Relative Free Energies Research Articles

Flexibility of Binding Site is Essential to the Ca2+ Selectivity in EF-Hand Calcium-Binding Proteins.

High binding affinity and selectivity of metal ions are essential to the function of metalloproteins. Thus, understanding the factors that determine these binding characteristics is of major interest for both fundamental mechanistic investigations and guiding of the design of novel metalloproteins. In this work, we perform QM cluster model calculations and quantum mechanics/molecular mechanics (QM/MM) free energy simulations to understand the binding selectivity of Ca2+ and Mg2+ in the wild-type carp parvalbumin and its mutant. While a nonpolarizable MM model (CHARMM36) does not lead to the correct experimental trend, treatment of the metal binding site with the DFTB3 model in a QM/MM framework leads to relative binding free energies (ΔΔGbind) comparable with experimental data. For the wild-type (WT) protein, the calculated ΔΔGbind is ∼6.6 kcal/mol in comparison with the experimental value of 5.6 kcal/mol. The good agreement highlights the value of a QM description of the metal binding site and supports the role of electronic polarization and charge transfer to metal binding selectivity. For the D51A/E101D/F102W mutant, different binding site models lead to considerable variations in computed binding affinities. With a coordination number of seven for Ca2+, which is shown by QM/MM metadynamics simulations to be the dominant coordination number for the mutant, the calculated relative binding affinity is ∼4.8 kcal/mol, in fair agreement with the experimental value of 1.6 kcal/mol. The WT protein is observed to feature a flexible binding site that accommodates a range of coordination numbers for Ca2+, which is essential to the high binding selectivity for Ca2+ over Mg2+. In the mutant, the E101D mutation reduces the flexibility of the binding site and limits the dominant coordination number of Ca2+ to be seven, thereby leading to reduced binding selectivity against Mg2+. Our results highlight that the binding selectivity of metal ions depends on both the structural and dynamical properties of the protein binding site.

Synthesis, Characterization,Computational Studies and Antimicrobial activity evaluation of Mixed ligand‐Metal complexes of selected thiosemicarbazones

AbstractThe current work reports the synthesis of mixed ligand‐metal complexes of (2‐(2‐oxoacenaphthylen‐1 ylidene)hydrazinecarbothioamide) (L1) and carbamothioyl‐2‐chloronicotinohydrazonic acid (L2) and imidazole (im), triphenylphosphine (PPh3) and pyridine (py) ligands with Cu (II), Zn (II), Fe (II) and Co (II) salts.The chacterization was done through FT‐IR, NMR, UV‐visible, and elemental analysis. The X‐ray diffraction (XRD) patterns of [Cu(L1)(im)2(SO4)] demonstrated the crystalline morphology. In the FT‐IR spectra of the ligands, the characteristic frequencies corresponding to ν(C=N) (imine) and ν(C=S) (thiocarbonyl) were observed around 1650 and 1130 cm−1 respectively, which shifted upon complexation. In the 1H NMR spectra, the peak due to the NH proton was observed downfield, whereas that of ‐NH2 was observed comparatively upfield owing to its secondary amine nature. The molecular docking was performed against transforming growth beta (tgf‐β), protease of SARS‐CoV‐2 (Mpro) and aspartate kinase (Asp) kinase.The molecular simulation with the docked complex of [Zn(L2)(py)2(SO4)] and [Zn(L1)(PPh3)2(SO4)] with Asp kinase showed significant stability during the simulation time of 100 ns. The relative free energies of the simulated complexes were calculated with the molecular mechanics Poison‐Boltmann surface area (MM‐PBSA). The complexes were tested against ten bacterial strains and showed enhanced antibacterial activity than the ligands. [Cu(L1)(im)2(SO4)], [Zn(L1)(im)2(SO4)], [Fe(L1)(im)2(SO4)] and [Co(L1)(im)2(Cl2)] were found to be active against E.coli.

Exploring Free Energies of Specific Protein Conformations Using the Martini Force Field.

