Water Model Research Articles

Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across key hydrological processes. By critically evaluating these techniques against traditional models, we highlight their superior ability to capture complex, nonlinear relationships and adapt to diverse environments. We further explore AI applications in precipitation forecasting, evapotranspiration estimation, groundwater dynamics, and extreme event prediction (floods, droughts, and compound events), showcasing their timely potential in addressing critical water-related challenges. A particular emphasis is placed on Explainable AI (XAI) and transfer learning as essential tools for improving model transparency and applicability, enabling broader stakeholder trust and cross-regional adaptability. The review also addresses persistent challenges, including data limitations, computational demands, and model interpretability, proposing solutions that integrate emerging technologies like quantum computing, the Internet of Things (IoT), and interdisciplinary collaboration. This review charts a strategic course for future research and practice by bridging AI advancements with practical hydrological applications. Our findings highlight the importance of embracing AI-driven approaches for next-generation hydrological modeling and provide actionable understandings for researchers, practitioners, and policymakers. As hydrology faces escalating challenges due to human-induced climate change and growing water demands, the continued evolution of AI-integrated models and innovations in data handling and stakeholder engagement will be imperative. In conclusion, the findings emphasize the critical role of AI-driven hydrological modeling in addressing global water challenges, including climate change adaptation, sustainable water resource management, and disaster risk reduction.

Insights of Dynamic Forcing Effects of MJO on ENSO from a Shallow Water Model

Abstract The Madden–Julian oscillation (MJO) is believed to play a significant role in triggering El Niño–Southern Oscillation (ENSO) events and affect the dynamics of ENSO. In this study, the dynamic forcing effects of MJO on the equatorial oceanic dynamic fields and the onsets of different types of ENSO events are investigated through sensitive experiments using spatiotemporally filtered forcing based on an anomalous shallow water model. The comparisons between observations and model responses provide meaningful insights into the extent of MJO’s impacts on sea surface dynamics relative to other atmospheric variabilities. The following conclusions are made. First, the MJO-forced perturbations on zonal currents are stronger and more significant than those on sea surface heights. Second, MJO is essential for improving zonal current simulation in the western-central Pacific and generating activity centers of zonal currents in the eastern Pacific in the model. Third, MJO tends to contribute to the onset of El Niño events rather than La Niña events. Strong intraseasonal oceanic Kelvin waves forced by MJO are confirmed in simulations during the onset stages of the 1997/98 and 2004/05 events. The 120-day running standard deviations of zonal current and sea surface height anomaly series forced by MJO exhibit positive skewness similar to those of the 20–100-day band-passed observational series. Yet, not all the onsets of historical ENSO events are in company with strong MJO-related perturbations. Additionally, the wind stress formula can amplify the responses of zonal current and sea surface height anomalies to synoptic forcings with periods shorter than 20 days through entraining lower-frequency variabilities. Significance Statement The Madden–Julian oscillation (MJO) is believed to be able to trigger El Niño–Southern Oscillation (ENSO) events and influence our understanding of the fundamental nature of ENSO. In this study, spatiotemporally filtered forcing experiments are implemented on an anomalous shallow water model. The results show that MJO is more important for improving the simulation of surface zonal currents rather than the sea surface heights and tends to contribute to the onset of El Niño events rather than La Niña events through triggering strong intraseasonal oceanic Kelvin waves.

Solvent-Directed Social Chiral Self-Sorting in Pd2L4 Coordination Cages.

A family of Pd2L4 cages prepared from ligands based on an axially chiral diamino-[1,1'-biazulene] motif (serving as a unique azulene-based surrogate of the ubiquitous BINOL moiety) is reported. We show that preparing a cage starting from the racemate of a shorter bis-monodentate ligand derivative, equipped with pyridine donor groups, leads to integrative ("social") chiral self-sorting, exclusively yielding the meso-trans product, but only in a selection of solvents. This phenomenon is driven by individual solvent molecules acting as hydrogen bonding tethers between the amino groups of neighboring ligands, thereby locking the final coordination cage in a single isomeric form. The experimental (solvent-dependent NMR, single-crystal X-ray diffraction) observations of this cooperative interaction could be explained by computational analyses only when explicit solvation was considered. Furthermore, we prepared a larger chiral ligand with isoquinoline donors, which, unlike the first one, does not undergo social self-sorting from its racemic mixture, further highlighting the importance of solvents bridging short distances between the amino groups. Homochiral cages formed from this larger ligand, however, furnish a cavity that can bind anionic and neutral metal complexes such as [Pt(CN)6]2- and Cr(CO)6 and discriminate between the two enantiomers of chiral guest camphor sulfonate.

