In this perspective, we focus on the induced point dipole formulation of polarizable QM/MM approaches and we show how efficient and linear scaling implementations have allowed their application to the modeling of complex biosystems. In particular, we discuss their use in the prediction of spectroscopies and in molecular dynamics simulations, including Born-Oppenheimer dynamics, enhanced sampling techniques and nonadiabatic descriptions. We finally suggest the theoretical and computational developments that still need to be achieved to overcome the limitations which have prevented so far larger diffusion of these methods.

Polarizable embedding QM/MM: the future gold standard for complex (bio)systems?

Bondanza, Mattia;Nottoli, Michele;Cupellini, Lorenzo;Lipparini, Filippo;Mennucci, Benedetta
2020-01-01

Abstract

Nowadays, hybrid QM/MM approaches are widely used to study (supra)molecular systems embedded in complex biological matrices. However, in their common formulation, mutual interactions between the quantum and classical parts are neglected. To go beyond such a picture, a polarizable embedding can be used. In this perspective, we focus on the induced point dipole formulation of polarizable QM/MM approaches and we show how efficient and linear scaling implementations have allowed their application to the modeling of complex biosystems. In particular, we discuss their use in the prediction of spectroscopies and in molecular dynamics simulations, including Born-Oppenheimer dynamics, enhanced sampling techniques and nonadiabatic descriptions. We finally suggest the theoretical and computational developments that still need to be achieved to overcome the limitations which have prevented so far larger diffusion of these methods.
2020
Bondanza, Mattia; Nottoli, Michele; Cupellini, Lorenzo; Lipparini, Filippo; Mennucci, Benedetta
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1046428
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