Molecular Dynamics Simulation Study of the Selective Inhibition of Coagulation Factor IXa over Factor Xa.

Thromboembolic disorders, arising from abnormal coagulation, pose a significant risk to human life in the modern world. The FDA has recently approved several anticoagulant drugs targeting factor Xa (FXa) to manage these disorders. However, these drugs have potential side effects, leading to bleeding complications in patients. To mitigate these risks, coagulation factor IXa (FIXa) has emerged as a promising target due to its selective regulation of the intrinsic pathway. Due to the high structural and functional similarities of these coagulation factors and their inhibitor binding modes, designing a selective inhibitor specifically targeting FIXa remains a challenging task. The dynamic behavior of protein-ligand interactions and their impact on selectivity were analyzed using molecular dynamics simulation, considering the availability of potent and selective compounds for both coagulation factors and the co-crystal structures of protein-ligand complexes. Throughout the simulations, we examined ligand movements in the binding site, as well as the contact frequencies and interaction fingerprints, to gain insights into selectivity. Interaction fingerprint (IFP) analysis clearly highlights the crucial role of strong H-bond formation between the ligand and D189 and A190 in the S1 subsite for FIXa selectivity, consistent with our previous study. This dynamic analysis also reveals additional FIXa-specific interactions. Additionally, the absence of polar interactions contributes to the selectivity for FXa, as observed from the dynamic profile of interactions. A contact frequency analysis of the protein-ligand complexes provides further confirmation of the selectivity criteria for FIXa and FXa, as well as criteria for binding and activity. Moreover, a ligand movement analysis reveals key interaction dynamics that highlight the tighter binding of selective ligands to the proteins compared to non-selective and inactive ligands.

Rational Prediction of PROTAC-Compatible Protein-Protein Interfaces by Molecular Docking.

Proteolysis targeting chimeras (PROTACs) are heterobifunctional ligands that mediate the interaction between a protein target and an E3 ligase, resulting in a ternary complex, whose interaction with the ubiquitination machinery leads to target degradation. This technology is emerging as an exciting new avenue for therapeutic development, with several PROTACs currently undergoing clinical trials targeting cancer. Here, we describe a general and computationally efficient methodology combining restraint-based docking, energy-based rescoring, and a filter based on the minimal solvent-accessible surface distance to produce PROTAC-compatible PPIs suitable for when there is no a priori known PROTAC ligand. In a benchmark employing a manually curated data set of 13 ternary complex crystals, we achieved an accuracy of 92% when starting from bound structures and 77% when starting from unbound structures, respectively. Our method only requires that the ligand-bound structures of the monomeric forms of the E3 ligase and target proteins be given to run, making it general, accurate, and highly efficient, with the ability to impact early-stage PROTAC-based drug design campaigns where no structural information about the ternary complex structure is available.

Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models.

RNA structure is conformationally dynamic, and accurate all-atom tertiary (3D) structure modeling of RNA remains challenging with the prevailing tools. Secondary structure (2D) information is the standard prerequisite for most RNA 3D modeling. Despite several 2D and 3D structure prediction tools proposed in recent years, one of the challenges is to choose the best combination for accurate RNA 3D structure prediction. Here, we benchmarked seven small RNA PDB structures (40 to 90 nucleotides) with different topologies to understand the effects of different 2D structure predictions on the accuracy of 3D modeling. The current study explores the blind challenge of 2D to 3D conversions and highlights the performances of de novo RNA 3D modeling from their predicted 2D structure constraints. Our results show that conformational sampling-based methods such as SimRNA and IsRNA1 depend less on 2D accuracy, whereas motif-based methods account for 2D evidence. Our observations illustrate the disparities in available 3D and 2D prediction methods and may further offer insights into developing topology-specific or family-specific RNA structure prediction pipelines.

Quantifying Functional-Group-like Structural Fragments in Molecules and Its Applications in Drug Design.

