The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations.

Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

Antitumor Activity of 1,18-Octadecanedioic Acid-Paclitaxel Complexed with Human Serum Albumin.

We describe the design, synthesis, and antitumor activity of an 18 carbon α,ω-dicarboxylic acid monoconjugated via an ester linkage to paclitaxel (PTX). This 1,18-octadecanedioic acid-PTX (ODDA-PTX) prodrug readily forms a noncovalent complex with human serum albumin (HSA). Preservation of the terminal carboxylic acid moiety on ODDA-PTX enables binding to HSA in the same manner as native long-chain fatty acids (LCFAs), within hydrophobic pockets, maintaining favorable electrostatic contacts between the ω-carboxylate of ODDA-PTX and positively charged amino acid residues of the protein. This carrier strategy for small molecule drugs is based on naturally evolved interactions between LCFAs and HSA, demonstrated here for PTX. ODDA-PTX shows differentiated pharmacokinetics, higher maximum tolerated doses and increased efficacy in vivo in multiple subcutaneous murine xenograft models of human cancer, as compared to two FDA-approved clinical formulations, Cremophor EL-formulated paclitaxel (crPTX) and Abraxane (nanoparticle albumin-bound (nab)-paclitaxel).

Accounting for apparent deviations between calorimetric and van't Hoff enthalpies.

BACKGROUND: In theory, binding enthalpies directly obtained from calorimetry (such as ITC) and the temperature dependence of the binding free energy (van't Hoff method) should agree. However, previous studies have often found them to be discrepant. METHODS: Experimental binding enthalpies (both calorimetric and van't Hoff) are obtained for two host-guest pairs using ITC, and the discrepancy between the two enthalpies is examined. Modeling of artificial ITC data is also used to examine how different sources of error propagate to both types of binding enthalpies. RESULTS: For the host-guest pairs examined here, good agreement, to within about 0.4kcal/mol, is obtained between the two enthalpies. Additionally, using artificial data, we find that different sources of error propagate to either enthalpy uniquely, with concentration error and heat error propagating primarily to calorimetric and van't Hoff enthalpies, respectively. CONCLUSIONS: With modern calorimeters, good agreement between van't Hoff and calorimetric enthalpies should be achievable, barring issues due to non-ideality or unanticipated measurement pathologies. Indeed, disagreement between the two can serve as a flag for error-prone datasets. A review of the underlying theory supports the expectation that these two quantities should be in agreement. GENERAL SIGNIFICANCE: We address and arguably resolve long-standing questions regarding the relationship between calorimetric and van't Hoff enthalpies. In addition, we show that comparison of these two quantities can be used as an internal consistency check of a calorimetry study.

Highly sampled tetranucleotide and tetraloop motifs enable evaluation of common RNA force fields.

Recent modifications and improvements to standard nucleic acid force fields have attempted to fix problems and issues that have been observed as longer timescale simulations have become routine. Although previous work has shown the ability to fold the UUCG stem-loop structure, until now no group has attempted to quantify the performance of current force fields using highly converged structural populations of the tetraloop conformational ensemble. In this study, we report the use of multiple independent sets of multidimensional replica exchange molecular dynamics (M-REMD) simulations with different initial conditions to generate well-converged conformational ensembles for the tetranucleotides r(GACC) and r(CCCC), as well as the larger UUCG tetraloop motif. By generating what is to our knowledge the most complete RNA structure ensembles reported to date for these systems, we remove the coupling between force field errors and errors due to incomplete sampling, providing a comprehensive comparison between current top-performing MD force fields for RNA. Of the RNA force fields tested in this study, none demonstrate the ability to correctly identify the most thermodynamically stable structure for all three systems. We discuss the deficiencies present in each potential function and suggest areas where improvements can be made. The results imply that although "short" (nsec-µsec timescale) simulations may stay close to their respective experimental structures and may well reproduce experimental observables, inevitably the current force fields will populate alternative incorrect structures that are more stable than those observed via experiment.

Probing the orientation of inhibitor and epoxy-eicosatrienoic acid binding in the active site of soluble epoxide hydrolase.

