Prediction Of Interaction Energies Research Articles

Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 Mpro

Here, we introduce the use of ANI-ML potentials as a rescoring function in the host–guest interaction in molecular docking. Our results show that the “docking power” of ANI potentials can compete with the current scoring functions at the same level of computational cost. Benchmarking studies on CASF-2016 dataset showed that ANI is ranked in the top 5 scoring functions among the other 34 tested. In particular, the ANI predicted interaction energies when used in conjunction with GOLD-PLP scoring function can boost the top ranked solution to be the closest to the x-ray structure. Rapid and accurate calculation of interaction energies between ligand and protein also enables screening of millions of drug candidates/docking poses. Using a unique protocol in which docking by GOLD-PLP, rescoring by ANI-ML potentials and extensive MD simulations along with end state free energy methods are combined, we have screened FDA approved drugs against the SARS-CoV-2 main protease (Mpro). The top six drug molecules suggested by the consensus of these free energy methods have already been in clinical trials or proposed as potential drug molecules in previous theoretical and experimental studies, approving the validity and the power of accuracy in our screening method.

Empirical Model of Solvophobic Interactions in Organic Solvents

AbstractAn empirical model was developed to predict organic solvophobic effects using N‐phenylimide molecular balances functionalized with non‐polar alkyl groups. Solution studies and X‐ray crystallography confirmed intramolecular alkyl‐alkyl interactions in their folded conformers. The structural modularity of the balances allowed systematic variation of alkyl group lengths. Control balances were instrumental in isolating weak organic solvophobic effects by eliminating framework solvent‐solute effects. A 19F NMR label enabled analysis across 46 deuterated and non‐deuterated solvent systems. Linear correlations were observed between organic solvophobic effects and solvent cohesive energy density (ced) as well as changes in solvent‐accessible surface areas (SASA). Using these empirical relationships, a model was constructed to predict organic solvophobic interaction energy per unit area for any organic solvent with known ced values. The predicted interaction energies aligned with recent organic solvophobic measurements and literature values for the hydrophobic effect on non‐polar surfaces confirmed the model‘s accuracy and utility.

Empirical Model of Solvophobic Interactions in Organic Solvents.

An empirical model was developed to predict organic solvophobic effects using N-phenylimide molecular balances functionalized with non-polar alkyl groups. Solution studies and X-ray crystallography confirmed intramolecular alkyl-alkyl interactions in their folded conformers. The structural modularity of the balances allowed systematic variation of alkyl group lengths. Control balances were instrumental in isolating weak organic solvophobic effects by eliminating framework solvent-solute effects. A 19 F NMR label enabled analysis across 46 deuterated and non-deuterated solvent systems. Linear correlations were observed between organic solvophobic effects and solvent cohesive energy density (ced) as well as changes in solvent-accessible surface areas (SASA). Using these empirical relationships, a model was constructed to predict organic solvophobic interaction energy per unit area for any organic solvent with known ced values. The predicted interaction energies aligned with recent organic solvophobic measurements and literature values for the hydrophobic effect on non-polar surfaces confirmed the model's accuracy and utility.

Simulation and Experimental Analysis of Microalgae and Membrane Surface Interaction

The microalgae-induced membrane system applied in wastewater treatment has attracted attention due to microalgae’s outstanding nutrient fixation capacity and biomass harvesting. However, the fundamental understanding of the interaction of microalgae and membrane surfaces is still limited. This study presents experimental and numerical methods to analyze the attachment of microalgae to the membrane. An atomic force microscope (AFM) analysis confirmed that a polydimethylsiloxane (PDMS) sensor, as a simulated membrane surface, exhibited a rougher surface morphology than a polyurethane (PU) sensor did. The contact angle and adsorption analysis using a quartz crystal microbalance confirmed that the PDMS surface, representing the membrane surface, provided a better attachment affinity than the PU surface for microalgae because of the lower surface tension and stronger hydrophobicity of PDMS. The simulation studies of this work involved the construction of roughly circular-shaped particles to represent microalgae, rough flat surfaces to represent membrane surfaces, and the interaction energy between particles and surfaces based on XDLVO theory. The modeling results of the microalgae adsorption trend are consistent and verified with the experimental results. It was observed that the interfacial energy increased with increasing the size of particles and asperity width of the membrane surface. Contrarily, the predicted interaction energy dropped with elevating the number of asperities and asperity height of the microalgae and membrane. The most influential parameter for controlling interfacial interaction between the simulated microalgae and membrane surface was the asperity height of the membrane; changing the height from 50 nm to 250 nm led to alteration in the primary minimum from −18 kT to −3 kT. Overall, this study predicted that the microalgae attachment depends on the size of the asperities to a great extent and on the number of asperities to a lesser extent. These results provide an insight into the interaction of microalgae and membrane surface, which would provide information on how the performance of microalgae-based membrane systems can be improved.

