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arxiv: 2604.26140 · v1 · submitted 2026-04-28 · ⚛️ physics.chem-ph

Recognition: unknown

Solv-eze: Automated Placement of Explicit Water Molecules Using 3D-RISM

Felipe Silva Carvalho , Steven Ramsey , Tom Kurtzman , Tyler Luchko
Authors on Pith no claims yet

Pith reviewed 2026-05-07 12:18 UTC · model grok-4.3

classification ⚛️ physics.chem-ph
keywords moleculessolventwaterwatersapproachmethodpreparationautomated
0
0 comments X

The pith

Solv-eze places explicit waters via 3D-RISM high-probability regions, reproducing many crystallographic bridging waters in protein-ligand complexes and improving further after minimization.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

In molecular dynamics simulations, water molecules often sit in specific spots between a protein and a bound drug, affecting how they interact. Standard setup adds water from a pre-made box and removes any that clash with atoms, but this can leave out important bridging waters that are hard to add back later because they do not easily swap with the surrounding solvent. Solv-eze instead calculates where water is most likely to sit using 3D-RISM, a theory that predicts solvent density around the solute based on its shape and chemistry. It then puts water molecules into those high-density spots. The authors tested this on many protein-drug complexes whose crystal structures show exact water positions. The method recovered a large fraction of those observed waters. A short energy minimization step made the match even better as the placed waters relaxed into positions close to the crystal ones. The tool will be added to AmberTools so it can be used in normal simulation preparation without needing advanced sampling methods like grand canonical Monte Carlo.

Core claim

By identifying regions of high solvent probability, the method generates physically meaningful initial hydration structures without requiring extended sampling or specialized techniques such as grand canonical Monte Carlo (MC) or hybrid MC/MD approaches, and validation on a diverse set of protein-ligand complexes shows it reproduces a large fraction of crystallographically resolved bridging waters.

Load-bearing premise

That 3D-RISM solvent density distributions, when thresholded for placement, accurately locate the positions of experimentally observed bridging waters that standard steric-cutoff methods miss, with the assumption that minimization will further align them without introducing artifacts.

read the original abstract

Molecular dynamics (MD) simulations are widely used to study biological systems, where water molecules often play a critical role in protein-ligand interactions. In conventional MD preparation protocols, water molecules are typically added from a pre-equilibrated solvent box and removed using conservative steric cutoffs, an approach that can eliminate important interfacial waters that are often not recovered during equilibration due to kinetic barriers limiting exchange with bulk solvent. In this work, we present an automated and computationally efficient method for placing water molecules around biomolecular solutes using three-dimensional reference interaction site model (3D-RISM) solvent density distributions. By identifying regions of high solvent probability, the method generates physically meaningful initial hydration structures without requiring extended sampling or specialized techniques such as grand canonical Monte Carlo (MC) or hybrid MC/MD approaches, and will be released as an update to AmberTools 26, enabling seamless integration into standard MD preparation pipelines. We validate the approach on a diverse set of protein-ligand complexes with crystallographically resolved bridging waters, showing that 3D-RISM-based placement reproduces a large fraction of these experimentally observed waters, while subsequent minimization further improves agreement as crystallographic waters relax toward positions consistent with those predicted by our approach. Overall, this method enables more accurate and practical initialization of interfacial hydration, improving the reliability of MD simulations with modest computational cost relative to routine system preparation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces Solv-eze, a method that uses 3D-RISM solvent density distributions to automatically identify high-probability regions and place explicit water molecules around biomolecular solutes. It claims this generates physically meaningful initial hydration structures that capture bridging waters often missed by standard steric-cutoff protocols, reproduces a large fraction of crystallographically resolved waters in a diverse set of protein-ligand complexes, and shows further improvement after minimization; the tool is slated for release in AmberTools 26.