Soluble epoxide hydrolase (sEH) is an important therapeutic target of many diseases, such as chronic obstructive pulmonary disease (COPD) and diabetic neuropathic pain. It acts by hydrolyzing and thus regulating specific bioactive long chain polyunsaturated fatty acid epoxides (lcPUFA), like epoxyeicosatrienoic acids (EETs). To better predict which epoxides could be hydrolyzed by sEH, one needs to dissect the important factors and structural requirements that govern the binding of the substrates to sEH. This knowledge allows further exploration of the physiological role played by sEH. Unfortunately, a crystal structure of sEH with a substrate bound has not yet been reported. In this report, new photoaffinity mimics of a sEH inhibitor and EET regioisomers were prepared and used in combination with peptide sequencing and computational modeling, to identify the binding orientation of different regioisomers and enantiomers of EETs into the catalytic cavity of sEH. Results indicate that the stereochemistry of the epoxide plays a crucial role in dictating the binding orientation of the substrate.

Evaluation and Minimization of Uncertainty in ITC Binding Measurements: Heat Error, Concentration Error, Saturation, and Stoichiometry.

BACKGROUND: Isothermal titration calorimetry (ITC) is uniquely useful for characterizing binding thermodynamics, because it straightforwardly provides both the binding enthalpy and free energy. However, the precision of the results depends on the experimental setup and how thermodynamic results are obtained from the raw data. METHODS: Experiments and Monte Carlo analysis are used to study how uncertainties in injection heat and concentration propagate to binding enthalpies in various scenarios. We identify regimes in which it is preferable to fix the stoichiometry parameter, N, and evaluate the reliability of uncertainties provided by the least squares method. RESULTS: The noise in the injection heat is mainly proportional in character, with ~1% and ~3% uncertainty at 27C and 65C, respectively; concentration errors are ~1%. Simulations of experiments based on these uncertainties delineate how experimental design and curve fitting methods influence the uncertainty in the final results. CONCLUSIONS: In most cases, experimental uncertainty is minimized by using more injections and by fixing N at its known value. With appropriate technique, the uncertainty in measured binding enthalpies can be kept below ~2% under many conditions, including low C values. GENERAL SIGNIFICANCE: We quantify uncertainties in ITC data due to heat and concentration error, and identify practices to minimize these uncertainties. The resulting guidelines are important when ITC data are used quantitatively, such as to test computer simulations of binding. Reproducibility and further study are supported by free distribution of the new software developed here.

The SAMPL5 host-guest challenge: computing binding free energies and enthalpies from explicit solvent simulations by the attach-pull-release (APR) method.

The absolute binding free energies and binding enthalpies of twelve host-guest systems in the SAMPL5 blind challenge were computed using our attach-pull-release (APR) approach. This method has previously shown good correlations between experimental and calculated binding data in retrospective studies of cucurbit[7]uril (CB7) and ß-cyclodextrin (ßCD) systems. In the present work, the computed binding free energies for host octa acid (OA or OAH) and tetra-endo-methyl octa-acid (TEMOA or OAMe) with guests are in good agreement with prospective experimental data, with a coefficient of determination (R2) of 0.8 and root-mean-squared error of 1.7 kcal/mol using the TIP3P water model. The binding enthalpy calculations achieve moderate correlations, with R2 of 0.5 and RMSE of 2.5 kcal/mol, for TIP3P water. Calculations using the newly developed OPC water model also show good performance. Furthermore, the present calculations semi-quantitatively capture the experimental trend of enthalpy-entropy compensation observed, and successfully predict guests with the strongest and weakest binding affinity. The most populated binding poses of all twelve systems, based on clustering analysis of 750 ns molecular dynamics (MD) trajectories, were extracted and analyzed. Computational methods using MD simulations and explicit solvent models in a rigorous statistical thermodynamic framework, like APR, can generate reasonable predictions of binding thermodynamics. Especially with continuing improvement in simulation force fields, such methods hold the promise of making substantial contributions to hit identification and lead optimization in the drug discovery process.

Overview of the SAMPL5 host-guest challenge: Are we doing better?

The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein-ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host-guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host-guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host-guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements.

Structural and energetic analysis of 2-aminobenzimidazole inhibitors in complex with the hepatitis C virus IRES RNA using molecular dynamics simulations.