Interpretable Machine Learning Model for Predicting Interaction Energies between Dimethyl Sulfide and Potential Absorbing Solvents

Non-bonding intermolecular interactions largely dominate the selective dissolution of trace species into physical solvents and, therefore, are fundamentally important to solvent development for the capture of environment-undesired compounds or purification of chemicals. However, acquirement of the interaction energy requires costly quantum chemical computation and still encounters a practical challenge to build a chemically interpretable machine learning (ML) prediction model using documented molecular descriptors. Herein, we report an ML model for predicting the interaction energies (Eint) between dimethyl sulfide and potential absorbing solvents. Applying the reduced density gradient and quantum theory of atoms in molecules analyses, the non-bonding intermolecular interactions of dimethyl sulfide with solvent compounds were elucidated through focusing on the molecular fragments containing the main center (MC) and secondary center (SC) rather than the whole molecule. The training data set was obtained using a molecular generation strategy, and 21 molecular descriptors were defined to describe the electronic states of the central atoms in each solvent molecule and its nearby hydrogen-bond donors. Model analysis reveals that Eint is mainly determined by the charge state of the crucial fragments and the hydrogen-bond donors of the solvent molecule. The custom-defined descriptors not only improve the regression and prediction performance but also enable the interpretability of the ML model. Additionally, the absorption equilibrium measurements of solubilities of dimethyl sulfide in several commercial solvents verified the strong correlation between the dissolving affinity and solute–solvent intermolecular interaction energy. The present study provides an approach to building practical and interpretable intelligent algorithms to aid the development of sustainable chemicals or processes.

Accurate non-covalent interaction energies on noisy intermediate-scale quantum computers via second-order symmetry-adapted perturbation theory.

The calculation of non-covalent interaction energies on noisy intermediate-scale quantum (NISQ) computers appears to be challenging with straightforward application of existing quantum algorithms. For example, the use of the standard supermolecular method with the variational quantum eigensolver (VQE) would require extremely precise resolution of the total energies of the fragments to provide for accurate subtraction to the interaction energy. Here we present a symmetry-adapted perturbation theory (SAPT) method that may provide interaction energies with high quantum resource efficiency. Of particular note, we present a quantum extended random-phase approximation (ERPA) treatment of the SAPT second-order induction and dispersion terms, including exchange counterparts. Together with previous work on first-order terms (Chem. Sci., 2022, 13, 3094), this provides a recipe for complete SAPT(VQE) interaction energies up to second order, which is a well established truncation. The SAPT interaction energy terms are computed as first-level observables with no subtraction of monomer energies invoked, and the only quantum observations needed are the VQE one- and two-particle density matrices. We find empirically that SAPT(VQE) can provide accurate interaction energies even with coarsely optimized, low circuit depth wavefunctions from a quantum computer, simulated through ideal statevectors. The errors of the total interaction energy are orders of magnitude lower than the corresponding VQE total energy errors of the monomer wavefunctions. In addition, we present heme-nitrosyl model complexes as a system class for near term quantum computing simulations. They are strongly correlated, biologically relevant and difficult to simulate with classical quantum chemical methods. This is illustrated with density functional theory (DFT) as the predicted interaction energies exhibit a strong sensitivity with respect to the choice of functional. Thus, this work paves the way to obtain accurate interaction energies on a NISQ-era quantum computer with few quantum resources. It is the first step in alleviating one of the major challenges in quantum chemistry, where in-depth knowledge of both the method and system is required a priori to reliably generate accurate interaction energies.

Interaction energy of Cl2 and Br2 with H2 O: Exchange, dispersion and density the crucial ingredients.