A computational study of the size effect of SiO2 spherical nanoparticles in water solvent.

This study comprehensively describes the interaction between SiO2 spherical nanoparticles and water molecules as a solvent medium. Our goal is to provide valuable insights into the significance of nanoparticle size in understanding their behavior and the resulting changes in the physical properties of materials. Our results indicate that SiO2 nanoparticles exhibit a strong affinity for water, which increases with the nanoparticle size. Our investigation can be relevant for the design of new composite materials with applications ranging from medical prostheses to quantum electronics, optoelectronic devices, catalysis, and photoluminescence. We have concentrated on the study of the amorphous, where size effects seem to be more pronounced. A computational study was carried out within the molecular dynamics simulations framework available in the GROMACS-v2019.2 software, with force fields consistent with DFTand the CHARMM36 utilized in the molecular description of the systems. The water model used was the TIP3P implemented in CHARMM36 force fields. A comprehensive analysis of molecular interactions of various system configurations was performed, including radial distribution function (RDF), mean square displacement (RMSD), hydrogen bonding analysis,interfacial analysis, andstudying system size'seffect on mechanical properties.

Streamflow simulation improvements enabled by a state‐of‐the‐art algorithm for reservoir routing in the U.S. National Water Model

AbstractThis study investigates whether improvements could be achieved for the United States National Water Model (NWM) by using a data‐driven reservoir operation simulation algorithm, that is, the generic data‐driven reservoir operation model (GDROM), to replace the existing reservoir representation in NWM for reservoir routing in streamflow simulation. The evaluations of NWM versus NWM + GDROM are conducted using 41‐year NWM retrospective simulation products during 1979 to 2020. The results show that NWM + GDROM exhibits significant improvement to NWM in both the entire Contiguous U.S. (CONUS) and various regions. It is found that the accuracy of reservoir inflow and storage values affects the NWM + GDROM improvement. When driven by observed storage (which is usually more accurate than modeled storage) or more accurately simulated inflow, NWM + GDROM produces more significant improvements in reservoir outflow simulations in all the study CONUS regions. Especially, since high flows are more accurately simulated by NWM than low flows, NWM + GDROM has larger improvement for the simulation of high flows than low flows.

Revised 4-Point Water Model for the Classical Drude Oscillator Polarizable Force Field: SWM4-HLJ.

In this work the 4-point polarizable SWM4 Drude water model is reparametrized. Multiple models were developed using different strategies toward reproduction of specific target data. Results indicate that no individual model can reproduce all the selected target data in the context of the present form of the potential energy function. The changes considered in the new models include, 1) variations in the gas phase dipole moment, 2) variations in the molecular polarizability, 3) variations of the distance between the oxygen and the M site, 4) variation of the oxygen Lennard-Jones (LJ) parameters, 5) introduction of a LJ potential to the hydrogen atoms, and 6) variations of the H-O-H angle. Detailed analysis is presented for 3 new water models from which a final model, SWM4-HLJ, is selected as the future default model for the Drude polarizable force field. The model maintains the gas phase dipole moment as the experimental value while the remaining listed terms were adjusted including a larger H-O-H angle (108.12). Compared to its predecessor, SWM4-NDP, the self-diffusion coefficient, water dimer properties, and water cluster energies are greatly improved. The temperature dependence of the density of the new model also performs better. Overall, the new SWM4-HLJ water model is a general improvement and a good balance between microscopic and bulk properties is achieved.

Longitudinal and transverse collective dynamics in water by simulation using the BK3 model.

Using a recent polarizable model for water (the BK3 model), we explore the collective dynamic modes in liquid water by molecular dynamics (MD) simulation. The dynamic structure factor and the longitudinal and transverse current correlation spectral densities are computed over the whole frequency range below intramolecular excitations. MD results of atom-atom partial correlation functions are fitted using the Generalized Collective Modes (GCMs) model, involving relaxing modes occurring in the longitudinal component and propagating modes occurring in both components. Three systems are studied as follows: (1) BK3 ambient water, (2) SPC/E ambient water, and (3) BK3 ambient heavy water. Comparison between the results of these systems reveals the influence of the polarizability, or the influence of the molecular mass, on the collective dynamics. Moreover, the GCM fitting allows a quantitative description of the excitation modes in terms of the frequencies, damping coefficients and possible coupling between longitudinal and transverse modes. The differences between the three situations are also clearly evidenced within this formalism.