A functional group in a molecule is a structural fragment consisting of a few atoms or a single atom that imparts reactivity to a molecule. Hence, defining functional groups is crucial in chemistry to predict the properties and reactivities of molecules. However, there is no established method in the literature for defining functional groups based on reactivity parameters. In this work, we addressed this issue by designing a set of predefined structural fragments along with reactivity parameters like electron conjugation and ring strain. This approach uses bond orders and atom connectivities to quantify the presence of these fragments within an organic molecule based on a given input molecular coordinate. To assess the effectiveness of this approach, we performed a case study to show the benefits of using these newly designed structural fragments instead of traditional fingerprint-based methods for grouping potential COX1/COX2 inhibitors by screening an approved drug library against aspirin molecule. The structural fragment-based model for ternary classification of rat oral LD50 of chemicals showed performance similar to the fingerprint-based models. In evaluating the regression model performance for aqueous solubility, log(S), predictions, our approach outperformed the fingerprint-based model.

Band Alignment Enabling Effective Charge Transfer for the Highly Enhanced Raman Scattering and Fluorescence of Metal-Nanoparticle-Decorated Conjugated Polymer Nanowires.

The charge transfer (CT) process has attracted much attention due to its contribution to the improvement of spectroscopic phenomena such as Raman scattering and fluorescence. A current challenge is understanding what factors can influence CT. Here, it is demonstrated that the enhancement factor (EF) of CT (∼2000) can reach the level of electromagnetic enhancement (∼1680) when resonant CT is carried out by (Fermi level energy) band alignment between a metal nanoparticle (NP) and conjugated polymer (polypyrrole (PPy)) nanowire (NW). This band alignment results in an on- or off-resonant CT. As a proof of concept for CT based surface enhanced Raman scattering (SERS) template, the Ag NPs-decorated PPy NW is utilized to effectively enhance the Raman signal of rhodamine 6G (EF of 5.7 × 105). Hence, by means of our demonstration, it is proposed that controlling the band alignment should be considered an important parameter for obtaining a large EF of spectroscopic phenomena.

PCW-A1001, AI-assisted de novo design approach to design a selective inhibitor for FLT-3(D835Y) in acute myeloid leukemia.

Treating acute myeloid leukemia (AML) by targeting FMS-like tyrosine kinase 3 (FLT-3) is considered an effective treatment strategy. By using AI-assisted hit optimization, we discovered a novel and highly selective compound with desired drug-like properties with which to target the FLT-3 (D835Y) mutant. In the current study, we applied an AI-assisted de novo design approach to identify a novel inhibitor of FLT-3 (D835Y). A recurrent neural network containing long short-term memory cells (LSTM) was implemented to generate potential candidates related to our in-house hit compound (PCW-1001). Approximately 10,416 hits were generated from 20 epochs, and the generated hits were further filtered using various toxicity and synthetic feasibility filters. Based on the docking and free energy ranking, the top compound was selected for synthesis and screening. Of these three compounds, PCW-A1001 proved to be highly selective for the FLT-3 (D835Y) mutant, with an IC50 of 764 nM, whereas the IC50 of FLT-3 WT was 2.54 µM.

Quantum-based machine learning and AI models to generate force field parameters for drug-like small molecules.

Force fields for drug-like small molecules play an essential role in molecular dynamics simulations and binding free energy calculations. In particular, the accurate generation of partial charges on small molecules is critical to understanding the interactions between proteins and drug-like molecules. However, it is a time-consuming process. Thus, we generated a force field for small molecules and employed a machine learning (ML) model to rapidly predict partial charges on molecules in less than a minute of time. We performed density functional theory (DFT) calculation for 31770 small molecules that covered the chemical space of drug-like molecules. The partial charges for the atoms in a molecule were predicted using an ML model trained on DFT-based atomic charges. The predicted values were comparable to the charges obtained from DFT calculations. The ML model showed high accuracy in the prediction of atomic charges for external test data sets. We also developed neural network (NN) models to assign atom types, phase angles and periodicities. All the models performed with high accuracy on test data sets. Our code calculated all the descriptors that were needed for the prediction of force field parameters and produced topologies for small molecules by combining results from ML and NN models. To assess the accuracy of the predicted force field parameters, we calculated solvation free energies for small molecules, and the results were in close agreement with experimental free energies. The AI-generated force field was effective in the fast and accurate generation of partial charges and other force field parameters for small drug-like molecules.

Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics of ligands targeting G-protein-coupled receptors.

Recently, academic and industrial scientific communities involved in kinetics-based drug development have become immensely interested in predicting the drug target residence time. Screening drug candidates in terms of their computationally predicted residence times, which is a measure of drug efficacy in vivo, and simultaneously assessing computational binding affinities are becoming inevitable. Non-equilibrium molecular simulation approaches are proven to be useful in this purpose. Here, we have implemented an optimized approach of combining the data derived from steered molecular dynamics simulations and the Bell-Evans model to predict the absolute residence times of the antagonist ZMA241385 and agonist NECA that target the A2A adenosine receptor of the G-protein-coupled receptor (GPCR) protein family. We have predicted the absolute ligand residence times on the timescale of seconds. However, our predictions were many folds shorter than those determined experimentally. Additionally, we calculated the thermodynamics of ligand binding in terms of ligand binding energies and the per-residue contribution of the receptor. Subsequently, binding pocket hotspot residues that would be important for further computational mutagenesis studies were identified. In the experiment, similar sets of residues were found to be in significant contact with both ligands under study. Our results build a strong foundation for further improvement of our approach by rationalizing the kinetics of ligand unbinding with the thermodynamics of ligand binding.

QM/MM Simulations for the Broken-Symmetry Catalytic Reaction Mechanism of Human Arginase I.

Human arginase I (HARGI) is a metalloprotein highly expressed in the liver cytosol and catalyzes the hydrolysis of l-arginine to form l-ornithine and urea. Understanding the reaction mechanism would be highly helpful to design new inhibitor molecules for HARGI as it is a target for heart- and blood-related diseases. In this study, we explored the hydrolysis reaction mechanism of HARGI with antiferromagnetic and ferromagnetic coupling between two Mn(II) ions at the catalytic site by employing molecular dynamics simulations coupled with quantum mechanics and molecular mechanics (QM/MM). The spin states, high-spin ferromagnetic couple (S Mn1 = 5/2, S Mn2 = 5/2), low-spin ferromagnetic couple (S Mn1 = 1/2, S Mn2 = 1/2), high-spin antiferromagnetic couple (S Mn1 = 5/2, S Mn2 = -5/2), and low-spin antiferromagnetic couple (S Mn1 = 1/2, S Mn2 = -1/2) are considered, and the calculated energetics for the complex of the substrate and HARGI are compared. The results show that the high-spin antiferromagnetic couple (S Mn1 = 5/2, S Mn2 = -5/2) is more stable than other spin states. The low-spin ferromagnetic and antiferromagnetic coupled states are highly unstable compared with the corresponding high-spin states. The high-spin antiferromagnetic couple (S Mn1 = 5/2, S Mn2 = -5/2) is stabilized by 0.39 kcal/mol compared with the ferromagnetic couple (S Mn1 = 5/2, S Mn2 = 5/2). The reaction mechanism is independent of spin states; however, the energetics of transition states and intermediates are more stable in the case of the high-spin antiferromagnetic couple (S Mn1 = 5/2, S Mn2 = -5/2) than the corresponding ferromagnetic state. It is evident that the calculated coupling constants are higher for antiferromagnetic states and, interestingly, superexchange coupling is found to occur between Mn(II) ions via hydroxide ions in a reactant. The hydroxide ion enhances the coupling interaction and initiates the catalytic reaction. It is also noted that the first intermediate structure where there is no superexchange coupling is similar to the known inhibitor 2(S)-amino-6-boronohexanoic acid.

PCW-1001, a Novel Pyrazole Derivative, Exerts Antitumor and Radio-Sensitizing Activities in Breast Cancer.

