A fast and high-quality charge model for the next generation general AMBER force field

Using Metadynamics to Reveal Extractant Conformational Free Energy Landscapes

Understanding the impact of extractant functionalization on metal-binding energetics in liquid-liquid extraction is essential to guide the development of better separation processes. Traditionally, computational extractant design uses electronic structure calculations on metal-ligand clusters to determine the metal-binding energy of the lowest energy state. Although highly accurate, this approach does not account for all of the relevant physics encountered under experimental conditions. Such methodologies often neglect entropic contributions such as temperature effects and ligand flexibility, in addition to approximating solvent-extractant interactions with implicit solvent models. In this study, we use classical molecular dynamics simulations with an advanced sampling method, metadynamics, to map out extractant molecule conformational free energies in the condensed phase. We generate the complete conformational landscape in solution for a family of bidentate malonamide-based extractants with different functionalizations of the headgroup and the side chains. In particular, we show how such alkyl functionalization reshapes the free energy landscape, affecting the free energy penalty of organizing the extractant into the cis-like metal-binding conformation from the trans-like conformation of the free extractant in solution. Specifically, functionalizing alkyl tails to the center of the headgroup has a greater influence on increasing molecular rigidity and disfavoring the binding conformation than functionalizing side chains. These findings are consistent with trends in metal-binding energetics based on experimentally reported distribution ratios. We also consider a different bidentate extractant molecule, carbamoylmethylphosphine oxide, and show how the choice of solvent can further reshape the conformational energetic landscape. This study demonstrates the feasibility of using molecular dynamics simulations with advanced sampling techniques to investigate extractant conformational energetics in solution, which, more broadly, will enable extractant design that accounts for entropic effects and explicit solvation.

Atomic Charge Dependency of Spiropyran/Merocyanine Adsorption as a Precursor to Surface Isomerization Reactions

This computational study investigates the adsorption of various spiropyran and merocyanine isomers on a NaCl substrate using a combination of density functional theory (DFT) and molecular mechanics (MM) calculations. Four different charge methods were used to determine the partial atomic charges for the adsorbate molecules, including Mulliken population analysis and three electrostatic potential (ESP) methods (Merz-Kollman, ChelpG, and Hu-Lu-Yang), while three different force fields (AMBER 3, CHARMM 27, and MM+) were employed for the MM calculations. The results show that the various DFT charge methods produced similar outcomes for the molecules' partial atomic charges, with some exceptions for individual atoms and methods. Additionally, it was found that the ESP charge methods were more sensitive to the conformer orientation than the Mulliken approach. The adsorption behavior of merocyanine conformers with the central bond in trans orientation (T-conformers) was similar for various configurations, with the molecule adsorbing mostly flat with its aromatic rings almost parallel to the substrate. However, C-conformers (with their central bond in cis orientation) and spiropyran isomers exhibited inconsistent adsorption behavior, mostly because only some of the aromatic rings contributed to the adsorption behavior. Due to additional van der Waals interactions of more aromatic rings, the adsorption energies for T-conformers are consistently 0.2-0.3 eV higher than for C-conformers and for spiropyran. The study found that the adsorption geometries and energies of stable T-conformers were independent of the partial atomic charge scheme and force field used, and C-conformers show parameter-dependent behavior upon adsorption, leading to metastable configurations. These findings indicate viable pathways during the spiropyran-merocyanine isomerization reactions. Therefore, the results provide initial insights into the possibility of switching spiropyran isomers into merocyanine isomers and vice versa after adsorption onto substrates.

Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands

The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.

Molecular Dynamics Simulations for Rationalizing Polymer Bioconjugation Strategies: Challenges, Recent Developments, and Future Opportunities

The covalent modification of proteins with polymers is a well-established method for improving the pharmacokinetic properties of therapeutically valuable biologics. The conjugated polymer chains of the resulting hybrid represent highly flexible macromolecular structures. As the dynamics of such systems remain rather elusive for established experimental techniques from the field of protein structure elucidation, molecular dynamics simulations have proven as a valuable tool for studying such conjugates at an atomistic level, thereby complementing experimental studies. With a focus on new developments, this review aims to provide researchers from the polymer bioconjugation field with a concise and up to date overview of such approaches. After introducing basic principles of molecular dynamics simulations, as well as methods for and potential pitfalls in modeling bioconjugates, the review illustrates how these computational techniques have contributed to the understanding of bioconjugates and bioconjugation strategies in the recent past and how they may lead to a more rational design of novel bioconjugates in the future.

Assessment of GAFF and OPLS Force Fields for Urea: Crystal and Aqueous Solution Properties

Molecular simulations such as Monte Carlo, molecular dynamics, and metadynamics have been used to provide insight into crystallization phenomena, including nucleation and crystal growth. However, these simulations depend on the force field used, which models the atomic and molecular interactions, to adequately reproduce relevant material properties for the phases involved. Two widely used force fields, the General AMBER Force Field (GAFF) and the Optimized Potential for Liquid Simulations (OPLS), including several variants, have previously been used for studying urea crystallization. In this work, we investigated how well four different versions of the GAFF force field and five different versions of the OPLS force field reproduced known urea crystal and aqueous solution properties. Two force fields were found to have the best overall performance: a specific urea charge-optimized GAFF force field and the original all-atom OPLS force field. It is recommended that a suitable testing protocol involving both solution and solid properties, such as that used in this work, is adopted for the validation of force fields used for simulations of crystallization phenomena.

Generalized open-source workflows for atomistic molecular dynamics simulations of viral helicases

Viral helicases are promising targets for the development of antiviral therapies. Given their vital function of unwinding double-stranded nucleic acids, inhibiting them blocks the viral replication cycle. Previous studies have elucidated key structural details of these helicases, including the location of substrate binding sites, flexible domains, and the discovery of potential inhibitors. Here we present a series of new Galaxy tools and workflows for performing and analyzing molecular dynamics simulations of viral helicases. We first validate them by demonstrating recapitulation of data from previous simulations of Zika (NS3) and SARS-CoV-2 (NSP13) helicases in apo and complex with inhibitors. We further demonstrate the utility and generalizability of these Galaxy workflows by applying them to new cases, proving their usefulness as a widely accessible method for exploring antiviral activity.

Evaluation of the Structure-Function Relationship of SGNH Lipase from Streptomyces rimosus by Site-Directed Mutagenesis and Computational Approach

Streptomyces rimosus extracellular lipase (SrL) is a multifunctional hydrolase belonging to the SGNH family. Here site-directed mutagenesis (SDM) was used for the first time to investigate the functional significance of the conserved amino acid residues Ser10, Gly54, Asn82, Asn213, and His216 in the active site of SrL. The hydrolytic activity of SrL variants was determined using para-nitrophenyl (pNP) esters with C4, C8, and C16 fatty acid chains. Mutation of Ser10, Asn82, or His216, but not Gly54, to Ala abolished lipase activity for all substrates. In contrast, the Asn213Ala variant showed increased enzymatic activity for C8 and C16 pNP esters. Molecular dynamics (MD) simulations showed that the interactions between the long alkyl chain substrate (C16) and Ser10 and Asn82 were strongest in Asn213Ala SrL. In addition to Asn82, Gly54, and Ser10, several new constituents of the substrate binding site were recognized (Lys28, Ser53, Thr89, and Glu212), as well as strong electrostatic interactions between Lys28 and Glu212. In addition to the H bonds Ser10-His216 and His216-Ser214, Tyr11 interacted strongly with Ser10 and His216 in all complexes with an active enzyme form. A previously unknown strong H bond between the catalytically important Asn82 and Gly54 was uncovered, which stabilizes the substrate in an orientation suitable for the enzyme reaction.

