How to Compute Atomistic Insight in DFT clusters: the REG-IQA Approach

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The Relative Energy Gradient (REG) method is paired with the topological energy partitioning method Interacting Quantum Atoms (IQA), as REG-IQA, to provide detailed and unbiased knowledge on the intra- and interatomic interactions. REG operates on a sequence of geometries representing a dynamical change of a system. Its recent application to the peptide hydrolysis of the HIV-1 protease (PDB Code: 4HVP) has demonstrated its full potential in recovering reaction mechanisms and through-space electrostatic and exchange-correlation effects, making it a compelling tool for analysing enzymatic reactions. In this study, the computational efficiency of the REG-IQA method for the 133-atom HIV-1 protease quantum mechanical system is analysed in every detail and substantially improved by means of three different approaches. The first approach of smaller integration grids for IQA integrations reduces the computational overhead by about a factor 3. The second approach uses the line-simplification Ramer-Douglas-Peucker (RDP) algorithm, which outputs the minimal number of geometries necessary for the REG-IQA analysis for a predetermined Root Mean Squared Error (RMSE) tolerance. This cuts the computational time of the whole REG analysis by a factor of 2 if an RMSE of 0.5 kJ/mol is considered. The third approach consists of a “biased” or “unbiased” selection of a specific subset of atoms of the whole initial quantum mechanical model wave-function, which results in more than a 10-fold speed-up per geometry for the IQA calculation, without deterioration of the outcome of the REG-IQA analysis. Finally, to show the capability of these approaches, the findings gathered from the HIV-1 protease system are also applied to a different system named HheC. In summary, this study takes the REG-IQA method to a computationally feasible and highly accurate level making it viable for the analysis of a multitude of enzymatic systems.
Original languageEnglish
JournalJournal of Chemical Information and Modeling
Publication statusAccepted/In press - 19 May 2023


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