As pyrazole and its derivatives have a wide range of biological activities, including anticancer activity, the design of novel pyrazole derivatives has emerged as an important research field. This study describes a novel pyrazole derivative that exerts antitumor and radiosensitizing activities in breast cancer both in vitro and in vivo. We synthesized a novel pyrazole compound N,N-dimethyl-N'-(3-(1-(4-(trifluoromethyl)phenyl)-1H-pyrazol-4-yl)phenyl)azanesulfonamide (PCW-1001) and showed that it inhibited several oncogenic properties of breast cancer both in vitro and in vivo. PCW-1001 induced apoptosis in several breast cancer cell lines. Transcriptome analysis of PCW-1001-treated cells showed that it regulates genes involved in the DNA damage response, suggesting its potential use in radiotherapy. Indeed, PCW-1001 enhanced the radiation sensitivity of breast cancer cells by modulating the expression of DNA damage response genes. Therefore, our data describe a novel pyrazole compound, PCW-1001, with antitumor and radiosensitizer activities in breast cancer.

Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy Estimation.

Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of research, along with the complexity of the algorithm, time-consuming procedures, and reproducibility issues. In this review, we have discussed the advantages and disadvantages of diverse free energy methods like Thermodynamic Integration (TI), Bennett's Acceptance Ratio (BAR), Free Energy Perturbation (FEP), and alchemical methods. Moreover, we discussed the possible application of the machine learning method in proteinligand binding free energy estimation.

Aryl hydrocarbon receptor-targeted therapy for CD4+ T cell-mediated idiopathic pneumonia syndrome in mice.

We previously demonstrated that interferon γ (IFN-γ) derived from donor T cells co-opts the indoleamine 2,3-dioxygenase 1 (IDO1) → aryl hydrocarbon receptor (AHR) axis to suppress idiopathic pneumonia syndrome (IPS). Here we report that the dysregulated expression of AP-1 family genes in Ahr-/- lung epithelial cells exacerbated IPS in allogeneic bone marrow transplantation settings. AHR repressed transcription of Jund by preventing STAT1 from binding to its promoter. As a consequence, decreased interleukin-6 impaired the differentiation of CD4+ T cells toward Th17 cells. IFN-γ- and IDO1-independent induction of Ahr expression indicated that the AHR agonist might be a better therapeutic target for IPS than the IDO1 activator. We developed a novel synthetic AHR agonist (referred to here as PB502) that potently inhibits Jund expression. PB502 was highly effective at inducing AHR activation and ameliorating IPS. Notably, PB502 was by far superior to the endogenous AHR ligand, L-kynurenine, in promoting the differentiation of both mouse and human FoxP3+ regulatory CD4+ T cells. Our results suggest that the IDO1-AHR axis in lung epithelial cells is associated with IPS repression. A specific AHR agonist may exhibit therapeutic activity against inflammatory and autoimmune diseases by promoting regulatory T-cell differentiation.

Towards design of drugs and delivery systems with the Martini coarse-grained model.

Coarse-grained (CG) modelling with the Martini force field has come of age. By combining a variety of bead types and sizes with a new mapping approach, the newest version of the model is able to accurately simulate large biomolecular complexes at millisecond timescales. In this perspective, we discuss possible applications of the Martini 3 model in drug discovery and development pipelines and highlight areas for future development. Owing to its high simulation efficiency and extended chemical space, Martini 3 has great potential in the area of drug design and delivery. However, several aspects of the model should be improved before Martini 3 CG simulations can be routinely employed in academic and industrial settings. These include the development of automatic parameterisation protocols for a variety of molecule types, the improvement of backmapping procedures, the description of protein flexibility and the development of methodologies enabling efficient sampling. We illustrate our view with examples on key areas where Martini could give important contributions such as drugs targeting membrane proteins, cryptic pockets and protein-protein interactions and the development of soft drug delivery systems.

A Structure Based Study of Selective Inhibition of Factor IXa over Factor Xa.