Coarse-grained (CG) level molecular dynamics simulations are routinely used to study various biomolecular processes. The Martini force field is currently the most widely adopted parameter set for such simulations. The functional form of this and several other CG force fields enforces secondary protein structure support by employing a variety of harmonic potentials or restraints that favor the protein's native conformation. We propose a straightforward method to calculate the energetic consequences of transitions between predefined conformational states in systems in which multiple factors can affect protein conformational equilibria. This method is designed for use within the Martini force field and involves imposing conformational transitions by linking a Martini-inherent elastic network to the coupling parameter λ. We demonstrate the applicability of our method using the example of five biomolecular systems that undergo experimentally characterized conformational transitions between well-defined structures (Staphylococcal nuclease, C-terminal segment of surfactant protein B, LAH4 peptide, and β2-adrenergic receptor) as well as between folded and unfolded states (GCN4 leucine zipper protein). The results show that the relative free energy changes associated with protein conformational transitions, which are affected by various factors, such as pH, mutations, solvent, and lipid membrane composition, are correctly reproduced. The proposed method may be a valuable tool for understanding how different conditions and modifications affect conformational equilibria in proteins.

Potassium tert‐Butoxide‐Catalyzed Selective Synthesis of 1,4‐Benzodioxepines via 7‐exo/7‐endo‐dig Cyclization of Alkynol: Experimental and DFT Study

AbstractA new method utilizing base catalysis is outlined for the synthesis of 1,4‐benzodioxepines, introducing a unique scaffold containing a seven‐membered ring heterocycle. The reaction proceeded under thermal conditions, with no waste was generated during the transformation, highlighting its remarkable atom efficiency and environmental friendliness. A straightforward metal‐free reaction condition utilizing 2‐propynyloxy benzyl alcohols as starting material and a catalytic amount of t‐BuOK in mesitylene at 100 °C for 1 hour was employed for this intramolecular cyclization transformation, resulting in the formation of 1,4‐Benzodioxepines in good to excellent yields and high regio‐ and diastereo‐selectivity. The density functional theory (DFT) calculations yielded the relative internal energy (U), enthalpy (H), and Gibbs free energy (G) values for all possible products, aligning closely with our experimental data.

Interplay between protonation and Jahn-Teller effects in a manganese vanadium cubane water oxidation catalyst.

Understanding the protonation behavior of metal-oxo water oxidation catalysts is essential to improve catalyst efficiency and long-term performance, as well as to tune their properties for specific applications. In this work, we explore the basicity and protonation effects of the highly active water oxidation catalyst [(Mn4O4) (V4O13) (OAc)3]3- using density functional theory. We computed the relative free energies of protonation in a systematic fashion for all symmetry-inequivalent O atoms, where the presence of multiple oxidation states from Mn4IV to Mn4III and a rich Jahn-Teller isomerism adds a significant amount of complexity. For high oxidation states, the compound behaves like some other polyoxometalates, showing protonation preferably at the terminal and μ2-bridging O atoms of the vanadate cap. However, upon reduction, eventually, the protonation preference switches to the cubane O atoms, mostly driven by a strong increase in basicity for O atoms located along the Jahn-Teller axes. Our work further evidences that protonation can potentially lead to several chemical transformations, like disproportionation and charge transfer to vanadium, dissociation of ligands, or the opening of the cubane structure. Our simulated UV/Vis absorption spectra additionally provide valuable insights about how the protonation of the catalyst could be tracked experimentally. Overall, our analysis highlights the complexity involved in the protonation of heterometallic polyoxometalate clusters.

The Jarzynski binding free energy can effectively rank ligand-protein affinities in inadequate samplings

The use of the Jarzynski equality for estimating ligand–protein binding free energies are illustrated for cases of inadequate samplings. Data obtained from a large amount (104) of work are thus converged to an incorrect value, due to creation of a distorted Gaussian-like distribution. However, even in such cases, the Jarzynski equality can effectively rank binding affinities in the early stage of rational drug design. A combination of steered molecular dynamics calculations, Jarzynski relation and block averaging can provide such relative binding free energies having significantly better correlations in the cases of the thrombin and HIV-1 complexes examined.

Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials.

This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.

Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability.

Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔGs) calculated from all-atom molecular dynamics free energy perturbation (FEP) simulations in explicit solvent. The fitness effects of mutations in the Potts model (ΔEs) showed good agreement with experimental thermostability data (Pearson r = 0.68), similar to the correlation we observed with ΔΔGs predicted from structure-based relative FEP simulations. Recognizing the possible advantages of using Potts models to rapidly estimate protein stability effects of kinase mutations seen in cancer genomics data, we used the Potts statistical energy model to estimate the stability effects of 65 conservative and nonconservative mutations across three distinct kinases (Wee1, Abl1, and Cdc7) with somatic mutations reported in the Genomic Data Commons (GDC) database. The ΔEs of these mutations calculated from the Potts model are consistent with the corresponding ΔΔGs from FEP simulations (Pearson ratio of 0.72). The agreement between these methods suggests that the Potts model may be used as a sequence-based tool for high-throughput screening of mutational effects as part of a computational pipeline for predicting the stability effects of mutations. We also demonstrate how the scalability of the fitness-based Potts model calculations permits analyses that are not easily accessed using FEP simulations. To this end, we employed site-saturation mutagenesis in the Potts model in order to investigate the relative stability effects of mutations seen in different cancer evolutionary scenarios. We used this approach to analyze the effects of drug pressure in Abl kinase by contrasting the relative fitness penalties of somatic mutations seen in miscellaneous cancer types with those calculated for mutations associated with cancer drug resistance. We observed that, in contrast to somatic mutations of Abl seen in various tumors that appear to have evolved neutrally, cancer mutations that evolved under drug pressure in Abl-targeted therapies tend to preserve enzyme stability.

Binding of Nucleotide Inhibitors to the NS5 RdRp of the ZIKA Virus in the Replication Initiation State.

The bindings of several ribonucleoside triphosphate (NTP) inhibitors to the RNA-dependent RNA polymerase (RdRp) of the Zika virus (ZIKV) are studied herein to identify potential drug-like candidates that can inhibit the replication of the viral genome by RdRp. In this study, a guanosine triphosphate (GTP) bound RdRp structure is generated to model the replication initiation state of RdRp. Subsequently, the bindings of 30 NTP inhibitors to the GTP binding site of RdRp are studied in detail by using the molecular docking method. Based on the docking scores, four NTP inhibitors, such as 2'-Cmethyl- adenosine-5'-triphosphate (mATP), 7-deaza-2'-C-methyladenosine-TP (daza-- mATP), 1-N6-Ethenoadenosine-5'-triphosphate (eATP), and Remdesivir-5'-triphosphate (RTP) are shortlisted for further analysis by employing molecular dynamics simulations and binding free-energy methods. These inhibitors are found to bind to RdRp quite strongly, as evident from their relative binding free energies that lie between -31.54±4.54 to -89.46±4.58 kcal/- mol. As the binding of RTP to the GTP site of RdRp generates the most stable complex, which is about 45 kcal/mol more stable than the binding of GTP to RdRp, it is most likely that RTP may inhibit the replication of the Zika viral genome efficiently. However, experimental studies are required to measure the potency of RTP and other drugs before their clinical use.

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations.

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization.

Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.

Computational exploration of FOXM1 inhibitors for glioblastoma: an integrated virtual screening and molecular dynamics simulation study