Two distinct mechanisms of flavoprotein spectral tuning revealed by low-temperature and time-dependent spectroscopy

Flavins such as flavin mononucleotide or flavin adenine dinucleotide are bound by diverse proteins, yet have very similar spectra when in the oxidized state. Recently, we developed new variants of flavin-binding protein CagFbFP exhibiting notable blue (Q148V) or red (I52V A85Q) shifts of fluorescence emission maxima. Here, we use time-resolved and low-temperature spectroscopy to show that whereas the chromophore environment is static in Q148V, an additional protein-flavin hydrogen bond is formed upon photoexcitation in the I52V A85Q variant. Consequently, in Q148V, excitation, emission, and phosphorescence spectra are shifted, whereas in I52V A85Q, excitation and low-temperature phosphorescence spectra are relatively unchanged, while emission spectrum is altered. We also determine the x-ray structures of the two variants to reveal the flavin environment and complement the spectroscopy data. Our findings illustrate two distinct color-tuning mechanisms of flavin-binding proteins and could be helpful for the engineering of new variants with improved optical properties.

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papainlike endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

Structure-based virtual screening and molecular dynamics simulations for detecting novel candidates for allosteric inhibition of EGFRT790M

Structure-based virtual screening (SBVS) was applied to predict lead compounds for the allosteric inhibition of epidermal growth factor receptor (EGFR) by screening the library of chemical compounds prepared from the e-molecules chemical database. The library of chemical compounds consisting of 133,083 ligands was composed by evaluating the chemical and physical properties of e-molecules chemicals. The prepared library was screened by CCDC Gold software in the allosteric binding site of EGFRT790M using the library and virtual screening default parameters to filter out, respectively. The GOLD fitness scores 75 and 80 were selected as threshold values for the library and virtual screening processes, respectively. After the docking study, molecular dynamics simulations (MDS) of the top 25 compounds were built for calculating binding free energies from their MDS trajectories. MM-GBSA binding free energies for the compounds were computed from 20 ns MDS, 50 ns MDS and 200 ns MDS trajectories to filter out the candidates. Following MM-GBSA/MM-PBSA binding free energy calculations, six compounds were detected as the most promising candidates for allosteric inhibition of EGFRT790M. The dynamic behaviors of final compounds inside EGFR T790M were searched using structure stability, binding modes and energy decomposition analysis. Besides, the estimated inhibitors were exposed to docking study and MM-GBSA/MM-PBSA binding free energy calculations inside wild-type EGFR, respectively, to be determined their selectivity towards mutant form. Five of the estimated inhibitors displayed estimated selectivity towards EGFRT790M. Besides the ADMET properties of the estimated inhibitors were predicted by PreAdmet tools.Communicated by Ramaswamy H. Sarma.

De novo design of drug-binding proteins with predictable binding energy and specificity

The de novo design of small-molecule-binding proteins has seen exciting recent progress; however, the ability to achieve exquisite affinity for binding small molecules while tuning specificity has not yet been demonstrated directly from computation. Here, we develop a computational procedure that results in the highest affinity binders to date with predetermined relative affinities, targeting a series of PARP1 inhibitors. Two of four designed proteins bound with affinities ranging from < 5 nM to low μM, in a predictable manner. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free-energy calculations performed directly on the designed models are in excellent agreement with the experimentally measured affinities, suggesting that the de novo design of small-molecule-binding proteins with tuned interaction energies is now feasible entirely from computation. We expect these methods to open many opportunities in biomedicine, including rapid sensor development, antidote design, and drug delivery vehicles.

Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson-Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges

Accurately predicting solvation free energy is the key to predict protein-ligand binding free energy. In addition, the partition coefficient (log P), which is an important physicochemical property that determines the distribution of a drug in vivo, can be derived directly from transfer free energies, i.e., the difference between solvation free energies (SFEs) in different solvents. Within the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) 9 challenge, we applied the Poisson-Boltzmann (PB) surface area (SA) approach to predict the toluene/water transfer free energy and partition coefficient (log Ptoluene/water) from SFEs. For each solute, only a single conformation automatically generated by the free software Open Babel was used. The PB calculation directly adopts our previously optimized boundary definition - a set of general AMBER force field 2 (GAFF2) atom-type based sphere radii for solute atoms. For the non-polar SA model, we newly developed the solvent-related molecular surface tension parameters γ and offset b for toluene and cyclohexane targeting experimental SFEs. This approach yielded the highest predictive accuracy in terms of root mean square error (RMSE) of 1.52 kcal mol-1 in transfer free energy for 16 small drug molecules among all 18 submissions in the SAMPL9 blind prediction challenge. The re-evaluation of the challenge set using multi-conformation strategies based on molecular dynamics (MD) simulations further reduces the prediction RMSE to 1.33 kcal mol-1. At the same time, an additional evaluation of our PBSA method on the SAMPL5 cyclohexane/water distribution coefficient (log Dcyclohexane/water) prediction revealed that our model outperformed COSMO-RS, the best submission model with RMSEPBSA = 1.88 versus RMSECOSMO-RS = 2.11 log units. Two external log Ptoluene/water and log Pcyclohexane/water datasets that contain 110 and 87 data points, respectively, are collected for extra validation and provide an in-depth insight into the error source of the PBSA method.

Structural and thermodynamic framework for PIEZO1 modulation by small molecules

Mechanosensitive PIEZO channels constitute potential pharmacological targets for multiple clinical conditions, spurring the search for potent chemical PIEZO modulators. Among them is Yoda1, a widely used synthetic small molecule PIEZO1 activator discovered through cell-based high-throughput screening. Yoda1 is thought to bind to PIEZO1's mechanosensory arm domain, sandwiched between two transmembrane regions near the channel pore. However, how the binding of Yoda1 to this region promotes channel activation remains elusive. Here, we first demonstrate that cross-linking PIEZO1 repeats A and B with disulfide bridges reduces the effects of Yoda1 in a redox-dependent manner, suggesting that Yoda1 acts by perturbing the contact between these repeats. Using molecular dynamics-based absolute binding free energy simulations, we next show that Yoda1 preferentially occupies a deeper, amphipathic binding site with higher affinity in PIEZO1 open state. Using Yoda1's binding poses in open and closed states, relative binding free energy simulations were conducted in the membrane environment, recapitulating structure-activity relationships of known Yoda1 analogs. Through virtual screening of an 8 million-compound library using computed fragment maps of the Yoda1 binding site, we subsequently identified two chemical scaffolds with agonist activity toward PIEZO1. This study supports a pharmacological model in which Yoda1 activates PIEZO1 by wedging repeats A and B, providing a structural and thermodynamic framework for the rational design of PIEZO1 modulators. Beyond PIEZO channels, the three orthogonal computational approaches employed here represent a promising path toward drug discovery in highly heterogeneous membrane protein systems.

Binding of Per- and Polyfluoroalkyl Substances (PFAS) to the PPARγ/RXRα-DNA Complex

Nuclear receptors are the fundamental building blocks of gene expression regulation and the focus of many drug targets. While binding to DNA, nuclear receptors act as transcription factors, governing a multitude of functions in the human body. Peroxisome proliferator-activator receptor γ (PPARγ) and the retinoid X receptor α (RXRα) form heterodimers with unique properties and have a primordial role in insulin sensitization. This PPARγ/RXRα heterodimer has been shown to be impacted by per- and polyfluoroalkyl substances (PFAS) and linked to a variety of significant health conditions in humans. Herein, a selection of the most common PFAS (legacy and emerging) was studied utilizing molecular dynamics simulations for PPARγ/RXRα. The local and global structural effects of PFAS binding on the known ligand binding pockets of PPARγ and RXRα as well as the DNA binding domain (DBD) of RXRα were inspected. The binding free energies were predicted computationally and were compared between the different binding pockets. In addition, two electronic structure approaches were utilized to model the interaction of PFAS within the DNA binding domain, density functional theory (DFT) and domain-based pair natural orbital coupled cluster with perturbative triples (DLPNO-CCSD(T)) approaches, with implicit solvation. Residue decomposition and hydrogen-bonding analysis were also performed, detailing the role of prominent residues in molecular recognition. The role of l-carnitine is explored as a potential in vivo remediation strategy for PFAS interaction with the PPARγ/RXRα heterodimer. In this work, it was found that PFAS can bind and act as agonists for all of the investigated pockets. For the first time in the literature, PFAS are postulated to bind to the DNA binding domain in a nonspecific manner. In addition, for the PPARγ ligand binding domain, l-carnitine shows promise in replacing smaller PFAS from the pocket.

