Computationally Efficient Yet Quantitatively Accurate Scaled MP2 Protocols for the Prediction of Weak Interaction Energies in Complex Biological Systems
Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide In this study, we introduce a set of novel computational strategies based on second-order Mo̷ller–Plesset perturbation theory (MP2), enhanced through acceleration techniques, such as the resolution of the identity (RI). These approaches are further refined via spin-component scaling (SCS), following Grimme’s methodology, and are specifically calibrated for the quantitatively accurate prediction of weak interaction energies─interactions that play a critical role in biological systems. Among the developed methods, three variants exhibit outstanding performance, surpassing the accuracy of several state-of-the-art, nondynamical electronic structure techniques. Benchmarking against a comprehensive data set of 274 dimerization energies, computed at the CCSD(T)/CBS level of theory, reveals that these models deliver quantitatively accurate interaction energies. In particular, the RIJCOSX-SCS-MP2 BWI-DZ method, employing uniquely optimized scaling parameters, demonstrates exceptional accuracy (errors below 1 kcal/mol) while maintaining computational efficiency superior to widely used hybrid and meta-GGA density functional approximations (DFAs). This method reliably captures a range of biologically relevant interactions, including π–π stacking between nucleotide base pairs, halogen bonding, and dissociation energy profiles, showcasing its robustness and predictive power. Given its accuracy, efficiency, and versatility, RI-SCS-MP2 BWI-DZ, RIJK-SCS-MP2 BWI-DZ, and RIJCOSX-SCS-MP2 BWI-DZ emerge as promising and reliable alternatives for modeling weak interactions in complex biological environments.
Source: ACS Omega
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,935
Awards: G4798001518, G7943141133
OpenAlex ID: W4413352837