Likely Overstabilization of Charge-Charge Interactions in CHARMM36m(w): A Case for a99SB-disp Water.

Recent years have witnessed drastic improvements in general-purpose explicit solvent protein force fields, partially driven by the need to study intrinsically disordered proteins (IDPs), and yet the state-of-the-art force fields such as CHARMM36m (c36m) and a99SB-disp still provide different performances in simulating disordered protein states, where c36m has a bias toward overcompaction for large IDPs. Here, we examine the performance of c36m and a99SB-disp in describing the stabilities of a set of 46 amino acid backbone and side chain pairs in various configurations. The free energy results show that c36m systematically predicts stronger interactions compared to a99SB-disp by an average of 0.2 kcal/mol for nonpolar pairs, 0.6 kcal/mol for polar pairs, and 0.8 kcal/mol for salt bridges. The most severe overstabilization in c36m is observed for charged pairs involving the Arg and Glu side chains by up to 2.9 kcal/mol. Importantly, the systematic overstabilization of c36m is only marginally alleviated by c36mw, an ad hoc patch to c36m that increases the dispersion interactions between TIP3P hydrogens and protein atoms. Guided by free energy decomposition, we evaluated if revising the charges alone could alleviate the severe overstabilization of salt bridges of c36m(w) vs a99SB-disp. The results suggested that the direct modification of protein-water interactions is also necessary. Toward this end, we proposed a tentative modification to c36m, referred to as c36mrb-disp, which combines modified Arg side chain charges, retuned backbone hydrogen bonding strength, and the a99SB-disp water model. The modified force field successfully reproduces the secondary structures of several intrinsically disordered peptides and proteins including (AAQAA)3, GB1p, and p53 transactivation domain, while maintaining the stability of a set of folded proteins. This work provides a set of useful systems for benchmarking and optimizing protein force fields and highlights the importance of balancing protein-protein and protein-water electrostatic interactions for accurately describing both folded and disordered proteins.

Nonadiabatic Excited-State Molecular Dynamics with an Explicit Solvent: NEXMD-SANDER Implementation.

In this article, the nonadiabatic excited-state Molecular dynamics (NEXMD) package is linked with the SANDER package, provided by AMBERTOOLS. The combination of these software packages enables the simulation of photoinduced dynamics of large multichromophoric conjugated molecules involving several coupled electronic excited states embedded in an explicit solvent by using the quantum/mechanics/molecular mechanics (QM/MM) methodology. The fewest switches surface hopping algorithm, as implemented in NEXMD, is used to account for quantum transitions among the adiabatic excited-state simulations of the photoexcitation and subsequent nonadiabatic electronic transitions, and vibrational energy relaxation of a substituted polyphenylenevinylene oligomer (PPV3-NO2) in vacuum and methanol as an explicit solvent has been used as a test case. The impact of including specific solvent molecules in the QM region is also analyzed. Our NEXMD-SANDER QM/MM implementation provides a useful computational tool to simulate qualitatively solvent-dependent effects, like electron transfer, stabilization of charge-separated excited states, and the role of solvent reorganization in the molecular optical properties, observed in solution-based spectroscopic experiments.

Force Field Limitations of All-Atom Continuous Constant pH Molecular Dynamics.

All-atom constant pH molecular dynamics simulations offer a powerful tool for understanding pH-mediated and proton-coupled biological processes. As the protonation equilibria of protein side chains are shifted by electrostatic interactions and desolvation energies, pKa values calculated from the constant pH simulations may be sensitive to the underlying protein force field and water model. Here we investigated the force field dependence of the all-atom particle mesh Ewald (PME) continuous constant pH (PME-CpHMD) simulations of a mini-protein BBL. The replica-exchange titration simulations based on the Amber ff19sb and ff14sb force fields with the respective water models showed significantly overestimated pKa downshifts for a buried histidine (His166) and for two glutamic acids (Glu141 and Glu161) that are involved in salt-bridge interactions. These errors (due to undersolvation of neutral histidines and overstabilization of salt bridges) are consistent with the previously reported pKa's based on the CHARMM c22/CMAP force field, albeit in larger magnitudes. The pKa calculations also demonstrated that ff19sb with OPC water is significantly more accurate than ff14sb with TIP3P water, and the salt-bridge related pKa downshifts can be partially alleviated by the atom-pair specific Lennard-Jones corrections (NBFIX). Together, these data suggest that the accuracies of the protonation equilibria of proteins from constant pH simulations can significantly benefit from improvements of force fields.