Density functional theory study of crown ether-magnesium complexes: from a solvated ion to an ion trap

Metal ion detection rests on host-guest recognition. We propose a theoretical protocol for designing an optimal trap for a desired metal cation. A host for magnesium ions was sought for among derivatives of crown ethers 12-crown-4, 15-crown-5, and 18-crown-6. Mg-crown complexes and their hydrated counterparts with water molecules bound to the cation were optimized using density functional theory. Based on specific geometric criteria, Interacting quantum atoms analysis and density functional theory-based molecular dynamics of Mg-crown complexes immersed in water, crown ethers for optimal accommodation of Mg2+ in aqueous solution were identified. Selectivity of the chosen crowns towards Na+, K+, and Ca2+ ions is addressed.

Computational prediction of analog compounds of the membrane protein MCHR1 antagonists ALB-127158 and KRX-104130

Obesity, which is already pervasive throughout the world, endangers public health by raising the prevalence of metabolic disorders and making their treatment more difficult. The development of drugs to treat obesity is a focus of effort. Melanin concentrated hormone receptor 1 (MCHR1) is the target of some of these therapeutic possibilities since as increased levels of melanin concentrated hormone have been found in obesity models. Known MCHR1 antagonists include BMS-830216, GW-856464, NGD-4715, ALB-127158, and AMG 076, but many have failed phase-I clinical studies. As a potential treatment for cardiotoxicity, KRX-104130 has only recently been identified. As MCH system is potentially effective target for treatment of obesity, in silico research into interaction between MCHR1 and its antagonists at molecular level was the primary goal of this study. Analogues ALB-127158 and KRX-104130 were screened among the RealEnamine library. The complexes obtained by molecular docking were embedded in mimics brain-cell membrane and simulated for 540 ns, and then MM-GBSA were calculated with MMPBSA.py. With all these computational studies, similar or different aspects of selected analogous compounds to ALB-127158 and KRX-104130 were investigated. The specificity of this study was that it analyzed MCHR1 protein as embedded in membrane. It was concluded that KRX-104130's analogue Z1922310273 and ALB-127158's analogue PV-002757495233 did not cause a difference in terms of phospholipid membrane properties. In addition, all ligands remained stable in putative binding site. It has been suggested that PV-002757495233 and Z1922310273 compounds can be evaluated as MCHR1 antagonists when all these outputs are considered in melting pots.

Identification of potential agonist-like molecules for α2-adrenergic receptor by multi-layer virtual screening to combat sinusitis

Sinusitis is one of the most common respiratory inflammatory conditions and a significant health issue that affects millions of people worldwide with a global prevalence of 10-15%. The side effects of available drug regimens of sinus infection demand the urgent development of new drug candidates to combat sinusitis. With the aim of identifying new drug-like candidates to control sinus, we have conducted multifold comprehensive screening of drug-like molecules targeting α2-adrenergic receptor (α2-AR), which serve as the primary drug target in sinusitis. By structure-based virtual screening of in-house compound's database, ten molecules (CP1-CP10) with agonistic effects for α2-AR were selected, and their binding mechanism with critical residues of α2-AR and their physicochemical properties were studied. Moreover, the process of receptor activation by these compounds and the conformational changes in α2-AR caused by these molecules, were further explored by molecular dynamic simulation. The MM-PBSA estimated free energies of compounds are higher than that of reference agonist (ΔGTOTAL = -39.0 kcal/mol). Among all, CP2-CP3, CP7-CP8 and CP6 have the highest binding free energies of -78.9 kcal/mol, -77.3 kcal/mol, -75.60 kcal/mol, -64.8 kcal/mol, and -61.6 kcal/mol, respectively. While CP4 (-55.0 kcal/mol), CP5 (-49.2 kcal/mol), CP9 (-54.8 ± 0.07 kcal/mol), CP10 (-56.7 ± 0.10 kcal/mol) and CP1 (-46.0 ± 0.08 kcal/mol) also exhibited significant binding free energies. These energetically favorable binding energies indicate strong binding affinity of our compounds for α2-AR as compared to known partial agonist. Therefore, these molecules can serve as excellent drug-like candidates for sinusitis.

Malvidin and Its Mono- and Di-Glucosides Forms: A Study of Combining Both In Vitro and Molecular Docking Studies Focused on Cholinesterase, Butyrylcholinesterase, COX-1 and COX-2 Activities

Malvidin, one of the six most prominent anthocyanins found in various fruits and vegetables, may possess a wide range of health-promoting properties. The biological activity of malvidin and its glycosides is not entirely clear and has been relatively less frequently studied compared to other anthocyanins. Therefore, this study aimed to determine the relationship between the structural derivatives of malvidin and their anti-cholinergic and anti-inflammatory activity. The study selected malvidin (Mv) and its two sugar derivatives: malvidin 3-O-glucoside (Mv 3-glc) and malvidin 3,5-O-diglucoside (Mv 3,5-diglc). The anti-inflammatory activity was assessed by inhibiting the enzymes, specifically COX-1 and COX-2. Additionally, the inhibitory effects on cholinesterase activity, particularly acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), were evaluated. Molecular modeling was also employed to examine and visualize the interactions between enzymes and anthocyanins. The results revealed that the highest inhibitory capacity at concentration 100 µM was demonstrated by Mv 3-glc in relation to AChE (26.3 ± 3.1%) and BChE (22.1 ± 3.0%), highlighting the crucial role of the glycoside substituent at the C3 position of the C ring in determining the inhibitory efficiency of these enzymes. In addition, the glycosylation of malvidin significantly reduced the anti-inflammatory activity of these derivatives compared to the aglycone form. The IC50 parameter demonstrates the following relationship for the COX-1 enzyme: Mv (12.45 ± 0.70 µM) < Mv 3-glc (74.78 ± 0.06 µM) < Mv 3,5-diglc (90.36 ± 1.92 µM). Similarly, for the COX-2 enzyme, we have: Mv (2.76 ± 0.16 µM) < Mv 3-glc (39.92 ± 3.02 µM) < Mv 3.5-diglc (66.45 ± 1.93 µM). All tested forms of malvidin exhibited higher activity towards COX-2 compared to COX-1, indicating their selectivity as inhibitors of COX-2. Theoretical calculations were capable of qualitatively replicating most of the noted patterns in the experimental data, explaining the impact of deprotonation and glycosylation on inhibitory activity. It can be suggested that anthocyanins, such as malvidins, could be valuable in the development of treatments for inflammatory conditions and Alzheimer's disease and deserve further study.

Origin of the enantioselectivity of alcohol dehydrogenase

Alcohol dehydrogenases (ADH) are a family of enzymes that catalyse the interconversion between ketones/aldehydes and alcohols in the presence of NADPH cofactor. It is challenging to desymmetrise the substituted cyclopentane-1,3-dione by engineering an ADH, while the reaction mechanism of the metal independent ADH remains elusive. Here we measured the conversion of a model substrate 2-benzyl-2-methylcyclopentane-1,3-dione by LbADH and found it predominately gave the (2R,3R) product. Binding mode analysis of the substrate in LbADH from molecular dynamics simulations disclosed the origin of the enantioselectivity of the enzyme; the opening and closing of the loop 191-205 above the substrate are responsible for shaping the binding pocket to orientate the substrate, so as to give different stereoisomer products. Using QM/MM calculations, we elucidated the reaction mechanism of LbADH. Furthermore, we demonstrated the reaction profile corresponding to the production of different stereoisomers, which is in accordance with our experimental observations. This research here will shed a light on the rational engineering of ADH to achieve stereodivergent stereoisomer products.