In this study, a comprehensive investigation of a set of phytochemicals to identify potential inhibitors for the Forkhead box protein M1 (FOXM1) was conducted. FOXM1 is overexpressed in glioblastoma (GBM) cells and plays a crucial role in cell cycle progression, proliferation, and invasion. FOXM1 inhibitors have shown promising results in preclinical studies, and ongoing clinical trials are assessing their efficacy in GBM patients. However, there are limited studies on the identification of novel compounds against this attractive therapeutic target. To address this, the NPACT database containing 1,574 phytochemicals was used, employing a hierarchical multistep docking approach, followed by an estimation of relative binding free energy. By fixing user-defined XP-dock and MM-GBSA cut-off scores of −6.096 and −37.881 kcal/mol, the chemical space was further narrowed. Through exhaustive analysis of molecular binding interactions and various pharmacokinetics profiles, we identified four compounds, namely NPACT00002, NPACT01454, NPACT00856, and NPACT01417, as potential FOXM1 inhibitors. To assess the stability of protein-ligand binding in dynamic conditions, 100 ns Molecular dynamics (MD) simulations studies were performed. Furthermore, Molecular mechanics with generalized Born and surface area solvation (MM-GBSA) based binding free energy estimations of the entire simulation trajectories revealed a strong binding affinity of all identified compounds towards FOXM1, surpassing that of the control drug Troglitazone. Based on extensively studied multistep docking approaches, we propose that these molecules hold promise as FOXM1 inhibitors for potential therapeutic applications in GBM. However, experimental validation will be necessary to confirm their efficacy as targeted therapies. Communicated by Ramaswamy H. Sarma

What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling.

We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor.

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations.

Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, computational and experimental methods capable of quantifying the energetic determinants of these equilibria are limited to systems of modest size. Recently, it has been demonstrated that the artificial intelligence system AlphaFold2 can be manipulated to produce structurally valid protein conformational ensembles. Here, we extend these studies and explore the extent to which AlphaFold2 contact distance distributions can approximate projections of the conformational Boltzmann distributions. For this purpose, we examine the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Our studies suggest that AlphaFold2 normalized contact distance distributions can correlate with conformation probabilities obtained with other methods but that they suffer from peak broadening. We also find that the AlphaFold2 contact distance distributions can be sensitive to point mutations. Overall, we anticipate that our findings will be valuable as the community seeks to model the thermodynamics of conformational changes in large biomolecular systems.

Computational study of the dimerization of glyphosate: mechanism and effect of solvent.

A computational study on the structure and stability of different series of glyphosate (Glyph) dimers comprising nonionized (N) and zwitterionic structures (Z) for neutral monomers, followed by an analysis of energetics of Glyph dimerization process have been performed by means of quantum chemical calculations in different media. Optimized geometries for energy minima, as well as relative potential and free energies of the possible various conformers of each series of Glyph dimer were computed as a function of the medium at B3LYP-D3/6-311++G(2d,2p) level. The solvation model based on density (SMD) is employed for all solution phase computations. Non-ionized dimers (DN), anion-cation (AC) and either zwitterion-zwitterion (DZP and DZC) or non-ionized-phosphonate zwitterion (NZP) ionized neutral forms of Glyph dimer are predicted to exist in the gas phase and in solution in large contrast to Glyph monomers. The DZC dimer form exhibiting a centrosymmetric arrangement of two carboxylate zwitterion units was found to be the most stable dimer structure in all media. In aqueous solution, the DZP and AC dimer type structures are significantly stabilized by hydration. The tautomerisms between DZC, DZP and AC dimer type structures have been investigated in the gas phase and in solution. The DZC type structures are more prone to experience proton transfer in water than in the gas phase and in cyclohexane. The mechanism for the tautomerization process in neutral ionized Glyph dimers proceeds via two direct proton transfer paths: DZP ⇋ AC ⇋ DZC. Results show that solvents play a key role in modulating the energetics of the dimerization process of Glyph. Solvation in cyclohexane, favors the dimerization process however, hydration opposed it. In aqueous solution, the mechanism of the dimerization of Glyph from its phosphonate zwitterionic monomer form (ZP) could be described by a set of equilibria including direct proton transfer paths as follows: 2ZP ⇋ DZP ⇋ AC ⇋ DZC. According to our results, in aqueous solution, DZC Glyph dimers and their corresponding DZP and AC tautomers should be present in higher concentration than phosphonate zwitterionic Glyph monomers for high Glyph concentration, a fact that seems controversial in the literature.

Identification of novel LRRK2 inhibitors by structure-based virtual screening and alchemical free energy calculation.