Modern Density Functional Theory models are now suitable for many molecular and condensed phase studies. The study of noncovalent interactions, a well-known drawback, is no longer an insurmountable obstacle through design and empirical corrections. However, using empirical corrections as in the DFT-D methods might not be an all-in-one solution. This work uses a simple system, X2 -H2 O with X=Cl or Br, with two different interactions, halogen-bonded (XB) and hydrogen-halogen (HX), to investigate the capability of current density functional approximations (DFA) in predicting interaction energies with eight different exchange-correlation functionals. SAPT(DFT) provides, for all the studied cases, better predictions than the widely used supermolecular approach. In addition, the components of the interaction energy suggest where some of the shortcomings originate in each DFA. The analysis of the functionals used confirms that PBE0 and ω-B97X-D have a physically correct behavior. Using SAPT(DFT) and PBE0, and ω-B97X-D, we obtained the interaction energy of Cl2 and Br2 inside different clathrate cages and satisfactorily compared with wavefunction results; hence, the lower and upper limits of this value are defined: Cl2 @512 , -5.3 ± 0.3kcal/mol; Cl2 @512 62 , -5.5 ± 0.1kcal/mol; Br2 @512 62 , -7.6 ± 1.0kcal/mol; Br2 @512 63 , -10.6 ± 1.0kcal/mol; Br2 @512 64 , -10.9 ± 0.8kcal/mol.

Accurate Modeling of Bromide and Iodide Hydration with Data-Driven Many-Body Potentials.

Ion-water interactions play a central role in determining the properties of aqueous systems in a wide range of environments. However, a quantitative understanding of how the hydration properties of ions evolve from small aqueous clusters to bulk solutions and interfaces remains elusive. Here, we introduce the second generation of data-driven many-body energy (MB-nrg) potential energy functions (PEFs) representing bromide-water and iodide-water interactions. The MB-nrg PEFs use permutationally invariant polynomials to reproduce two-body and three-body energies calculated at the coupled cluster level of theory, and implicitly represent all higher-body energies using classical many-body polarization. A systematic analysis of the hydration structure of small Br-(H2O)n and I-(H2O)n clusters demonstrates that the MB-nrg PEFs predict interaction energies in quantitative agreement with "gold standard" coupled cluster reference values. Importantly, when used in molecular dynamics simulations carried out in the isothermal-isobaric ensemble for single bromide and iodide ions in liquid water, the MB-nrg PEFs predict extended X-ray absorption fine structure (EXAFS) spectra that accurately reproduce the experimental spectra, which thus allows for characterizing the hydration structure of the two ions with a high level of confidence.

Compositional complementarity between genomic RNA and coat proteins in positive-sense single-stranded RNA viruses.

During packaging in positive-sense single-stranded RNA (+ssRNA) viruses, coat proteins (CPs) interact directly with multiple regions in genomic RNA (gRNA), but the underlying physicochemical principles remain unclear. Here we analyze the high-resolution cryo-EM structure of bacteriophage MS2 and show that the gRNA/CP binding sites, including the known packaging signal, overlap significantly with regions where gRNA nucleobase-density profiles match the corresponding CP nucleobase-affinity profiles. Moreover, we show that the MS2 packaging signal corresponds to the global minimum in gRNA/CP interaction energy in the unstructured state as derived using a linearly additive model and knowledge-based nucleobase/amino-acid affinities. Motivated by this, we predict gRNA/CP interaction sites for a comprehensive set of 1082 +ssRNA viruses. We validate our predictions by comparing them with site-resolved information on gRNA/CP interactions derived in SELEX and CLIP experiments for 10 different viruses. Finally, we show that in experimentally studied systems CPs frequently interact with autologous coding regions in gRNA, in agreement with both predicted interaction energies and a recent proposal that proteins in general tend to interact with own mRNAs, if unstructured. Our results define a self-consistent framework for understanding packaging in +ssRNA viruses and implicate interactions between unstructured gRNA and CPs in the process.

Interaction Analysis of the Spike Protein of Delta and Omicron Variants of SARS-CoV-2 with hACE2 and Eight Monoclonal Antibodies Using the Fragment Molecular Orbital Method.

In the past 2 years, since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), multiple SARS-CoV-2 variants have emerged. Whenever a new variant emerges, considerable time is required to analyze the binding affinity of the viral surface proteins to human angiotensin-converting enzyme 2 (hACE2) and monoclonal antibodies. To efficiently predict the binding affinities associated with hACE2 and monoclonal antibodies in a short time, herein, we propose a method applying statistical analysis to simulations performed using molecular and quantum mechanics. This method efficiently predicted the trend of binding affinity for the binding of the spike protein of each variant of SARS-CoV-2 to hACE2 and individually to eight commercial monoclonal antibodies. Additionally, this method accurately predicted interaction energy changes in the crystal structure for 10 of 13 mutated residues in the Omicron variant, showing a significant change in the interaction energy of hACE2. S375F was found to be a mutation that majorly changed the binding affinity of the spike protein to hACE2 and the eight monoclonal antibodies. Our proposed analysis method enables the prediction of the binding affinity of new variants to hACE2 or to monoclonal antibodies in a shorter time compared to that utilized by the experimental method.