Discovery of potential RIPK1 inhibitors by machine learning and molecular dynamics simulations

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) plays a crucial role in inflammation and cell death, so it is a promising candidate for the treatment of autoimmune, inflammatory, neurodegenerative, and ischemic diseases. So far, there are no approved RIPK1 inhibitors available. In this study, four machine learning algorithms were employed (random forest, extra trees, extreme gradient boosting and light gradient boosting machine) to predict small molecule inhibitors of RIPK1. The statistical metrics revealed similar performance and demonstrated outstanding predictive capabilities in all four models. Molecular docking and clustering analysis were employed to confirm six compounds that are structurally distinct from existing RIPK1 inhibitors. Subsequent molecular dynamics simulations were performed to evaluate the binding ability of these compounds. Utilizing the Shapley additive explanation (SHAP) method, the 1855 bit has been identified as the most significant molecular fingerprint fragment. The findings propose that these six small molecules exhibit promising potential for targeting RIPK1 in associated diseases. Notably, the identification of Cpd-1 small molecule (ZINC000085897746) from the Musa acuminate highlights its natural product origin, warranting further attention and investigation.

Computational Modeling of Stapled Coiled-Coil Inhibitors Against Bcr-Abl: Toward a Treatment Strategy for CML

The chimeric oncoprotein Bcr-Abl is the causative agent of virtually all chronic myeloid leukemias (CML) and a subset of acute lymphoblastic leukemias (ALL). As a result of the so-called Philadelphia Chromosome translocation t(9;22), Bcr-Abl manifests as a constitutively active tyrosine kinase which promotes leukemogenesis by activation of cell cycle signaling pathways. Constitutive and oncogenic activation is mediated by an N-terminal coiled-coil oligomerization domain in Bcr (Bcr-CC), presenting a therapeutic target for inhibition of Bcr-Abl activity toward the treatment of Bcr-Abl+ leukemias. Previously, we demonstrated that a rationally designed Bcr-CC mutant, CCmut3, exerts a dominant negative effect upon Bcr-Abl activity by preferential oligomerization with Bcr-CC. Moreover, we have shown conjugation to a leukemia-specific cell-penetrating peptide (CPP-CCmut3) improves intracellular delivery and activity. However, our full-length CPP-CCmut3 construct (81 aa) is encumbered by an intrinsically high degree of conformational variability and susceptibility to proteolytic degradation, relative to traditional small molecule therapeutics. Here, we iterate a new generation of our inhibitor against Bcr-CC mediated Bcr-Abl assembly that is designed to address these constraints through incorporation of all-hydrocarbon staples spanning i, i + 7 positions in helix α2 (CPP-CCmut3-st). We utilize computational modeling and biomolecular simulation to design and characterize single and double staple candidates in silico, evaluating binding energetics and building upon our seminal work modeling single hydrocarbon staples when applied to a truncated Bcr-CC sequence. This strategy enables us to efficiently build, characterize, and screen lead single/double stapled CPP-CCmut3-st candidates for experimental studies and validation in vitro and in vivo. In addition to full-length CPP-CCmut, we model a truncated system characterized by deletion of helix α1 and the flexible-loop linker, which are known to impart high conformational variability. To study the impact of the N-terminal cyclic CPP toward model stability and inhibitor activity, we also model the full-length and truncated systems without CPP, with cyclized CPP, and with linear CPP, for a total of six systems which comprise our library. From this library, we present lead stapled peptide candidates to be synthesized and evaluated experimentally as our next-generation inhibitors against Bcr-Abl.

Analysis of structure-activity relationship of indol-3-yl-N-phenylcarbamic amides as potent STING inhibitors

A structure-activity relationship (SAR) study of stimulator of interferon gene (STING) inhibition was performed using a series of indol-3-yl-N-phenylcarbamic amides and indol-2-yl-N-phenylcarbamic amides. Among these analogs, compounds 10, 13, 15, 19, and 21 inhibited the phosphorylation of STING and interferon regulatory factor 3 (IRF3) to a greater extent than the reference compound, H-151. All five analogs showed stronger STING inhibition than H-151 on the 2',3'-cyclic GMP-AMP-induced expression of interferon regulatory factors (IRFs) in a STINGR232 knock-in THP-1 reporter cell line. The half-maximal inhibitory concentration of the most potent compound, 21, was 11.5 nM. The molecular docking analysis of compound 21 and STING combined with the SAR study suggested that the meta- and para-positions of the benzene ring of the phenylcarbamic amide moiety could be structurally modified by introducing halides or alkyl substituents.

An Exploration of Small Molecules That Bind Human Single-Stranded DNA Binding Protein 1

Human single-stranded DNA binding protein 1 (hSSB1) is critical to preserving genome stability, interacting with single-stranded DNA (ssDNA) through an oligonucleotide/oligosaccharide binding-fold. The depletion of hSSB1 in cell-line models leads to aberrant DNA repair and increased sensitivity to irradiation. hSSB1 is over-expressed in several types of cancers, suggesting that hSSB1 could be a novel therapeutic target in malignant disease. hSSB1 binding studies have focused on DNA; however, despite the availability of 3D structures, small molecules targeting hSSB1 have not been explored. Quinoline derivatives targeting hSSB1 were designed through a virtual fragment-based screening process, synthesizing them using AlphaLISA and EMSA to determine their affinity for hSSB1. In parallel, we further screened a structurally diverse compound library against hSSB1 using the same biochemical assays. Three compounds with nanomolar affinity for hSSB1 were identified, exhibiting cytotoxicity in an osteosarcoma cell line. To our knowledge, this is the first study to identify small molecules that modulate hSSB1 activity. Molecular dynamics simulations indicated that three of the compounds that were tested bound to the ssDNA-binding site of hSSB1, providing a framework for the further elucidation of inhibition mechanisms. These data suggest that small molecules can disrupt the interaction between hSSB1 and ssDNA, and may also affect the ability of cells to repair DNA damage. This test study of small molecules holds the potential to provide insights into fundamental biochemical questions regarding the OB-fold.

Synergistic Interaction of Hyperbranched Polyglycerols and Cetyltrimethylammonium Bromide for Oil/Water Interfacial Tension Reduction: A Molecular Dynamics Study

Applying surfactants to reduce the interfacial tension (IFT) on water/oil interfaces is a proven technique. The search for new surfactants and delivery strategies is an ongoing research area with applications in many fields such as drug delivery through nanoemulsions and enhanced oil recovery. Experimentally, the combination of hyperbranched polyglycerol (HPG) with cetyltrimethylammonium bromide (CTAB) substantially reduced the observed IFT of oil/water interface, 0.9 mN/m, while HPG alone was 5.80 mN/m and CTAB alone IFT was 8.08 mN/m. Previous simulations in an aqueous solution showed that HPG is a surfactant carrier. Complementarily, in this work, we performed classical molecular dynamics simulations on combinations of CTAB and HPG with one aliphatic chain to investigate further the interaction of this pair in oil interfaces and propose the mechanism of IFT decrease. Basically, from our results, one can observe that the IFT reduction comes from a combination of effects that have not been observed for other dual systems: (i) Due to the CTAB-HPG strong interaction, a weakening of their specific and isolated interactions with the water and oil phases occurs. (ii) Aggregates enlarge the interfacial area, turning it into a less ordered interface. (iii) The spread of individual molecules charge profiles leads to the much lower interfacial tension observed with the CTAB+HPG systems.