The Leucine-rich repeat kinase 2 (LRRK2) target has been identified as a promising drug target for Parkinson's disease (PD) treatment. This study focuses on optimizing the activity of LRRK2 inhibitors using alchemical relative binding free energy (RBFE) calculations. Initially, we assessed various free energy calculation methods across different LRRK2 kinase inhibitor scaffolds. The results indicate that alchemical free energy calculations are promising for prospective predictions on LRRK2 inhibitors, especially for the aminopyrimidine scaffold with an RMSE of 1.15 kcal mol-1 and Rp of 0.83. Following this, we optimized a potent LRRK2 kinase inhibitor identified from previous virtual screenings, featuring a novel scaffold. Guided by RBFE predictions using alchemical methods, this optimization led to the discovery of compound LY2023-001. This compound, with a [1,2,4]triazolo[5,6-b]indole scaffold, exhibited enhanced inhibitory activity against G2019S LRRK2 (IC50 = 12.9 nM). Molecular dynamics (MD) simulations revealed that LY2023-001 formed stable hydrogen bonds with Glu1948, and Ala1950 in the G2019S LRRK2 protein. Additionally, its phenyl substituents engage in strong electrostatic interactions with Lys1906 and van der Waals interactions with Leu1885, Phe1890, Val1893, Ile1933, Met1947, Leu1949, Leu2001, Ala2016, and Asp2017. Our findings underscore the potential of computational methods in the successful optimization of small molecules, offering important insights for the development of novel LRRK2 inhibitors.

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.

Adaptive lambda schemes for efficient relative binding free energy calculation.

The relative free energy perturbation (RFEP) calculation is one of the most theoretically sound computational chemistry approaches for the binding affinity prediction. However, its application is often hindered by the complexity of the calculation choices and the requirement of expertise in the field. Improper lambda scheme of RFEP may result in deviations from an accurate description of the perturbation process and is prone to erroneous affinity predictions. To address such challenges, an automated adaptive lambda method is proposed where the adaptive lambda schemes are obtained through a split-and-merge algorithm based on the pilot runs. The newly established workflow along with a series of improvements to the perturbation settings increases the consistency of the RFEP calculation results. Comparing the pilot and adaptive lambda schemes, the latter demonstrated improvements in convergence and reproducibility and lowered the mean unsigned error and the root-mean-square error. Overall, the adaptive lambda method is a reliable and robust choice to predict small molecule relative binding free energy and can be capitalized to benefit routine RFEP calculations for drug discovery projects.

Binding of Cholesterol to the N-Terminal Domain of the NPC1L1 Transporter: Analysis of the Epimerization-Related Binding Selectivity and Loop Mutations.

Cholesterol is a fat-like substance with a pivotal physiological relevance in humans, and its homeostasis is tightly regulated by various cellular processes, including the import in the small intestine and the reabsorption in the biliary ducts by the Niemann-Pick C1 Like 1 (NPC1L1) importer. NPC1L1 can mediate the absorption of a variety of sterols but strikingly exhibits a large sensitivity to cholesterol epimerization. This study examines the molecular basis of the epimerization-related selective binding of cholesterol by combining extended unbiased molecular dynamics simulations of the apo and holo species of the N-terminal domain of wild-type NPC1L1, in conjunction with relative binding free energy, umbrella sampling, and well-tempered metadynamics calculations. The analysis of the results discloses the existence of two distinct binding modes for cholesterol and epi-cholesterol. The former binds deeper in the cavity, forming key hydrogen-bond interactions with Q95, S56, and a water molecule. In contrast, epi-cholesterol is shifted ca. 3 Å to the mouth of the cavity and the transition to the Q95 site is prevented by an energetic barrier of 4.1 kcal·mol-1. Thus, the configuration of the hydroxyl group of cholesterol, together with the presence of a structural water molecule, is a key feature for effective absorption. Finally, whereas these findings may seemingly be challenged by single-point mutations that impair cholesterol transport but have a mild impact on the binding of cholesterol to the Q95 binding site, our results reveal that they have a drastic influence on the conformational landscape of the α8/β7 loop in the apo species compared to the wild-type protein. Overall, the results give support to the functional role played by the α8/β7 loop in regulating the access of ligands to NPC1L1, and hence to interpreting the impact of these mutations on diseases related to disruption of sterol absorption, paving the way to understanding certain physiological dysfunctions.