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NCIPLOT4: A New Step Towards a Fast Quantification of Noncovalent Interactions

revised on 26.01.2020, 15:50 and posted on 27.01.2020, 12:34 by Roberto Boto, Francesca Peccati, Rubén Laplaza, chaoyu quan, Alessandra Carbone, Jean-Philip Piquemal, Yvon Maday, Julia Contreras-García

The quantification of noncovalent interactions in big systems is of crucial importance for understanding the structure and function of biosystems. The NCI method [J. Am. Chem. Soc. 132 , 6498 (2010)] enables to identify attractive and repulsive noncovalent interactions from promolecular densities in a fast manner. However, the approach remained up to now visual/qualitative, the relationship with energetics was conspicuously missing. We present a new version of NCIPLOT which allows quantifying the properties of the NonCovalent Interaction (NCI) regions in a fast manner. In order to do so, the definition of NCI volumes is introduced, which allows quantification of intra and intermolecular NCI properties in big systems where wavefunctions are not available. The connection between these integrals and energetics is reviewed for benchmark systems (S668), showing that our simple approach can lead to GGAquality energies while scaling with the number of atoms involved in the interaction (not the total number of atoms). The new implementation also includes an adaptive grid which allows the computation in a fast, parallelizable and efficient computational environment. The relationship with energetics derived from force fields is highlighted
and the faster algorithm exploited to analyze the evolution of interactions along MD trajectories. Through machine learning algorithms we characterize the relevance of NCI integrals in understanding the energetics of big systems, which is then applied in revealing the energetic changes along conformational changes, as well as identifying the atoms involved. This simple approach enables to identify the driving forces in biomolecular structural changes both at the spatial and energetic levels, while going beyond a mere parametrized-distances analysis.


All authors express their gratefulness to LabEx CALSIMLAB (public grant ANR-11-LABX- 0037-01 constituting a part of the "Investissements d’Avenir" program - reference : ANR-11- IDEX-0004-02) RL thanks ED 388 for a PhD grant. YM and CQ acknowledge the funding from the PICS-CNRS and the PHC PROCOPE 2017 (Project No.37855ZK). AC acknowledges the Institut Universitaire de France.


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