Calculation of absolute protein-ligand binding free energy using distributed replica sampling
Bibliographic record
Abstract
Distributed replica sampling [T. Rodinger et al., J. Chem. Theory Comput. 2, 725 (2006)] is a simple and general scheme for Boltzmann sampling of conformational space by computer simulation in which multiple replicas of the system undergo a random walk in reaction coordinate or temperature space. Individual replicas are linked through a generalized Hamiltonian containing an extra potential energy term or bias which depends on the distribution of all replicas, thus enforcing the desired sampling distribution along the coordinate or parameter of interest regardless of free energy barriers. In contrast to replica exchange methods, efficient implementation of the algorithm does not require synchronicity of the individual simulations. The algorithm is inherently suited for large-scale simulations using shared or heterogeneous computing platforms such as a distributed network. In this work, we build on our original algorithm by introducing Boltzmann-weighted jumping, which allows moves of a larger magnitude and thus enhances sampling efficiency along the reaction coordinate. The approach is demonstrated using a realistic and biologically relevant application; we calculate the standard binding free energy of benzene to the L99A mutant of T4 lysozyme. Distributed replica sampling is used in conjunction with thermodynamic integration to compute the potential of mean force for extracting the ligand from protein and solvent along a nonphysical spatial coordinate. Dynamic treatment of the reaction coordinate leads to faster statistical convergence of the potential of mean force than a conventional static coordinate, which suffers from slow transitions on a rugged potential energy surface.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".