Nonbonded Force Field Parameters Derived from Atoms-in-Molecules Methods Reproduce Interactions in Proteins from First-Principles
Bibliographic record
Abstract
Noncovalent interactions govern many chemical and biological phenomena and are crucial in protein–protein interactions, enzyme catalysis, and DNA folding. The size of these macromolecules and their various conformations demand computationally inexpensive force fields that can accurately mimic the quantum chemical nature of the atomic noncovalent interactions. Accurate force fields, coupled with increasingly longer molecular dynamics simulations, may empower us to predict conformational changes associated with the biochemical function of proteins. Here, we aim to derive nonbonded protein force field parameters from the partitioned electron density of amino acids, the fundamental units of proteins, via the atoms-in-molecules (AIM) approach. The AIM parameters are validated using a database of charged, aromatic, and hydrophilic side-chain interactions in 610 conformations, primarily involving π–π interactions, as recently reported by one of us (Carter-Fenk et al., 2023). Electrostatic and van der Waals interaction energies calculated with nonbonded force field parameters from different AIM methodologies were compared to first-principles interaction energies from absolute localized molecular orbital-energy decomposition analysis (ALMO-EDA) at the ωB97XV/def2-TZVPD level. Our findings show that electrostatic interactions between side chains are accurately reproduced by atomic charges from the minimal basis iterative stockholder (MBIS) scheme with mean absolute errors of 4–7 kJ/mol. Meanwhile, C 6 coefficients from the MBIS AIM method effectively predict dispersion interactions with a mean error of −2 kJ/mol and a maximal error of −5 kJ/mol. As an outlook to use AIM methods in the development of protein force fields, we present the constrained AIM method that allows one to fix backbone parameters during the optimization of side-chain interactions. Backbone dihedral parameters have been optimized to reproduce secondary structure elements in proteins, and not altering them maintains compatibility with conventional protein force fields while improving the description of side-chain interactions. Our validated AIM methods allow for the depiction of noncovalent, long-range interactions in proteins using cost-effective force fields that achieve chemical precision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".