Unravel the Tangle: Atomistic Insight into Ultrahigh Curcumin-Loaded Polymer Micelles

Amphiphilic ABA-triblock copolymers, comprised of poly(2-oxazoline) and poly(2-oxazine), can solubilize poorly water-soluble molecules in a structure-dependent manner forming micelles with exceptionally high drug loading. All-atom molecular dynamics simulations are conducted on previously experimentally characterized, curcumin-loaded micelles to dissect the structure-property relationships. Polymer-drug interactions for different levels of drug loading and variation in polymer structures of both the inner hydrophobic core and outer hydrophilic shell are investigated. In silico, the system with the highest experimental loading capacity shows the highest number of drug molecules encapsulated by the core. Furthermore, in systems with lower loading capacity outer A blocks show a greater extent of entanglement with the inner B blocks. Hydrogen bond analyses corroborate previous hypotheses: poly(2-butyl-2-oxazoline) B blocks, found experimentally to have reduced loading capacity for curcumin compared to poly(2-propyl-2-oxazine), establish fewer but longer-lasting hydrogen bonds. This possibly results from different sidechain conformations around the hydrophobic cargo, which is investigated by unsupervised machine learning to cluster monomers in smaller model systems mimicking different micelle compartments. Exchanging poly(2-methyl-2-oxazoline) with poly(2-ethyl-2-oxazoline) leads to increased drug interactions and reduced corona hydration; this suggests an impairment of micelle solubility or colloidal stability. These observations can help driving forward a more rational a priori nanoformulation design.

An interoperable implementation of collective-variable based enhanced sampling methods in extended phase space within the OpenMM package

Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques. Extended variables are kept at a much higher temperature than the physical temperature by ensuring adiabatic separation between the extended and physical subsystems and employing rigorous thermostatting. In this work, we present a computational platform to perform extended phase space enhanced sampling simulations using the open-source molecular dynamics engine OpenMM. The implementation allows users to have interoperability of sampling techniques, as well as employ state-of-the-art thermostats and multiple time-stepping. This work also presents protocols for determining the critical parameters and procedures for reconstructing high-dimensional free energy surfaces. As a demonstration, we present simulation results on the high dimensional conformational landscapes of the alanine tripeptide in vacuo, tetra-N-methylglycine (tetra-sarcosine) peptoid in implicit solvent, and the Trp-cage mini protein in explicit water.

Synthesis and Binding Mode Predictions of Novel Siglec-7 Ligands

Siglec-7 regulates immune cell activity and is a promising target for immunomodulation. Here, we report the discovery of novel sialic acid derivatives binding to Siglec-7. Synthesis and affinity measurements are complemented by high-quality models of sialoside-Siglec-7 complexes based on molecular dynamics (MD) simulations on the microsecond time scale. We provide details for the predicted binding modes for the new ligands, e.g., that an extension of the carbon backbone leads to a different molecular interaction pattern with the receptor and the nearby water structure than found for known Siglec-7 ligands. Further on, we uncover some shortcomings of the GLYCAM06 and GAFF2 force fields when used for the simulation of sialoside-based glycomimetics. Our results open new opportunities for the rational design of Siglec-7 inhibitors. In addition, we provide strategies on how to use and visualize MD simulations to describe and investigate sialoside-Siglec complexes in general.

LVC/MM: A Hybrid Linear Vibronic Coupling/Molecular Mechanics Model with Distributed Multipole-Based Electrostatic Embedding for Highly Efficient Surface Hopping Dynamics in Solution

We present a theoretical framework for a hybrid linear vibronic coupling model electrostatically embedded into a molecular mechanics environment, termed the linear vibronic coupling/molecular mechanics (LVC/MM) method, for the surface hopping including arbitrary coupling (SHARC) molecular dynamics package. Electrostatic embedding is realized through the computation of interactions between environment point charges and distributed multipole expansions (DMEs, up to quadrupoles) that represent each electronic state and transition densities in the diabatic basis. The DME parameters are obtained through a restrained electrostatic potential (RESP) fit, which we extended to yield higher-order multipoles. We also implemented in SHARC a scheme for achieving roto-translational invariance of LVC models as well as a general quantum mechanics/molecular mechanics (QM/MM) interface, an OpenMM interface, and restraining potentials for simulating liquid droplets. Using thioformaldehyde in water as a test case, we demonstrate that LVC/MM can accurately reproduce the solvation structure and energetics of rigid solutes, with errors on the order of 1-2 kcal/mol compared to a BP86/MM reference. The implementation in SHARC is shown to be very efficient, enabling the simulation of trajectories on the nanosecond time scale in a matter of days.

Application of molecular dynamic simulations in modeling the excited state behavior of confined molecules

Relative to isotropic organic solvent medium, the structure and conformation of a reactant molecule in an organized and confining medium are often different. In addition, because of the rigidity of the immediate environment, the reacting molecule have a little freedom to undergo large changes even upon gaining energy or modifications in the electronic structure. These alterations give rise to differences in the photochemistry of a molecular and supramolecular species. In this study, one such example is presented. α-Alkyl dibenzylketones upon excitation in isotropic solvents give products via Norrish type I and type II reactions that are independent of the chain length of the alkyl substituent. On the other hand, when these molecules are enclosed within an organic capsule of volume ~ 550 Å3, they give products that are strikingly dependent on the length of the α-alkyl substitution. These previously reported experimental observations are rationalized based on the structures generated by molecular modeling (docking and molecular dynamics (MD) simulations). It is shown that MD simulations that are utilized extensively in biologically important macromolecules can also be useful to understand the excited state behavior of reactive molecules that are part of supramolecular assemblies. These simulations can provide structural information of the reactant molecule and the surroundings complementing that with the one obtained from 1 and 2D NMR experiments. MD simulated structures of seven α-alkyl dibenzylketones encapsulated within the octa acid capsule provide a clear understanding of their unique behavior in this restricted medium. Because of the rigidity of the medium, these structures although generated in the ground state can rationalize the photochemical behavior of the molecules in the excited state.

Identification of Potential JNK3 Inhibitors: A Combined Approach Using Molecular Docking and Deep Learning-Based Virtual Screening

JNK3, a member of the MAPK family, plays a pivotal role in mediating cellular responses to stress signals, with its activation implicated in a myriad of inflammatory conditions. While JNK3 holds promise as a therapeutic target for neurodegenerative disorders such as Huntington's, Parkinson's, and Alzheimer's diseases, there remains a gap in the market for effective JNK3 inhibitors. Despite some pan-JNK inhibitors reaching clinical trials, no JNK-targeted therapies have achieved market approval. To bridge this gap, our study introduces a sophisticated virtual screening approach. We begin with an energy-based screening, subsequently integrating a variety of rescoring techniques. These encompass glide docking scores, MM/GBSA, and artificial scoring mechanisms such as DeepDock and advanced Graph Neural Networks. This virtual screening workflow is designed to evaluate and identify potential small-molecule inhibitors with high binding affinity. We have implemented a virtual screening workflow to identify potential candidate molecules. This process has resulted in the selection of ten molecules. Subsequently, these ten molecules have undergone biological activity evaluation to assess their potential efficacy. Impressively, molecule compound 6 surfaced as the most promising, exhibiting a potent kinase inhibitory activity marked by an IC50 of 130.1 nM and a notable reduction in TNF-α release within macrophages. This suggests that compound 6 could potentially serve as an effective inhibitor for the treatment of neuroinflammation and neurodegenerative diseases. The prospect of further medicinal modifications to optimize compound 6 presents a promising avenue for future research and development in this field. Utilizing binding pose metadynamics coupled with molecular dynamics simulations, we delved into the explicit binding mode of compound 6 to JNK3. Such insights pave the way for refined drug development strategies. Collectively, our results underscore the efficacy of the hybrid virtual screening workflow in the identification of robust JNK3 inhibitors, holding promise for innovative treatments against neuroinflammation and neurodegenerative disorders.