Exploring artificial neural networks to model interatomic and intermolecular potential energy surfaces

We demonstrate how a back-propagation artificial neural network can be trained to represent a potential energy surface (PES) in a formless manner with limited data points and exploited to predict interaction energies for configurations not included in the training set. A similar exercise is undertaken for predicting the eigenvalues and eigenvectors of a model Hamiltonian matrix that delicately depends on parameters and exhibits crossing of eigen values.

Exploring and enhancing the accuracy of interior-scaled Perdew-Zunger self-interaction correction.

The Perdew-Zunger self-interaction correction (PZ-SIC) improves the performance of density functional approximations for the properties that involve significant self-interaction error (SIE), as in stretched bond situations, but overcorrects for equilibrium properties where SIE is insignificant. This overcorrection is often reduced by local scaling self-interaction correction (LSIC) of the PZ-SIC to the local spin density approximation (LSDA). Here, we propose a new scaling factor to use in an LSIC-like approach that satisfies an additional important constraint: the correct coefficient of the atomic number Z in the asymptotic expansion of the exchange-correlation (xc) energy for atoms. LSIC and LSIC+ are scaled by functions of the iso-orbital indicator zσ, which distinguishes one-electron regions from many-electron regions. LSIC+ applied to the LSDA works better for many equilibrium properties than LSDA-LSIC and the Perdew, Burke, and Ernzerhof generalized gradient approximation (GGA), and almost close to the strongly constrained and appropriately normed (SCAN) meta-GGA. LSDA-LSIC and LSDA-LSIC+, however, fail to predict interaction energies involving weaker bonds, in sharp contrast to their earlier successes. It is found that more than one set of localized SIC orbitals can yield a nearly degenerate energetic description of the same multiple covalent bond, suggesting that a consistent chemical interpretation of the localized orbitals requires a new way to choose their Fermi orbital descriptors. To make a locally scaled down SIC to functionals beyond the LSDA requires a gauge transformation of the functional's energy density. The resulting SCAN-sdSIC, evaluated on SCAN-SIC total and localized orbital densities, leads to an acceptable description of many equilibrium properties including the dissociation energies of weak bonds.

Sustainable management of cut flowers waste by activation and its application in wastewater treatment technology.

Cut flowers and floral waste (CFW) is a major concern in the present era. This study emphasizes the reuse of cut flowers as a neat bioadsorbent and activated carbon (AC). The biomass from the cut flowers was utilized as a neat bioadsorbent for the removal of the antibiotic levofloxacin and lead ions from water. The results revealed that the neat bioadsorbent was able to remove 60% levofloxacin and 45% of lead ions. Upon chemical activation of the CFW (using KOH), almost all levofloxacin and more than 99% of lead ions were removed. Physical activation (using CO2) gave a material that removed 90% of levofloxacin and 85% of lead ions. The adsorption of levofloxacin and lead ions on all the adsorbents followed Langmuir isotherms and pseudo-second-order kinetic model. The results have been further explained via the prediction of interaction energies between the adsorbates and adsorbents using COSMO-RS simulation.

Cationic dyes adsorption by Na-Montmorillonite Nano Clay: Experimental study combined with a theoretical investigation using DFT-based descriptors and molecular dynamics simulations

In this work, adsorption kinetics, isotherms and mechanisms of two cationic dyes, Toluidine Blue (TB) and Crystal Violet (CV), on a Na-Montmorillonite Nano Clay (Na-MNC) have been experimentally and theoretically determined. The structure and the morphology of the Na-MNC were characterized by XRD, FTIR, SEM, EDAX and BET. High adsorption capacity was observed for both dyes (5.80 mmol/g for TB and 5.40 mmol/g for CV). Maximum removal of both dyes was observed at pH 11.0. Equilibrium was attained after the contact time of 30 min for CV dye and 50 min for TB dye. The adsorption isotherms were described by Langmuir isotherm model suggesting that the dyes were adsorbed homogenously over a monolayer surface of the Na-MNC. The adsorption kinetics was well fitted to pseudo-second-order kinetic model (R2 > 0.98). The theoretical investigation carried out on the CV and TB dyes structures using the quantum molecular descriptors derived from density functional theory (DFT) calculations have proved that the TB cations are more electrophilic, reactive and have the ability to accept electrons allow to adsorb strongly on the adsorbent surface compared to the CV ones. Molecular dynamic (MD) simulations were performed to obtain information on molecular arrangements and to predict interaction energies of studied dyes with the Na-MNC surface. These simulations show that the adsorption of the studied dyes can result in a variety of configurations, including single layers parallel to the basal surface, tilted and random piles.