Despite the many biological functions of RNA, very few drugs have been designed or found to target RNA. Here we report the results of molecular dynamics (MD) simulations and binding energy analyses on hepatitis C virus internal ribosome entry site (IRES) RNA in complex with highly charged 2-aminobenzimidazole inhibitors. Initial coordinates were taken from NMR and crystallography studies that had yielded different binding modes. During MD simulations, the RNA-inhibitor complex is stable in the crystal conformation but not in the NMR conformation. Additionally, we found that existing and standard MD trajectory postprocessing free energy methods, such as the MM-GBSA and MM-PBSA approaches available in AMBER, seem unsuitable to properly rank the binding energies of complexes between highly charged molecules. A better correlation with the experimental data was found using a rather simple binding enthalpy calculation based on the explicitly solvated potential energies. In anticipation of further growth in the use of small molecules to target RNA, we include results addressing the impact of charge assignment on docking, the structural role of magnesium in the IRES-inhibitor complex, the entropic contribution to binding energy, and simulations of a plausible scaffold design for new inhibitors.

Synthesis and Evaluation of 1,3-Disubstituted Imidazolidine-2,4,5-triones as Inhibitors of Pyruvate Carboxylase.

Substituted imidazolidinetriones (IZTs) have been identified as potent inhibitors of pyruvate carboxylase (PC) through an in silico screening approach. Alkyl 2-(2,4,5-trioxo-3-substituted imidazolidin-1-yl)acetates (6i-6r) are the most potent of the series, with IC50 values between 3 and 12 µM, and several IZTs demonstrate high passive permeability across an artificial membrane. IZTs are mixed-type inhibitors with respect to pyruvate and noncompetitive with respect to ATP. This class of inhibitors appears to be selective for PC. Inhibitors in the IZT series do not inhibit the metalloenzymes human carbonic anhydrase II and matrix metalloprotease-12, and they do not inhibit the related biotin-dependent enzyme, guanidine carboxylase. Altogether, IZTs offer promise as PC inhibitors with potential downstream applications in cellular and in vivo systems.

AI-Based Discovery and CryoEM Structural Elucidation of a KATP Channel Pharmacochaperone.

Pancreatic KATP channel trafficking defects underlie congenital hyperinsulinism (CHI) cases unresponsive to the KATP channel opener diazoxide, the mainstay medical therapy for CHI. Current clinically used KATP channel inhibitors have been shown to act as pharmacochaperones and restore surface expression of trafficking mutants; however, their therapeutic utility for KATP trafficking impaired CHI is hindered by high-affinity binding, which limits functional recovery of rescued channels. Recent structural studies of KATP channels employing cryo-electron microscopy (cryoEM) have revealed a promiscuous pocket where several known KATP pharmacochaperones bind. The structural knowledge provides a framework for discovering KATP channel pharmacochaperones with desired reversible inhibitory effects to permit functional recovery of rescued channels. Using an AI-based virtual screening technology AtomNet® followed by functional validation, we identified a novel compound, termed Aekatperone, which exhibits chaperoning effects on KATP channel trafficking mutations. Aekatperone reversibly inhibits KATP channel activity with a half-maximal inhibitory concentration (IC50) ~ 9 µM. Mutant channels rescued to the cell surface by Aekatperone showed functional recovery upon washout of the compound. CryoEM structure of KATP bound to Aekatperone revealed distinct binding features compared to known high affinity inhibitor pharmacochaperones. Our findings unveil a KATP pharmacochaperone enabling functional recovery of rescued channels as a promising therapeutic for CHI caused by KATP trafficking defects.

AI Based Discovery of a New AKR1C3 Inhibitor for Anticancer Applications.

AKR1C3 is an upregulated enzyme in prostate and other cancers; in addition to regulating hormone synthesis, this enzyme is thought to play a role in the aggressiveness of tumors and in the defense against drugs. We here used an unbiased method to discover new potent AKR1C3 inhibitors: through an AI-based virtual drug screen, compound 4 was identified as a potent and selective enzymatic inhibitor able to translate this activity into a pronounced antiproliferative effect in the 22RV1 prostate cancer cell model. As other known AKR1C3 inhibitors, compound 4 determined a significantly increased activity of abiraterone, a drug approved for advanced prostate cancer. Compound 4 also showed a synergic effect with doxorubicin in osteosarcoma cell lines; specifically, the effect is correlated with AKR1C3 expression. In this research work, therefore, the use of AI allowed the identification of a new structure as an AKR1C3 inhibitor and its potential to enhance the effect of chemotherapeutics.

Identification of anti-pathogenic activity among in silico predicted small-molecule inhibitors of Pseudomonas aeruginosa LasR or nitric oxide reductase (NOR).