Triazolothiadiazoles and triazolothiadiazines as potent α-glucosidase inhibitors: Mechanistic insights from kinetics studies, molecular docking and dynamics simulations

Diabetes mellitus has been considered as a serious health problem worldwide due its high prevalence rate and associated complications. In this context, the current research work aims at exploring new structural leads for the treatment of a major metabolic disorder, diabetes mellitus type 2. The outcomes of our prior studies on a diverse set of triazolothiadiazole and triazolothiadiazine derivatives and their therapeutic potential, encouraged us to explore their anti-diabetic competency by targeting the key carbohydrate catabolic enzyme, α-glucosidase. Therefore, all these analogues were examined to reveal their contribution towards this severe metabolic issue. Interestingly, all the tested compounds (2a-2l and 3a-3p) exhibited several times more potent α-glucosidase inhibitory activities (IC50 in the range of 2.44-219.93 μM) as compared to marketed drug, acarbose (IC50 = 873.34 ± 1.67 μM). Furthermore, their mechanism of action was investigated through in vitro kinetics studies which revealed compounds 3a, 3d, 3o, and 2k as competitive inhibitors, and 3f as a mixed type inhibitor of α-glucosidase. In addition, in silico molecular docking and molecular dynamics simulations were applied to observe the mode of interaction of the active hits within the binding pocket of α-glucosidase. Both docking and simulation results favored our in vitro mechanistic analysis.

BamA-targeted antimicrobial peptide design for enhanced efficacy and reduced toxicity

The emergence of drug-resistant superbugs has necessitated a pressing need for innovative antibiotics. Antimicrobial peptides (AMPs) have demonstrated broad-spectrum antibacterial activity, reduced susceptibility to resistance, and immunomodulatory effects, rendering them promising for combating drug-resistant microorganisms. This study employed computational simulation methods to screen and design AMPs specifically targeting ESKAPE pathogens. Particularly, AMPs were rationally designed to target the BamA and obtain novel antimicrobial peptide sequences. The designed AMPs were assessed for their antibacterial activities, mechanisms, and stability. Molecular docking and dynamics simulations demonstrated the interaction of both designed AMPs, 11pep and D-11pep, with the β1, β9, β15, and β16 chains of BamA, resulting in misfolding of outer membrane proteins and antibacterial effects. Subsequent antibacterial investigations confirmed the broad-spectrum activity of both 11pep and D-11pep, with D-11pep demonstrating higher potency against resistant Gram-negative bacteria. D-11pep exhibited MICs of 16, 8, and 32 μg/mL against carbapenem-resistant Escherichia coli, carbapenem-resistant Pseudomonas aeruginosa, and multi-drug-resistant Acinetobacter baumannii, respectively, with a concomitant lower resistance induction. Mechanism of action studies confirmed that peptides could disrupt the bacterial outer membrane, aligning with the findings of molecular dynamics simulations. Additionally, D-11pep demonstrated superior stability and reduced toxicity in comparison to 11pep. The findings of this study underscore the efficacy of rational AMP design that targets BamA, along with the utilization of D-amino acid replacements as a strategy for developing AMPs against drug-resistant bacteria.

Orthosteric ligand selectivity and allosteric probe dependence at Hydroxycarboxylic acid receptor HCAR2

Hydroxycarboxylic acid receptor 2 (HCAR2), a member of Class A G-protein-coupled receptor (GPCR) family, plays a pivotal role in anti-lipolytic and anti-inflammatory effects, establishing it as a significant therapeutic target for treating dyslipidemia and inflammatory diseases. However, the mechanism underlying the signaling of HCAR2 induced by various types of ligands remains elusive. In this study, we elucidate the cryo-electron microscopy (cryo-EM) structure of Gi-coupled HCAR2 in complex with a selective agonist, MK-6892, resolved to a resolution of 2.60 Å. Our structural analysis reveals that MK-6892 occupies not only the orthosteric binding pocket (OBP) but also an extended binding pocket (EBP) within HCAR2. Pharmacological assays conducted in this study demonstrate that the OBP is a critical determinant for ligand selectivity among the HCARs subfamily. Moreover, we investigate the pharmacological properties of the allosteric modulator compound 9n, revealing its probe-dependent behavior on HCAR2 in response to varying orthosteric agonists. Collectively, our findings provide invaluable structural insights that contribute to a deeper understanding of the regulatory mechanisms governing HCAR2 signaling transduction mediated by both orthosteric and allosteric ligands.

Unveiling the Selectivity Mechanism of Type-I LRRK2 Inhibitors by Computational Methods: Insights from Binding Thermodynamics and Kinetics Simulation

Understanding the selectivity mechanisms of inhibitors toward highly similar proteins is very important in new drug discovery. Developing highly selective targeting of leucine-rich repeat kinase 2 (LRRK2) kinases for the treatment of Parkinson's disease (PD) is challenging because of the similarity of the kinase ATP binding pocket. During the development of LRRK2 inhibitors, off-target effects on other kinases, especially TTK and JAK2 kinases, have been observed. As a result, significant time and resources have been devoted to improving the selectivity for the LRRK2 target. DNL201 is an LRRK2 kinase inhibitor entering phase I clinical studies. The experiments have shown that DNL201 significantly inhibits LRRK2 kinase activity, with >167-fold selectivity over JAK2 and TTK kinases. However, the potential mechanisms of inhibitor preferential binding to LRRK2 kinase are still not well elucidated. In this work, to reveal the underlying general selectivity mechanism, we carried out several computational methods and comprehensive analyses from both the binding thermodynamics and kinetics on two representative LRRK2 inhibitors (DNL201 and GNE7915) to LRRK2. Our results suggest that the structural and kinetic differences between the proteins may play a key role in determining the activity of the selective small-molecule inhibitor. The selectivity mechanisms proposed in this work could be helpful for the rational design of novel selective LRRK2 kinase inhibitors against PD.

A biophysical framework for double-drugging kinases

Selective orthosteric inhibition of kinases has been challenging due to the conserved active site architecture of kinases and emergence of resistance mutants. Simultaneous inhibition of distant orthosteric and allosteric sites, which we refer to as "double-drugging", has recently been shown to be effective in overcoming drug resistance. However, detailed biophysical characterization of the cooperative nature between orthosteric and allosteric modulators has not been undertaken. Here, we provide a quantitative framework for double-drugging of kinases employing isothermal titration calorimetry, Förster resonance energy transfer, coupled-enzyme assays, and X-ray crystallography. We discern positive and negative cooperativity for Aurora A kinase (AurA) and Abelson kinase (Abl) with different combinations of orthosteric and allosteric modulators. We find that a conformational equilibrium shift is the main principle governing cooperativity. Notably, for both kinases, we find a synergistic decrease of the required orthosteric and allosteric drug dosages when used in combination to inhibit kinase activities to clinically relevant inhibition levels. X-ray crystal structures of the double-drugged kinase complexes reveal the molecular principles underlying the cooperative nature of double-drugging AurA and Abl with orthosteric and allosteric inhibitors. Finally, we observe a fully closed conformation of Abl when bound to a pair of positively cooperative orthosteric and allosteric modulators, shedding light on the puzzling abnormality of previously solved closed Abl structures. Collectively, our data provide mechanistic and structural insights into rational design and evaluation of double-drugging strategies.

Multiscale computational modeling of the effects of 2'-deoxy-ATP on cardiac muscle calcium handling

2'-Deoxy-ATP (dATP), a naturally occurring near analog of ATP, is a well-documented myosin activator that has been shown to increase contractile force, improve pump function, and enhance lusitropy in the heart. Calcium transients in cardiomyocytes with elevated levels of dATP show faster calcium decay compared with cardiomyocytes with basal levels of dATP, but the mechanisms behind this are unknown. Here, we design and utilize a multiscale computational modeling framework to test the hypothesis that dATP acts on the sarcoendoplasmic reticulum calcium-ATPase (SERCA) pump to accelerate calcium re-uptake into the sarcoplasmic reticulum during cardiac relaxation. Gaussian accelerated molecular dynamics simulations of human cardiac SERCA2A in the E1 apo, ATP-bound and dATP-bound states showed that dATP forms more stable contacts in the nucleotide binding pocket of SERCA and leads to increased closure of cytosolic domains. These structural changes ultimately lead to changes in calcium binding, which we assessed using Brownian dynamics simulations. We found that dATP increases calcium association rate constants to SERCA and that dATP binds to apo SERCA more rapidly than ATP. Using a compartmental ordinary differential equation model of human cardiomyocyte excitation-contraction coupling, we found that these increased association rate constants contributed to the accelerated rates of calcium transient decay observed experimentally. This study provides clear mechanistic evidence of enhancements in cardiac SERCA2A pump function due to interactions with dATP.