Statistical mechanical properties of sequence space determine the efficiency of the various algorithms to predict interaction energies and native contacts from protein coevolution

Studying evolutionary correlations in alignments of homologous sequences by means of an inverse Potts model has proven useful to obtain residue-residue contact energies and to predict contacts in proteins. The quality of the results depend much on several choices of the detailed model and on the algorithms used. We built, in a very controlled way, synthetic alignments with statistical properties similar to those of real proteins, and used them to assess the performance of different inversion algorithms and of their variants. Realistic synthetic alignments display typical features of low-temperature phases of disordered systems, a feature that affects the inversion algorithms. We showed that a Boltzmann-learning algorithm is computationally feasible and performs well in predicting the energy of native contacts. However, all algorithms, when applied to alignments of realistic size, suffer of false positives quite equally, making the quality of the prediction of native contacts with the different algorithm much system-dependent.

Assessment of Density Functional Theory in Predicting Interaction Energies between Water and Polycyclic Aromatic Hydrocarbons: from Water on Benzene to Water on Graphene.

The interactions of water with polycyclic aromatic hydrocarbons, from benzene to graphene, are investigated using various exchange-correlation functionals selected across the hierarchy of density functional theory (DFT) approximations. The accuracy of the different functionals is assessed through comparisons with random phase approximation (RPA) and coupled-cluster with single, double, and perturbative triple excitations [CCSD(T)] calculations. Diffusion Monte Carlo (DMC) data reported in the literature are also used for comparison. Relatively large variations are found in interaction energies predicted by different DFT models, with GGA functionals underestimating the interaction strength for configurations with the water oxygen pointing toward the aromatic molecules. The meta-GGA B97M-rV and range-separated hybrid, meta-GGA ωB97M-V functionals provide nearly quantitative agreement with CCSD(T) values for the water-benzene, water-coronene, and water-circumcoronene dimers, while RPA and DMC predict interaction energies that differ by up to ∼1 kcal/mol and ∼0.4 kcal/mol from the corresponding CCSD(T) values, respectively. Similar trends among GGA, meta-GGA, and hybrid functionals are observed for larger polycyclic aromatic hydrocarbons. By performing absolutely localized molecular orbital energy decomposition analyses (ALMO-EDA), it is found that, independently of the number of carbon atoms and exchange-correlation functional, the dominant contributions to the interaction energies between water and polycyclic aromatic hydrocarbon molecules are the electrostatic and dispersion terms while polarization and charge transfer effects are negligibly small. Calculations carried out with GGA and meta-GGA functionals indicate that, as the number of carbon atoms increases, the interaction energies slowly converge to the corresponding values obtained for an infinite graphene sheet.

Portraying the selectivity of GSK-3 inhibitors towards CDK-2 by 3D similarity and molecular docking

The striking structural resemblance between adenosine triphosphate (ATP) binding sites of glycogen synthase kinase-3 (GSK-3) and cyclin-dependent kinase-2 (CDK-2) raises numerous off-target selectivity problems in lead-identification processes that may jeopardize their progress into safe and effective drugs. The structural disparities between GSK-3 and CDK-2 in terms of inhibitors chemical space and binding site characteristics were investigated computationally by ligand-based (3D-similarity search) and structure-based (molecular docking) methods to reproduce the selectivity trend of indirubin derivatives. We attempted to assess distinctive key selectivity features of GSK-3 over CDK-2 with focus on indirubins and to provide a cascade virtual screening approach capable to identify suitable de novo GSK-3 selective scaffolds. Seven inhibitors with higher predicted interaction energies against GSK-3 compared to the highly active reference inhibitor were proposed. Concerted effects between 3D similarity search and docking afforded an exhaustive characterization of the binding site interactions. In spite of inherent challenges and limitations, the workflow developed hereby can be applied to other GSK-3 inhibitors, which display similar inhibitory profile against CDK-2, to rationally design potentially selective scaffolds.