Introduction: Antibiotic-resistant Pseudomonas aeruginosa strains cause considerable morbidity and mortality globally. Identification of novel targets in this notorious pathogen is urgently warranted to facilitate discovery of new anti-pathogenic agents against it. This study attempted to identify small-molecule inhibitors of two important proteins LasR and nitric oxide reductase (NOR) in P. aeruginosa. 'Las' system can be said to be the 'master' regulator of quorum sensing in P. aeruginosa, whose receptor protein is LasR. Similarly, NOR is crucial to detoxification of reactive nitrogen species. Methods: In silico identification of potential LasR or NOR inhibitors was attempted through a virtual screening platform AtomNet® to obtain a final subset of <100 top scoring compounds. These compounds were evaluated for their in vivo anti-pathogenic activity by challenging the model host Caenorhabditis elegans with P. aeruginosa in the presence or absence of test compounds. Survival of the worm population in 24-well assay plates was monitored over a period of 5 days microscopically. Results: Of the 96 predicted LasR inhibitors, 11 exhibited anti-Pseudomonas activity (23%-96% inhibition of bacterial virulence as per third-day end-point) at 25-50 µg/mL. Of the 85 predicted NOR inhibitors, 8 exhibited anti-Pseudomonas activity (40%-85% inhibition of bacterial virulence as per second-day end-point) at 25-50 µg/mL. Conclusion: Further investigation on molecular mode of action of compounds found active in this study is warranted. Virtual screening can be said to be a useful tool in narrowing down the list of compounds requiring actual wet-lab screening, saving considerable time and efforts for drug discovery.

Development of Enterovirus Antiviral Agents That Target the Viral 2C Protein.

The Enterovirus (EV) genus includes several important human and animal pathogens. EV-A71, EV-D68, poliovirus (PV), and coxsackievirus (CV) outbreaks have affected millions worldwide, causing a range of upper respiratory, skin, and neuromuscular diseases, including acute flaccid myelitis, and hand-foot-and-mouth disease. There are no FDA-approved antiviral therapeutics for these enteroviruses. This study describes novel antiviral compounds targeting the conserved non-structural viral protein 2C with low micromolar to nanomolar IC50 values. The selection of resistant mutants resulted in amino acid substitutions in the viral capsid protein, implying these compounds may play a role in inhibiting the interaction of 2C and the capsid protein. The assembly and encapsidation stages of the viral life cycle still need to be fully understood, and the inhibitors reported here could be useful probes in understanding these processes.

Molecular dynamics re-refinement of two different small RNA loop structures using the original NMR data suggest a common structure.

Restrained molecular dynamics simulations are a robust, though perhaps underused, tool for the end-stage refinement of biomolecular structures. We demonstrate their utility-using modern simulation protocols, optimized force fields, and inclusion of explicit solvent and mobile counterions-by re-investigating the solution structures of two RNA hairpins that had previously been refined using conventional techniques. The structures, both domain 5 group II intron ribozymes from yeast ai5γ and Pylaiella littoralis, share a nearly identical primary sequence yet the published 3D structures appear quite different. Relatively long restrained MD simulations using the original NMR restraint data identified the presence of a small set of violated distance restraints in one structure and a possibly incorrect trapped bulge nucleotide conformation in the other structure. The removal of problematic distance restraints and the addition of a heating step yielded representative ensembles with very similar 3D structures and much lower pairwise RMSD values. Analysis of ion density during the restrained simulations helped to explain chemical shift perturbation data published previously. These results suggest that restrained MD simulations, with proper caution, can be used to "update" older structures or aid in the refinement of new structures that lack sufficient experimental data to produce a high quality result. Notable cautions include the need for sufficient sampling, awareness of potential force field bias (such as small angle deviations with the current AMBER force fields), and a proper balance between the various restraint weights.

Totopotensamides, polyketide-cyclic peptide hybrids from a mollusk-associated bacterium Streptomyces sp.

Two new compounds, the peptide-polyketide glycoside totopotensamide A (1) and its aglycone totopotensamide B (2), were isolated from a Streptomyces sp. cultivated from the gastropod mollusk Lienardia totopotens collected in the Philippines. The compounds contain a previously undescribed polyketide component, a novel 2,3-diaminobutyric acid-containing macrolactam, and a new amino acid, 4-chloro-5,7-dihydroxy-6-methylphenylglycine. The application of Marfey's method to phenylglycine derivatives was explored using quantum mechanical calculations and NMR.