Structural basis for the ligand recognition and signaling of free fatty acid receptors

Free fatty acid receptors 1-4 (FFA1-4) are class A G protein-coupled receptors (GPCRs). FFA1-3 share substantial sequence similarity whereas FFA4 is unrelated. Despite this FFA1 and FFA4 are activated by the same range of long chain fatty acids (LCFAs) whilst FFA2 and FFA3 are instead activated by short chain fatty acids (SCFAs) generated by the intestinal microbiota. Each of FFA1, 2 and 4 are promising targets for novel drug development in metabolic and inflammatory conditions. To gain insights into the basis of ligand interactions with, and molecular mechanisms underlying activation of, FFAs by LCFAs and SCFAs, we determined the active structures of FFA1 and FFA4 bound to the polyunsaturated LCFA docosahexaenoic acid (DHA), FFA4 bound to the synthetic agonist TUG-891, as well as SCFA butyrate-bound FFA2, each complexed with an engineered heterotrimeric Gq protein (miniGq), by cryo-electron microscopy. Together with computational simulations and mutagenesis studies, we elucidated the similarities and differences in the binding modes of fatty acid ligands with varying chain lengths to their respective GPCRs. Our findings unveil distinct mechanisms of receptor activation and G protein coupling. We anticipate that these outcomes will facilitate structure-based drug development and underpin future research to understand allosteric modulation and biased signaling of this group of GPCRs.

Multilayer Model of Gold Nanoparticles (AuNPs) and Its Application in the Classical Molecular Dynamics Simulation of Citrate-Capped AuNPs

Studies on the interaction between gold nanoparticles (AuNPs) and functional proteins have been useful in developing diagnostic and therapeutic agents. Such studies require a realistic computational model of AuNPs for successful molecular design works. This study offers a new multilayer model of AuNPs to address the inconsistency between its molecular mechanics' interpretation and AuNP's plasmonic nature. We performed partial charge quantum calculation of AuNPs using Au13 and Au55 models. The result showed that it has partial negative charges on the surface and partial positive charges on the inner part, indicating that the AuNP model should be composed of multiatom types. We tested the partial charge parameters of these gold (Au) atoms in classical molecular dynamics simulation (CMD) of AuNPs. The result showed that our parameters performed better in simulating the adsorption of Na+ and dicarboxy acetone in terms of consistency with surface charge density than the zero charges Au in the interface force field (IFF). We proposed that the multiple-charged AuNP model can be developed further into a simpler four-atom type of Au in a larger AuNP size.

Oligo-PROTAC strategy for cell-selective and targeted degradation of activated STAT3

Decoy-oligodeoxynucleotides (D-ODNs) can target undruggable transcription factors, such as STAT3. However, challenges in D-ODN delivery and potency hampered their translation. To overcome these limitations, we conjugated STAT3-specific D-ODN to thalidomide (Tha), a known ligand to cereblon (CRBN, a component of E3 ubiquitin ligase) to generate a proteolysis-targeting chimera (STAT3D PROTAC ). STAT3D PROTAC downregulated STAT3, but not STAT1 or STAT5, in target cells. Computational modeling of the STAT3D PROTAC ternary complex predicted two surface lysines on STAT3, K601 and K626 as potential ubiquitination sites for the PROTAC bound E3 ligase. Accordingly, K601/K626 point mutations in STAT3, as well as proteasome inhibitors, and CRBN deletion alleviated STAT3D PROTAC effect. Next, we conjugated STAT3D PROTAC to a CpG ligand targeting Toll-like receptor 9 (TLR9) to generate myeloid/B-cell-selective C-STAT3D PROTAC conjugate. Naked C-STAT3D PROTAC was spontaneously internalized by TLR9 + myeloid cells, B cells as well as human Ly18 and mouse A20 lymphoma cells, but not by T cells. C-STAT3D PROTAC decreased STAT3 levels to 50% at 250 nM and over 85% at 2 µM dosing in myeloid cells. We also observed significantly improved downregulation of STAT3 target genes involved in lymphoma cell proliferation and/or survival ( BCL2L1, CCND2, MYC ). Finally, we assessed the antitumor efficacy of C-STAT3D PROTAC compared to C-STAT3D or scrambled control (C-SCR) against human lymphoma xenotransplants. Local C-STAT3D PROTAC administration triggered lymphoma regression while control treatments had limited effects. Our results underscore feasibility of using PROTAC strategy for cell-selective, decoy oligonucleotide-based targeting of STAT3 and potentially other tumorigenic transcription factors for cancer therapy.

Cycloartenyl ferulate improves natural killer (NK) cell immunity against cancer by binding to IFNγ receptor 1

Cycloartenyl ferulate (CF) is abundant in brown rice with multiple biologic functions. It has been reported to possess antitumor activity; however, the related mechanism of action of CF has not been clarified. Herein, we unexpectedly uncover the immunological regulation effects of CF and its molecular mechanism. We discovered that CF directly enhanced the killing capacity of natural killer (NK) cells for various cancer cells in vitro. In vivo, CF also improved cancer surveillance in mouse models of lymphoma clearance and metastatic melanoma dependent on NK cells. In addition, CF promoted anticancer efficacy of the anti-PD1 antibody with improvement of tumor immune microenvironment. Mechanistically, we first unveiled that CF acted on the canonical JAK1/2-STAT1 signaling pathway to enhance the immunity of the NK cells by selectively binding to interferon γ receptor 1. Collectively, our results indicate that CF is a promising immunoregulation agent worthy of attention in clinical application in the future. Due to broad biological significance of interferon γ, our findings also provide a capability to understand the diverse functions of CF.

Development of a Systematic and Extensible Force Field for Peptoids (STEPs)

Peptoids (N-substituted glycines) are a class of biomimetic polymers that have attracted significant attention due to their accessible synthesis and enzymatic and thermal stability relative to their naturally occurring counterparts (polypeptides). While these polymers provide the promise of more robust functional materials via hierarchical approaches, they present a new challenge for computational structure prediction for material design. The reliability of calculations hinges on the accuracy of interactions represented in the force field used to model peptoids. For proteins, structure prediction based on sequence and de novo design has made dramatic progress in recent years; however, these models are not readily transferable for peptoids. Current efforts to develop and implement peptoid-specific force fields are spread out, leading to replicated efforts and a fragmented collection of parameterized sidechains. Here, we developed a peptoid-specific force field containing 70 different side chains, using GAFF2 as starting point. The new model is validated based on the generation of Ramachandran-like plots from DFT optimization compared against force field reproduced potential energy and free energy surfaces as well as the reproduction of equilibrium cis/trans values for some residues experimentally known to form helical structures. Equilibrium cis/trans distributions (Kct) are estimated for all parameterized residues to identify which residues have an intrinsic propensity for cis or trans states in the monomeric state.

Solvatomorphism and first-time observation of acid-acid catemer in 4-phenylamino-benzoic acids

To investigate the polymorphism in 4-phenylamino-benzoic acids (4-PABAs) in general, and the effect on the polymorphism of these compounds exerted by substitution in particular, a series of 4-PABAs (1-8) varying in the substitution position and pattern were synthesized, and their polymorphic behavior was investigated for the first time. A relatively comprehensive polymorph screening led to the discovery of two forms, one solvent-free and the other solvate, for compounds 1, 3 and 8, and one form for the other compounds. The crystal structures were determined by single-crystal XRD. All the 4-PABAs in the crystal structures are highly twisted, and all the solvent-free crystals are based on the conventional acid-acid dimer motif, except for 2, which has a rarely observed acid-acid catemer motif. Two of the solvates (1-S and 8-S) have pyridine in the lattice while the other (3-S) has dichloromethane. The observation indicates that neither conformational flexibility or substitution alone nor the combination of both leads to polymorphism in these compounds, which is in dramatic contrast to the polymorphism of fenamic acids. The thermal properties of each system were investigated by differential scanning calorimetry and desolvation of the solvates was studied by thermogravimetric analysis. Hirshfeld surface analysis and molecular dynamics simulation were performed to study the mechanism of polymorphism and the intermolecular interactions contributing to the formation and stability of each crystal form.

