Recent progress in general force fields of small molecules

Electrochemical Sensing of Phenylalanine using Polyaniline-Based Molecularly Imprinted Polymers

Polyaniline (PANI)-based molecularly imprinted polymers were investigated for their efficacy in sensing phenylalanine (Phe) when fabricated on both glassy carbon electrode (GCE) and indium tin oxide (ITO) sheets. This study highlights the superior performance of PANI-MIP/ITO over PANI-MIP/GCE for sensing Phe, with clear and distinct redox responses. Molecular computation helps to understand the interaction mechanism between PANI and Phe, where molecular crowding, aggregated clusters, hydrogen bonding, and π-π stacking facilitate stable interactions. We tested the specificity of Phe sensing by PANI-MIP with different amino acids such as cysteine, tryptophan, and tyrosine as well as organic molecules such as ascorbic acid, allantoin, sucrose, and urea, confirming its remarkable electrochemical efficiency. The oxidation response curve yielded a limit of detection of 4.88 μM and a limit of quantification of 16.3 μM, comparable to or better than earlier reported sensors. This work demonstrates the promise of MIP-based electrochemical sensing. It also lays the groundwork for future investigations into optimizing PANI-MIPs with nanocomposites to develop more selective and stable sensors.

Exploring protein structural ensembles: Integration of sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling methods

Under physiological conditions, proteins continuously undergo structural fluctuations on different timescales. Some conformations are only sparsely populated, but still play a key role in protein function. Thus, meaningful structure-function frameworks must include structural ensembles rather than only the most populated protein conformations. To detail protein plasticity, modern structural biology combines complementary experimental and computational approaches. In this review, we survey available computational approaches that integrate sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling techniques to derive all-atom structural models of rare protein conformations. We also propose strategies to increase the reliability and improve efficiency using deep learning approaches, thus advancing the field of integrative structural biology.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Monte Carlo simulations for free energies of hydration: Past to present

A summary of the development of Monte Carlo statistical mechanics simulations for the computation of free energies of hydration of organic molecules is followed by presentation of results with the latest version of the optimized potentials for liquid simulations-all atom force field and the TIP4P water model. Scaling of the Lennard-Jones interactions between water, oxygen, and carbon atoms by a factor of 1.25 is found to improve the accuracy of free energies of hydration for 50 prototypical organic molecules from a mean unsigned error of 1.0-1.2 to 0.4 kcal/mol.

Deciphering molecular structures: NMR spectroscopy and quantum mechanical insights of halogenated 4H-Chromenediones

Sesquiterpene lactones (SL) represent a class of secondary metabolites found in the Asteraceae family, notable for their unique structures. The SL α-santonin (1) and its derivatives are worthy of mention due to their diverse biological properties. Additionally, 4H-chromenes and 4H-chromones are appealing frameworks holding the capability to be used as structural motifs for new drugs. Furthermore, unambiguous structural elucidation is crucial for developing novel compounds for diverse applications. In this context, it is common to find in the literature molecules erroneously assigned. Therefore, the use of quantum mechanical calculations to simulate NMR chemical shifts has emerged as a valuable strategy. In this work, we conceived the synthesis of two halogenated 4H-chromenediones derived from photosantonic acid (2), a photoproduct arising from irradiation of α-santonin (1) in the ultraviolet region. The structure of the chlorinated and brominated products was determined by NMR analysis, with the aid of quantum mechanical calculations at the B3LYP/6-311 + G(2d,p)//M062x/6-31 + G(d,p) level of theory. All analyses were in agreement and led to the assignment of the brominated 4H-chromene-2,7-dione as (3S,3aS,5aR,9bS)-5a-(2-bromopropan-2-yl)-3-methyl-3,3a,5,5a,8,9b-hexahydro-4H-furo[2,3-f]chromene-2,7-dione (11b) and of the chlorinated 4H-chromene-2,7-dione as (3S,3aS,5aR,9bS)-5a-(2-chloropropan-2-yl)-3-methyl-3,3a,5,5a,8,9b-hexahydro-4H-furo[2,3-f]chromene-2,7-dione (12b). The diastereoselectivities of the reactions were explained based on products and intermediates formation energy calculated using B3LYP/6-31 + G(d,p) as the level of theory. Structures 11b and 12b were identified as the thermodynamic and kinetic products of the reaction among all candidates. Consequently, the strategy utilized in this study is robust and successfully illustrates the use of quantum mechanical calculations in the structural elucidation of new compounds with potential applications as novel drugs or products.