Identifying the Most Probable Transition Path with Constant Advance Replicas.

Locating plausible transition paths and enhanced sampling of rare events are fundamental to understanding the functional dynamics of biomolecules. Here, a constraint-based constant advance replicas (CAR) formalism of reaction paths is reported for identifying the most probable transition path (MPTP) between two given states. We derive the temporal-integrated effective dynamics governing the projected subsystem under the holonomic CAR path constraints and show that a dynamical action functional can be defined and used for optimizing the MPTP. We further demonstrate how the CAR MPTP can be located by asymptotically minimizing an upper bound of the CAR action functional using a variational expectation-maximization framework. Essential thermodynamics and kinetic observables are retrieved by integrating the boxed molecular dynamics on the CAR MPTP using a newly proposed adaptive reflecting boundary condition. The efficiency of the proposed method is demonstrated for the Müller potential, the alanine dipeptide isomerization, and the DNA base pairing transition (Watson-Crick to Hoogsteen) in explicit solvent. The CAR representation of transition paths constitutes a robust and extensible platform that can be combined with diverse enhanced sampling methods to aid future flexible and reliable biomolecular simulations.

Crown Ether-Magnesium Ion Complexes: A Reliable Theoretical Estimation of Host-Guest Interaction and Binding Energies in a Solvent.

Metal ion detection is of a paramount importance for health monitoring. The host should properly accommodate the desired ion and be selective with respect to other potential guests, which makes devising ion sensors a demanding task. Recently (Phys. Chem. Chem. Phys. 2023, 25, 32656), we suggested a procedure for a computational design of crown ethers that can capture magnesium ions. In the present contribution we apply the same approach to search for Mg2+ trap with thiophene units, in accord with proposed hosts for Na+ and K+ ions (Adv. Funct. Mater., 2016, 26, 514). Additionally, we present a procedure based on the combination of Density Functional Theory based Molecular Dynamics and the Interacting Quantum Fragments methodology for determination of host-guest interaction and binding energies in a model with a large number of explicit solvent molecules. The presented strategy could be applied for identification of the right host for an arbitrary guest.

MARTINI Coarse-Grained Force Field for Thermoplastic Starch Nanocomposites.

Thermoplastic starch (TPS) is an excellent film-forming material, and the addition of fillers, such as tetramethylammonium-montmorillonite (TMA-MMT) clay, has significantly expanded its use in packaging applications. We first used an all-atom (AA) simulation to predict several macroscopic (Young's modulus, glass transition temperature, density) and microscopic (conformation along 1-4 and 1-6 glycosidic linkages, composite morphology) properties of TPS melt and TPS-TMA-MMT composite. The interplay of polymer-surface (weakly repulsive), plasticizer-surface (attractive), and polymer-plasticizer (weakly attractive) interactions leads to conformational and dynamics properties distinct from those in systems with either attractive or repulsive polymer-surface interactions. A subset of AA properties was used to parametrize the MARTINI-2 coarse-grained (CG) force field (FF) for the melt and composite systems. The missing bonded parameters of amylose and amylopectin and the bead types for 1-4 and 1-6 linked α-D glucose were determined using two-body excess entropy, density, and bond and angle distributions in the AA TPS melt. This new MARTINI-2 CG model was also compared with the MARTINI-3 model for the TPS melt. However, the requirement of a polarizable water model necessitates the use of MARTINI-2 FF for the composite system. This liquid-liquid partitioning-based FF shows freezing and compaction of polymer chains near the clay surface, further accentuated by lowering of dispersive interactions between pairs of high-covalent-coordination ring units of TPS polymers and the montmorillonite sheet. A rescaling of the effective dispersive component of TPS-MMT cross interactions was used to optimize the MARTINI-2 FF for the composite system with structural (chain size distribution), thermodynamic (chain conformational entropy and density), and dynamic (self-diffusion coefficient) properties obtained from long AA simulations forming the constraints for optimization. The obtained CG FF parameters provided excellent estimates for several other properties of the melt and composite systems not used in parameter estimation, thus establishing the robustness of the developed model.