Mechanisms of graphene oxide aggregation, retention, and release in quartz sand

The roles of graphene oxide (GO) particle geometry, GO surface orientation, surface roughness, and nanoscale chemical heterogeneity on interaction energies, aggregation, retention, and release of GO in porous media were not fully considered in previous studies. Consequently, mechanisms controlling the environmental fate of GO were incompletely or inaccurately quantified. To overcome this limitation, plate-plate interaction energies were modified to account for these factors and used in conjunction with a mathematical model to interpret the results of GO aggregation, retention, and release studies. Calculations revealed that these factors had a large influence on the predicted interaction energy parameters. Similar to previous literature, the secondary minimum was predicted to dominate on smooth, chemically homogeneous surfaces that were oriented parallel to each other, especially at higher ionic strength (IS). Conversely, shallow primary minimum interactions were sometimes predicted to occur on surfaces with nanoscale roughness and chemical heterogeneity due to adsorbed Ca2+ ions, especially when the GO particles were oriented perpendicular to the interacting surface. Experimental results were generally consistent with these predictions and indicated that the primary minimum played a major role in GO retention and the secondary minimum contributed to GO release with IS reduction. Cation exchange (Na+ replacing Ca2+) enhanced GO release with IS reduction when particles were initially deposited in the presence of Ca2+ ions. However, retained GO were always completely recovered into the excess deionized water when the sand pore structure was destroyed during excavation, and this indicates that primary minima were shallow and that the pore structure also played an important role in GO retention. Further evidence for the role of pore structure on GO retention was obtained by conducting experiments in finer textured sand and at higher input concentrations that induced greater aggregation. In both cases, greater GO retention occurred, and retention profiles became more hyperexponential in shape.

New Angles on Standard Force Fields: Toward a General Approach for Treating Atomic-Level Anisotropy.

Nearly all standard force fields employ the "sum-of-spheres" approximation, which models intermolecular interactions purely in terms of interatomic distances. Nonetheless, atoms in molecules can have significantly nonspherical shapes, leading to interatomic interaction energies with strong orientation dependencies. Neglecting this "atomic-level anisotropy" can lead to significant errors in predicting interaction energies. Herein, we propose a simple, transferable, and computationally efficient model (MASTIFF) whereby atomic-level orientation dependence can be incorporated into ab initio intermolecular force fields. MASTIFF includes anisotropic exchange-repulsion, charge penetration, and dispersion effects, in conjunction with a standard treatment of anisotropic long-range (multipolar) electrostatics. To validate our approach, we benchmark MASTIFF against various sum-of-spheres models over a large library of intermolecular interactions between small organic molecules. MASTIFF achieves quantitative accuracy, with respect to both high-level electronic structure theory and experiment, thus showing promise as a basis for "next-generation" force field development.

Contributions of Nanoscale Roughness to Anomalous Colloid Retention and Stability Behavior.

All natural surfaces exhibit nanoscale roughness (NR) and chemical heterogeneity (CH) to some extent. Expressions were developed to determine the mean interaction energy between a colloid and a solid-water interface, as well as for colloid-colloid interactions, when both surfaces contain binary NR and CH. The influence of heterogeneity type, roughness parameters, solution ionic strength (IS), mean zeta potential, and colloid size on predicted interaction energy profiles was then investigated. The role of CH was enhanced on smooth surfaces with larger amounts of CH, especially for smaller colloids and higher IS. However, predicted interaction energy profiles were mainly dominated by NR, which tended to lower the energy barrier height and the magnitudes of both the secondary and primary minima, especially when the roughness fraction was small. This dramatically increased the relative importance of primary to secondary minima interactions on net electrostatically unfavorable surfaces, especially when roughness occurred on both surfaces and for conditions that produced small energy barriers (e.g., higher IS, lower pH, lower magnitudes in the zeta potential, and for smaller colloid sizes) on smooth surfaces. The combined influence of roughness and Born repulsion frequently produced a shallow primary minimum that was susceptible to diffusive removal by random variations in kinetic energy, even under electrostatically favorable conditions. Calculations using measured zeta potentials and hypothetical roughness properties demonstrated that roughness provided a viable alternative explanation for many experimental deviations that have previously been attributed to electrosteric repulsion (e.g., a decrease in colloid retention with an increase in solution IS; reversible colloid retention under favorable conditions; and diminished colloid retention and enhanced colloid stability due to adsorbed surfactants, polymers, and/or humic materials).