Temperature-Dependent Dielectric Relaxation Measurements of (Betaine + Urea + Water) Deep Eutectic Solvent in Hz-GHz Frequency Window: Microscopic Insights into Constituent Contributions and Relaxation Mechanisms

A combined experimental and simulation study of dielectric relaxation (DR) of a deep eutectic solvent (DES) composed of betaine, urea, and water with the composition [Betaine:Urea:Water = 11.7:12:1 (weight ratio) and 9:18:5 (molar ratio)] was performed to explore and understand the interaction and dynamics of this system. Temperature-dependent (303 ≤ T/K ≤ 343) measurements were performed over 9 decades of frequency, combining three different measurement setups. Measured DR, comprising four distinct steps with relaxation times spreading over a few picoseconds to several nanoseconds, was found to agree well with simulations. The simulated total DR spectra, upon dissection into three self (intraspecies) and three cross (interspecies) interaction contributions, revealed that the betaine-betaine self-term dominated (∼65%) the relaxation, while the urea-urea and water-water interactions contributed only ∼7% and ∼1%, respectively. The cross-terms (betaine-urea, betaine-water, and urea-water) together accounted for <30% of the total DR. The slowest DR component with a time constant of ∼1-10 ns derived dominant contribution from betaine-betaine interactions, where betaine-water and urea-water interactions also contributed. The subnanosecond (0.1-0.6 ns) time scale originated from all interactions except betaine-water interaction. An extensive interaction of water with betaine and urea severely reduced the average number of water-water H-bonds (∼0.7) and heavily decreased the static dielectric constant of water in this DES (εs ∼ 2). Furthermore, simulated first rank collective single particle reorientational relaxations (C1(t)) and the structural H-bond fluctuation dynamics (CHB (t)) exhibited multiexponential kinetics with time scales that corresponded well with those found both in the simulated and measured DR.

Leveraging binding pose metadynamics to optimise target fishing predictions for three diverse ligands and their true targets

Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50-100, 100-150, and 250-300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50-100, 100-150, 150-200, and 200-250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure-based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts.

The SAMPL9 host-guest blind challenge: an overview of binding free energy predictive accuracy

We report the results of the SAMPL9 host-guest blind challenge for predicting binding free energies. The challenge focused on macrocycles from pillar[n]-arene and cyclodextrin host families, including WP6, and bCD and HbCD. A variety of methods were used by participants to submit binding free energy predictions. A machine learning approach based on molecular descriptors achieved the highest accuracy (RMSE of 2.04 kcal mol-1) among the ranked methods in the WP6 dataset. Interestingly, predictions for WP6 obtained via docking tended to outperform all methods (RMSE of 1.70 kcal mol-1), most of which are MD based and computationally more expensive. In general, methods applying force fields achieved better correlation with experiments for WP6 opposed to the machine learning and docking models. In the cyclodextrin-phenothiazine challenge, the ATM approach emerged as the top performing method with RMSE less than 1.86 kcal mol-1. Correlation metrics of ranked methods in this dataset were relatively poor compared to WP6. We also highlight several lessons learned to guide future work and help improve studies on the systems discussed. For example, WP6 may be present in other microstates other than its -12 state in the presence of certain guests. Machine learning approaches can be used to fine tune or help train force fields for certain chemistry (i.e. WP6-G4). Certain phenothiazines occupy distinct primary and secondary orientations, some of which were considered individually for accurate binding free energies. The accuracy of predictions from certain methods while starting from a single binding pose/orientation demonstrates the sensitivity of calculated binding free energies to the orientation, and in some cases the likely dominant orientation for the system. Computational and experimental results suggest that guest phenothiazine core traverses both the secondary and primary faces of the cyclodextrin hosts, a bulky cationic side chain will primarily occupy the primary face, and the phenothiazine core substituent resides at the larger secondary face.

Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein-ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.

OPLS/2020 Force Field for Unsaturated Hydrocarbons, Alcohols, and Ethers

The OPLS all-atom force field was updated and applied to modeling unsaturated hydrocarbons, alcohols, and ethers. Testing has included gas-phase conformational energetics, properties of pure liquids, and free energies of hydration. Monte Carlo statistical mechanics (MC) calculations were used to model 60 liquids. In addition, a robust, automated procedure was devised to compute the free energies of hydration with high precision via free-energy perturbation (FEP) calculations using double annihilation. Testing has included larger molecules than in the past, and parameters are reported for the first time for some less common groups including alkynes, allenes, dienes, and acetals. The average errors in comparison with experimental data for the computed properties of the pure liquids were improved with the modified force field (OPLS/2020). For liquid densities and heats of vaporization, the average unsigned errors are 0.01 g/cm3 and 0.2 kcal/mol. The average error and signed error for free energies of hydration are both 1.2 kcal/mol. As noted before, this reflects a systematic overestimate of the hydrophobicity of organic molecules when the parametrization is done to minimize the errors for properties of pure liquids. Implications for the modeling of biomolecular systems with standard force fields are considered.

Assessment of an Electrostatic Energy-Based Charge Model for Modeling the Electrostatic Interactions in Water Solvent

The protein force field based on the restrained electrostatic potential (RESP) charges has limitations in accurately describing hydrogen bonding interactions in proteins. To address this issue, we propose an alternative approach called the electrostatic energy-based charges (EEC) model, which shows improved performance in describing electrostatic interactions (EIs) of hydrogen bonds in proteins. In this study, we further investigate the performance of the EEC model in modeling EIs in water solvent. Our findings demonstrate that the fixed EEC model can effectively reproduce the quantum mechanics/molecular mechanics (QM/MM)-calculated EIs between a water molecule and various water solvent environments. However, to achieve the same level of computational accuracy, the electrostatic potential (ESP) charge model needs to fluctuate according to the electrostatic environment. Our analysis indicates that the requirement for charge adjustments depends on the specific mathematical and physical representation of EIs as a function of the environment for deriving charges. By comparing with widely used empirical water models calibrated to reproduce experimental properties, we confirm that the performance of the EEC model in reproducing QM/MM EIs is similar to that of general purpose TIP4P-like water models such as TIP4P-Ew and TIP4P/2005. When comparing the computed 10,000 distinct EI values within the range of -40 to 0 kcal/mol with the QM/MM results calculated at the MP2/aug-cc-pVQZ/TIP3P level, we noticed that the mean unsigned error (MUE) for the EEC model is merely 0.487 kcal/mol, which is remarkably similar to the MUE values of the TIP4P-Ew (0.63 kcal/mol) and TIP4P/2005 (0.579 kcal/mol) models. However, both the RESP method and the TIP3P model exhibit a tendency to overestimate the EIs, as evidenced by their higher MUE values of 1.761 and 1.293 kcal/mol, respectively. EEC-based molecular dynamics simulations have demonstrated that, when combined with appropriate van der Waals parameters, the EEC model can closely reproduce oxygen-oxygen radial distribution function and density of water, showing a remarkable similarity to the well-established TIP4P-like empirical water models. Our results demonstrate that the EEC model has the potential to build force fields with comparable accuracy to more sophisticated empirical TIP4P-like water models.