PME Switching in Confinement Simulations of Charged Solutes.

The confinement method is a reaction coordinate-free enhanced sampling method for the calculation of conformational free energy differences. We show that in explicit solvent, artifacts occur when treating charged solutes. These artifacts are resolved by switching off the particle mesh Ewald (PME) method at the start of confinement. Calculations of the free energy cost of this switching converge rapidly, with small statistical error. The effectiveness and accuracy of confinement with PME switching is demonstrated by its application to a series of solutes of different charge; its ability to treat complex systems is illustrated by evaluating the free energy difference between B and Z-DNA.

Sensor Arrays for Electrochemical Detection of PCR-Amplified Genes Extracted from Cells Suspended in Environmental Waters

Ecological surveys of living things based on DNAs from environmental samples are attractive. However, despite simplicity of water sampling from the target environment, it is still necessary to transport the samples to the laboratory for DNA analysis based on skillful next-generation sequencers. To perform DNA-oriented surveys based on a simple protocol without any special training, we demonstrated, in this study, the detection of genes from cell-containing environmental waters using gene sensor arrays that require no DNA labeling and no external indicators. Cell-suspended PBS or river water were used as models of environmental waters containing living things, and DNA samples were prepared by PCR amplification. Ferrocene-terminated probes were synthesized and immobilized on an electrode array to develop a sensor array. The sensor array showed a large response to a target DNA complementary to the probe and no response to a mismatched DNA, indicating sequence-specific detection. For DNA samples prepared from the cells in PBS, they showed good responses similar to those for the target DNA. They also significantly detected DNA samples from the cells in river water at a general environmental concentration (38 cells mL−1) with 28-fold larger responses than those for 0 cells mL−1.

Sustainable Resource Management in an Agro-Industrial Cluster: A Case Study

Introduction: The challenge of water supply and sustainable energy is crucial for addressing long-term clean water and energy challenges. Moreover, there has been an increase in water and air pollution resulting from the community's activities. Objective: The objective of this study is to investigate conservation strategies must be enacted to reduce dependency on clean water and energy supplies. Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. Cost reductions, environmental sustainability, and an improved public image for understanding are context of the investigation. Method: The methodology adopted for this research comprises the case study uses a quantitative descriptive method to identify the estimated potential amount of biogas and water quality produced from tapioca and sago industrial wastewater. Data collection was carried out through the specific methods used, such as interviews, questionnaires, observations,and laboratory testing. Results and Discussion: The results obtained revealed energy generation from starch wastewater and recycled water from tapioca wastewater. The wastewater was treated in a biogas reactor as primary treatment to reduce the environmental load and produce biogas simultaneously. The wastewater recycling scheme for the tapioca industry, the quality and quantity of treated water recycled. Research Implications: Biogas production from wastewater reduces energy costs in starch industries. Recycling wastewater saves fresh water daily for starch production. Excess methane can be used for electricity generation or distributed to the community. Implementing anaerobic reactors minimizes environmental pollution. The system boosts sustainability, lowering reliance on external water and energy sources. Originality/Value: This study contributes to the literature by innovative integration of water recycling and biogas generation in starch industries. First application of CoLAR technology for energy self-sufficiency in tapioca and sago sectors. Demonstrates significant cost savings by reusing treated wastewater in production. Offers a scalable model for sustainable water and energy use in agro-industrial clusters. Contributes to reducing environmental impact through waste-to-energy conversion.

Reparameterization of the mW model to accurately predict the experimental phase diagram of methane hydrate.

Due to their high computational efficiency, the coarse-grained water models are of particular importance for practical molecular simulations of gas hydrates. In these models, the mW model is successfully used to study many thermodynamics and dynamics of methane hydrate. Yet, despite several decades of intense research, the mW model is still found to overestimate the melting temperature of methane hydrate. We here employ the minimum mean squared error estimation to revisit the key parameter of the mW model, which determines the strength of the tetrahedral angle of the water system. Relying on the free energy calculations, we first estimate the chemical potentials of water in the liquid phase for temperatures at which methane hydrate forms. We then turn to the mean squared error to describe the chemical potential deviation between the mW model and the TIP4P/ice model (the latter could reproduce the experimental phase diagram of methane hydrate). By minimizing the mean squared error, we finally have an optimized parameter for the mW model. In this part, we also discuss the pressure effect on such reparameterization procedure. Moreover, relying on the direct coexistence method, the melting temperature determined using the reparameterized mW model is found to be consistent with the experimental data. This strategy provides a means to improve the coarse-grained model to match the experimental observations for temperatures in the range of interest.