AQDnet: Deep Neural Network for Protein-Ligand Docking Simulation

We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training dataset by generating thousands of diverse ligand configurations for each protein-ligand complex and subsequently determining the binding energy of each configuration through quantum computation. Second, we have devised a method that incorporates the atom-centered symmetry function (ACSF), highly effective in describing molecular energies, for the prediction of protein-ligand interactions. These advancements have enabled us to effectively train a neural network to learn the protein-ligand quantum energy landscape (P-L QEL). Consequently, we have achieved a 92.6% top 1 success rate in the CASF-2016 docking power, placing first among all models assessed in the CASF-2016, thus demonstrating the exceptional docking performance of our model.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

The role of loop dynamics in the prediction of ligand-protein binding enthalpy

The enthalpic and entropic components of ligand-protein binding free energy reflect the interactions and dynamics between ligand and protein. Despite decades of study, our understanding and hence our ability to predict these individual components remains poor. In recent years, there has been substantial effort and success in the prediction of relative and absolute binding free energies, but the prediction of the enthalpic (and entropic) contributions in biomolecular systems remains challenging. Indeed, it is not even clear what kind of performance in terms of accuracy could currently be obtained for such systems. It is, however, relatively straight-forward to compute the enthalpy of binding. We thus evaluated the performance of absolute enthalpy of binding calculations using molecular dynamics simulation for ten inhibitors against a member of the bromodomain family, BRD4-1, against isothermal titration calorimetry data. Initial calculations, with the AMBER force-field showed good agreement with experiment (R² = 0.60) and surprisingly good accuracy with an average of root-mean-square error (RMSE) = 2.49 kcal mol^-1. Of the ten predictions, three were obvious outliers that were all over-predicted compared to experiment. Analysis of various simulation factors, including parameterization, buffer concentration and conformational dynamics, revealed that the behaviour of a loop (the ZA loop on the periphery of the binding site) strongly dictates the enthalpic prediction. Consistent with previous observations, the loop exists in two distinct conformational states and by considering one or the other or both states, the prediction for the three outliers can be improved dramatically to the point where the R² = 0.95 and the accuracy in terms of RMSE improves to 0.90 kcal mol^-1. However, performance across force-fields is not consistent: if OPLS and CHARMM are used, different outliers are observed and the correlation with the ZA loop behaviour is not recapitulated, likely reflecting parameterization as a confounding problem. The results provide a benchmark standard for future study and comparison.

The Effect of Ethanol on Lipid Nanoparticle Stabilization from a Molecular Dynamics Simulation Perspective

Lipid nanoparticles (LNPs) have emerged as a promising delivery system, particularly for genetic therapies and vaccines. LNP formation requires a specific mixture of nucleic acid in a buffered solution and lipid components in ethanol. Ethanol acts as a lipid solvent, aiding the formation of the nanoparticle's core, but its presence can also affect LNP stability. In this study, we used molecular dynamics (MD) simulations to investigate the physicochemical effect of ethanol on LNPs and gain a dynamic understanding of its impact on the overall structure and stability of LNPs. Our results demonstrate that ethanol destabilizes LNP structure over time, indicated by increased root mean square deviation (RMSD) values. Changes in the solvent-accessible surface area (SASA), electron density, and radial distribution function (RDF) also suggest that ethanol affects LNP stability. Furthermore, our H-bond profile analysis shows that ethanol penetrates the LNP earlier than water. These findings emphasize the importance of immediate ethanol removal in lipid-based systems during LNP production to ensure stability.

Binding Mechanism of Inhibitors to Heat Shock Protein 90 Investigated by Multiple Independent Molecular Dynamics Simulations and Prediction of Binding Free Energy

The heat shock protein (HSP90) has been an import target of drug design in the treatment of human disease. An exploration of the conformational changes in HSP90 can provide useful information for the development of efficient inhibitors targeting HSP90. In this work, multiple independent all-atom molecular dynamics (AAMD) simulations followed by calculations of the molecular mechanics generalized Born surface area (MM-GBSA) were performed to explore the binding mechanism of three inhibitors (W8Y, W8V, and W8S) to HSP90. The dynamics analyses verified that the presence of inhibitors impacts the structural flexibility, correlated movements, and dynamics behavior of HSP90. The results of the MM-GBSA calculations suggest that the selection of GB models and empirical parameters has important influences on the predicted results and verify that van der Waals interactions are the main forces that determine inhibitor-HSP90 binding. The contributions of separate residues to the inhibitor-HSP90 binding process indicate that hydrogen-bonding interactions (HBIs) and hydrophobic interactions play important roles in HSP90-inhibitor identifications. Moreover, residues L34, N37, D40, A41, D79, I82, G83, M84, F124, and T171 are recognized as hot spots of inhibitor-HSP90 binding and provide significant target sites of for the design of drugs related to HSP90. This study aims to contribute to the development of efficient inhibitors that target HSP90 by providing an energy-based and theoretical foundation.

Identification Mechanism of BACE1 on Inhibitors Probed by Using Multiple Separate Molecular Dynamics Simulations and Comparative Calculations of Binding Free Energies

β-amyloid cleaving enzyme 1 (BACE1) is regarded as an important target of drug design toward the treatment of Alzheimer's disease (AD). In this study, three separate molecular dynamics (MD) simulations and calculations of binding free energies were carried out to comparatively determine the identification mechanism of BACE1 for three inhibitors, 60W, 954 and 60X. The analyses of MD trajectories indicated that the presence of three inhibitors influences the structural stability, flexibility and internal dynamics of BACE1. Binding free energies calculated by using solvated interaction energy (SIE) and molecular mechanics generalized Born surface area (MM-GBSA) methods reveal that the hydrophobic interactions provide decisive forces for inhibitor-BACE1 binding. The calculations of residue-based free energy decomposition suggest that the sidechains of residues L91, D93, S96, V130, Q134, W137, F169 and I179 play key roles in inhibitor-BACE1 binding, which provides a direction for future drug design toward the treatment of AD.

Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

Integrative Approach to Dissect the Drug Resistance Mechanism of the H172Y Mutation of SARS-CoV-2 Main Protease

Nirmatrelvir is an orally available inhibitor of SARS-CoV-2 main protease (Mpro) and the main ingredient of Paxlovid, a drug approved by the U.S. Food and Drug Administration for high-risk COVID-19 patients. Recently, a rare natural mutation, H172Y, was found to significantly reduce nirmatrelvir's inhibitory activity. As the COVID-19 cases skyrocket in China and the selective pressure of antiviral therapy builds in the US, there is an urgent need to characterize and understand how the H172Y mutation confers drug resistance. Here, we investigated the H172Y Mpro's conformational dynamics, folding stability, catalytic efficiency, and inhibitory activity using all-atom constant pH and fixed-charge molecular dynamics simulations, alchemical and empirical free energy calculations, artificial neural networks, and biochemical experiments. Our data suggest that the mutation significantly weakens the S1 pocket interactions with the N-terminus and perturbs the conformation of the oxyanion loop, leading to a decrease in the thermal stability and catalytic efficiency. Importantly, the perturbed S1 pocket dynamics weaken the nirmatrelvir binding in the P1 position, which explains the decreased inhibitory activity of nirmatrelvir. Our work demonstrates the predictive power of the combined simulation and artificial intelligence approaches, and together with biochemical experiments, they can be used to actively surveil continually emerging mutations of SARS-CoV-2 Mpro and assist the optimization of antiviral drugs. The presented approach, in general, can be applied to characterize mutation effects on any protein drug targets.