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

This manuscript provides an overview of the current state of the art in terms of the molecular modelling of the thermophysical properties of fluids. It is intended to manage the expectations and serve as guidance to practising physical chemists, chemical physicists and engineers in terms of the scope and accuracy of the more commonly available intermolecular potentials along with the peculiarities of the software and methods employed in molecular simulations while providing insights on the gaps and opportunities available in this field. The discussion is focused around case studies which showcase both the precision and the limitations of frequently used workflows.

The nucleotide excision repair proteins through the lens of molecular dynamics simulations

Mutations that affect the proteins responsible for the nucleotide excision repair (NER) pathway can lead to diseases such as xeroderma pigmentosum, trichothiodystrophy, Cockayne syndrome, and Cerebro-oculo-facio-skeletal syndrome. Hence, understanding their molecular behavior is needed to elucidate these diseases' phenotypes and how the NER pathway is organized and coordinated. Molecular dynamics techniques enable the study of different protein conformations, adaptable to any research question, shedding light on the dynamics of biomolecules. However, as important as they are, molecular dynamics studies focused on DNA repair pathways are still becoming more widespread. Currently, there are no review articles compiling the advancements made in molecular dynamics approaches applied to NER and discussing: (i) how this technique is currently employed in the field of DNA repair, focusing on NER proteins; (ii) which technical setups are being employed, their strengths and limitations; (iii) which insights or information are they providing to understand the NER pathway or NER-associated proteins; (iv) which open questions would be suited for this technique to answer; and (v) where can we go from here. These questions become even more crucial considering the numerous 3D structures published regarding the NER pathway's proteins in recent years. In this work, we tackle each one of these questions, revising and critically discussing the results published in the context of the NER pathway.

On the potentials of sialic acid derivatives as inhibitors for the mumps virus: A molecular dynamics and quantum chemistry investigation

Mumps virus is an infectious pathogen causing major health problems for humans such as encephalitis, orchitis, and parotitis. Therefore, designing an inhibitor for this virus is of great medical and public health importance. With this goal in mind, we investigate the affinity of different sialic acid-based compounds (ligands) against the hemagglutinin-neuraminidase (HN) protein of the mumps virus, using a combination of molecular dynamics (MD) simulations and quantum chemistry calculations. Our MD simulation results indicate that the ligands form stable complexes with the HN protein through a combination of electrostatic, van der Waals (vdW), and hydrogen bond (H-bond) interactions, which the electrostatic interactions play a more important role in the complexation process. Based on the obtained results from the structural analysis Arg381, Arg291, and Arg49 play a key role in the binding site interactions with the different ligands, in comparison with other residues. There are some candidates such as Neu5Acα2-6Galβ1-4GlcNAcβ, Neu5Acα2-3Galβ1-3GlcNacβ1-3Galβ1-4Glc, and Neu5Acα2-6Galβ1-4GlcNAcβ1-3Galβ1-4Glc that form more stable complexes with the HN than the α2-3-Sialyllactose confirmed by the calculated Gibbs binding energies (-39.65, -46.93, and -36.49 kcal.mol^-1, respectively). To investigate the relationship between the molecular properties of the selected compounds and their affinity to the HN receptor, density functional theory dispersion corrected (DFT-D3) calculations were employed. According to our DFT-D3 results, neutral sialic acid-based compounds have lower reactivity to the mumps virus than the negativity charge structures. Moreover, by increasing the electronic chemical potential (μ) the vdW and H-bond interactions between drugs and the HN protein increase. In other words, by elevating the electron tendency of the selected ligands their affinity to the mumps virus increases. Our quantum chemistry calculations reveal that in addition to the structural features the molecular properties of the drugs can play important roles in their affinity and reactivity against the virus. The results of this study can provide useful details to design new compounds or improve their properties against the mumps virus.

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation

Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.