Araiosamines A-D: tris-bromoindole cyclic guanidine alkaloids from the marine sponge Clathria (Thalysias) araiosa.

Four new tris-bromoindole cyclic guanidine alkaloids, araiosamines A-D, were isolated from the methanol extract of a marine sponge, Clathria (Thalysias) araiosa, collected from Vanuatu. Their carbon skeletons delineate a new class of indole alkaloids apparently derived from a linear polymerization process involving a carbon-carbon bond formation. Comparison of the structures including the relative configurations suggests a common intermediate containing a dihydroaminopyrimidine moiety capable of undergoing various modalities of conjugate addition to yield unprecedented ring systems.

Data-Driven Mapping of Gas-Phase Quantum Calculations to General Force Field Lennard-Jones Parameters.

Molecular dynamics simulations are helpful tools for a range of applications, ranging from drug discovery to protein structure determination. The successful use of this technology largely depends on the potential function, or force field, used to determine the potential energy at each configuration of the system. Most force fields encode all of the relevant parameters to be used in distinct atom types, each associated with parameters for all parts of the force field, typically bond stretches, angle bends, torsions, and nonbonded terms accounting for van der Waals and electrostatic interactions. Much attention has been paid to the nonbonded parameters and their derivation, which are important in particular due to their governance of noncovalent interactions, such as protein-ligand binding. Parametrization involves adjusting the nonbonded parameters to minimize the error between simulation results and experimental properties, such as heats of vaporization and densities of neat liquids. In this setting, determining the best set of atom types is far from trivial, and the large number of parameters to be fit for the atom types in a typical force field can make it difficult to approach a true optimum. Here, we utilize a previously described Minimal Basis Iterative Stockholder (MBIS) method to carry out an atoms-in-molecules partitioning of electron densities. Information from these atomic densities is then mapped to Lennard-Jones parameters using a set of mapping parameters much smaller than the typical number of atom types in a force field. This approach is advantageous in two ways: it eliminates atom types by allowing each atom to have unique Lennard-Jones parameters, and it greatly reduces the number of parameters to be optimized. We show that this approach yields results comparable to those obtained with the typed GAFF 1.7 force field, even when trained on a relatively small amount of experimental data.

Binding Thermodynamics of Host-Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative.

Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small-molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a druglike guest molecule have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations. The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and ß-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root-mean-square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.

Consensus Conformations of Dinucleoside Monophosphates Described with Well-Converged Molecular Dynamics Simulations.

Dinucleoside monophosphates (DNMPs) have been described using various experimental approaches as flexible molecules which generate ensembles populating at least a small set of different conformations in solution. However, due to limitations of each approach in its ability to delineate the ensemble of conformations, an accurate and quantitative description of certain conformational features has not been performed for all DNMPs. Here, we apply a temperature replica-exchange molecular dynamics approach to fully and quickly converge conformational distributions of all RNA DNMPs immersed in the TIP3P water model using the AMBER ff14 force field. For a selection of DNMPs, the conformational ensembles were also generated when immersed in the OPC water model using alternative AMBER and CHARMM force fields. The OPC water model and other force field choices did not introduce new conformational classes but shifted the populations among existing conformations. Except for pyrimidine-pyrimidine dinucleosides, all other DNMPs populated four major conformations (which are defined in the main text and labeled A-form, Ladder, Inverted, and Sheared), in addition to an Extended form. Pyrimidine-pyrimidines did not generate the Sheared conformation. Distinguishing features and stabilizing factors of each conformation were identified and assessed based on the known experimental interpretations. The configuration of the glycosidic bond and the nonbonding interactions of hydrogen bond acceptors with the 2'-hydroxyl group were found to play determining roles in stabilizing particular conformations which could serve as a guide for potential force field modifications to improve the accuracy. Additionally, we computed stacking free energies based on the DNMP conformational distributions and found significant discrepancies with a previous study. Our investigation determined that the AMBER force field was incorrectly implemented in the previous study. In the future, this simulation approach can be used to quickly analyze the effects of new force field modifications in shifting the conformational populations of DNMPs, and can can be further applied to foresee such effects in larger RNA motifs including tetranucleotides and tetraloops.