An all-atom model of the human cardiac sodium channel in a lipid bilayer

Voltage-gated sodium channels (NaV) are complex macromolecular proteins that are responsible for the initial upstroke of an action potential in excitable cells. Appropriate function is necessary for many physiological processes such as heartbeat, voluntary muscle contraction, nerve conduction, and neurological function. Dysfunction can have life-threatening consequences. During the past decade, there have been significant advancements with ion channel structural characterization by CryoEM, yet descriptions of cytosolic components are often lacking. Many investigations have biophysically characterized reconstituted cytosolic components and their interactions. However, extrapolating the structural alterations and allosteric communication within an intact ion channel can be challenging. To address this, we have developed an all-atom model of the human cardiac sodium channel (NaV1.5) in a lipid bilayer with explicit salt and water. Our simulations contain descriptions of cytosolic components that are poorly predicted by AlphaFold and lacking in many CryoEM structures. Leveraging the latest advancements of the Amber force fields (ff19sb and Lipid21) and water model (OPC), our simulations improved protein backbone torsion angles and generated structural information across time (four independent one-microsecond simulations). Our analysis provided descriptions of lipid and solvent contacts and insight into the C-Terminal Domain - inactivation gate and inactivation gate - latch receptor interactions.

Orientational Disorder of Alcohol Molecules at Their Solution Surfaces in Low Concentration Range: A Molecular Dynamics Simulation Study.

Molecular dynamics (MD) simulations of short-chain alcohols (methanol, ethanol, and 1-propanol) in solution were carried out to examine the orientational disordering (randomizing) of alcohol molecules at the surface under diluted conditions. Recent vibrational sum frequency generation (VSFG) spectroscopy, combined with photoelectron spectroscopy, has successfully measured the disordering structure at low concentrations. The present MD simulations accurately reproduce this observation for the first time. To ensure reliable results through MD simulations, several widely used force field models for water and alcohol, including polarizable models, were examined. This examination involved a comparison of structural and thermodynamic quantities, such as surface density times orientation and surface excess concentration, which were obtained through surface-specific measurements like VSFG spectroscopy and surface tension measurements. It is found that the width of the density profile for alcohol molecules at the surface, along the surface normal, increases as the concentration decreases in the diluted condition, which is consistent with the results obtained from the previous neutron and X-ray grazing incidence reflection experiments. A molecular mechanism explaining the disordering of alcohol molecules with decreasing concentration is also discussed.

Impact of water models on the structure and dynamics of enzyme tunnels

Protein hydration plays a vital role in many biological functions, and molecular dynamics simulations are frequently used to study it. However, the accuracy of these simulations is often sensitive to the water model used, a phenomenon particularly evident in intrinsically disordered proteins. Here, we investigated the extent to which the choice of water model alters the behavior of complex networks of tunnels within proteins. Tunnels are essential because they allow the exchange of substrates and products between buried enzyme active sites and the bulk solvent, directly affecting enzyme efficiency and selectivity, making the study of tunnels crucial for a holistic understanding of enzyme function at the molecular level. By performing simulations of haloalkane dehalogenase LinB and its two variants with engineered tunnels using TIP3P and OPC models, we investigated their effects on the overall tunnel topology. We also analyzed the properties of the primary tunnels, including their conformation, bottleneck dimensions, sampling efficiency, and the duration of tunnel openings. Our data demonstrate that all three proteins exhibited similar conformational behavior in both models but differed in the geometrical characteristics of their auxiliary tunnels, consistent with experimental observations. Interestingly, the results indicate that the stability of the open tunnels might be sensitive to the water model used. Because TIP3P can provide comparable data on the overall tunnel network, it is a valid choice when computational resources are limited or compatibility issues impede the use of OPC. However, OPC seems preferable for calculations requiring an accurate description of transport kinetics.