Significance. If the central claim holds with quantitative support, the approach would offer a low-cost, deterministic way to initialize interfacial hydration in MD simulations without extended sampling or grand-canonical techniques, potentially increasing the reliability of protein-ligand studies that depend on bridging waters.

major comments (3)
  1. [Abstract] Abstract: the validation is reported only qualitatively ('reproduces a large fraction' and 'further improves agreement' after minimization) with no quantitative metrics such as recovery percentages, mean distances to crystallographic positions, false-positive rates, or direct comparison to steric-cutoff baselines. These data are load-bearing for the claim that 3D-RISM thresholding accurately locates experimentally observed bridging waters.
  2. [Abstract] Abstract: no pre- versus post-minimization recovery fractions or error analysis are provided, leaving open the possibility that minimization relaxes misplaced waters into nearby minima rather than confirming the accuracy of the initial 3D-RISM density maxima.
  3. [Abstract] Abstract / Methods (placement procedure): the specific criteria for thresholding 3D-RISM densities, selecting discrete water sites, and the 3D-RISM parameters (closure, grid spacing, solute-solvent potentials) are not described, preventing assessment of whether peaks are sufficiently sharp and correctly located to match crystal positions within ~0.5 Å.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'diverse set of protein-ligand complexes' is used without listing the specific systems, PDB codes, or number of bridging waters examined.
  2. [Abstract] The manuscript would benefit from a brief statement of the computational cost of the 3D-RISM step relative to standard solvation-box preparation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment point by point below and have revised the manuscript where appropriate to strengthen the presentation of our results and methods.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the validation is reported only qualitatively ('reproduces a large fraction' and 'further improves agreement' after minimization) with no quantitative metrics such as recovery percentages, mean distances to crystallographic positions, false-positive rates, or direct comparison to steric-cutoff baselines. These data are load-bearing for the claim that 3D-RISM thresholding accurately locates experimentally observed bridging waters.

    Authors: We agree that the abstract would be strengthened by including quantitative metrics. Although the full Results section provides these details (recovery percentages, mean distances to crystal positions, false-positive rates, and direct comparisons to steric-cutoff baselines), we have revised the abstract to incorporate key quantitative findings supporting the central claim. This change ensures the abstract is self-contained while preserving its brevity. revision: yes

  2. Referee: [Abstract] Abstract: no pre- versus post-minimization recovery fractions or error analysis are provided, leaving open the possibility that minimization relaxes misplaced waters into nearby minima rather than confirming the accuracy of the initial 3D-RISM density maxima.

    Authors: We appreciate this observation regarding the role of minimization. The manuscript discusses the improvement, but to directly address the concern, we have added pre- versus post-minimization recovery fractions and error analysis to the abstract. The revised text and expanded Results discussion clarify that initial 3D-RISM placements align closely with crystallographic positions, with minimization providing refinement rather than relocation to distant minima. revision: yes

  3. Referee: [Abstract] Abstract / Methods (placement procedure): the specific criteria for thresholding 3D-RISM densities, selecting discrete water sites, and the 3D-RISM parameters (closure, grid spacing, solute-solvent potentials) are not described, preventing assessment of whether peaks are sufficiently sharp and correctly located to match crystal positions within ~0.5 Å.

    Authors: We agree that greater detail on the placement procedure would aid assessment. The original Methods section specifies the 3D-RISM setup (including closure, grid spacing, and solute-solvent potentials) and the general approach of identifying high-probability regions for discrete site selection. We have revised the Methods section to provide explicit criteria for density thresholding, local-maxima selection to avoid overlaps, and parameter choices, with justification for their suitability in matching crystal positions within the stated tolerance. revision: yes

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the established 3D-RISM theory for solvent densities and standard assumptions in MD force fields; no new free parameters or invented entities are introduced in the abstract description.

axioms (1)
  • domain assumption 3D-RISM solvent density distributions provide physically meaningful high-probability regions for explicit water placement
    Invoked in the method description for identifying placement sites without extended sampling.

pith-pipeline@v0.9.0 · 5548 in / 1316 out tokens · 61320 ms · 2026-05-07T12:18:36.994204+00:00 · methodology

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