Blood coagulation is an essential physiological process for hemostasis; however, abnormal coagulation can lead to various potentially fatal disorders, generally known as thromboembolic disorders, which are a major cause of mortality in the modern world. Recently, the FDA has approved several anticoagulant drugs for Factor Xa (FXa) which work via the common pathway of the coagulation cascade. A main side effect of these drugs is the potential risk for bleeding in patients. Coagulation Factor IXa (FIXa) has recently emerged as the strategic target to ease these risks as it selectively regulates the intrinsic pathway. These aforementioned coagulation factors are highly similar in structure, functional architecture, and inhibitor binding mode. Therefore, it remains a challenge to design a selective inhibitor which may affect only FIXa. With the availability of a number of X-ray co-crystal structures of these two coagulation factors as protein-ligand complexes, structural alignment, molecular docking, and pharmacophore modeling were employed to derive the relevant criteria for selective inhibition of FIXa over FXa. In this study, six ligands (three potent, two selective, and one inactive) were selected for FIXa inhibition and six potent ligands (four FDA approved drugs) were considered for FXa. The pharmacophore hypotheses provide the distribution patterns for the principal interactions that take place in the binding site. None of the pharmacophoric patterns of the FXa inhibitors matched with any of the patterns of FIXa inhibitors. Based on pharmacophore analysis, a selectivity of a ligand for FIXa over FXa may be defined quantitatively as a docking score of lower than -8.0 kcal/mol in the FIXa-grids and higher than -7.5 kcal/mol in the FXa-grids.

Computational Study on Selective PDE9 Inhibitors on PDE9-Mg/Mg, PDE9-Zn/Mg, and PDE9-Zn/Zn Systems.

PDE9 inhibitors have been studied to validate their potential to treat diabetes, neurodegenerative disorders, cardiovascular diseases, and erectile dysfunction. In this report, we have selected highly potent previously reported selective PDE9 inhibitors BAY73-6691R, BAY73-6691S, 28r, 28s, 3r, 3s, PF-0447943, PF-4181366, and 4r to elucidate the differences in their interaction patterns in the presence of different metal systems such as Zn/Mg, Mg/Mg, and Zn/Zn. The initial complexes were generated by molecular docking followed by molecular dynamics simulation for 100 ns in triplicate for each system to understand the interactions' stability. The results were carefully analyzed, focusing on the ligands' non-bonded interactions with PDE9 in different metal systems.

Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations.

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.

Lead Acetate Assisted Interface Engineering for Highly Efficient and Stable Perovskite Solar Cells.

High power conversion efficiency (PCE) and long-term stability are inevitable issues faced in practical device applications of perovskite solar cells. In this paper, significant enhancements in the device efficiency and stability are achieved by using a surface-active lead acetate (Pb(OAc)2) at the top or bottom of CH3NH3PbI3 (MAPbI3)-based perovskite. When a saturated Pb(OAc)2 solution is introduced on the top of the MAPbI3 perovskite precursor, the OAc- in Pb(OAc)2 participates in lattice restructuring of MAPbI3 to form MAPbI3-x(OAc)x, thereby producing a high-quality perovskite film with high crystallinity, large grain sizes, and uniform and pinhole-free morphology. Moreover, when Pb(OAc)2 solution is mixed in the poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT-PSS) solution in the bottom way, the OAc- in Pb(OAc)2 improves the water resistance of PEDOT-PSS. As the OAc- easily bonds with the Pb2+, the deposition of MAPbI3 precursor onto the Pb(OAc)2 mixed with PEDOT-PSS results in a reduction of the uncoordinated Pb, leading to strong stabilization of the perovskite layer. Both the top- and bottom-treated devices exhibit enhanced PCE values of 18.93% and 18.28%, respectively, compared to the conventional device with a PCE of 16.47%, which originates from decreased trap sites and reduced energy barriers. In particular, the bottom-treated device exhibits long-term stability, with more than 84% of its initial PCE over 800 h in an ambient environment.

Eckol as a Potential Therapeutic against Neurodegenerative Diseases Targeting Dopamine D3/D4 Receptors.

The G protein-coupled receptor (GPCR) family of proteins comprises signaling proteins that mediate cellular responses to various hormones and neurotransmitters, and serves as a prime target for drug discovery. Towards our goal of discovering secondary metabolites from natural sources that can function as neuronal drugs, we evaluated the modulatory effect of eckol on various GPCRs via cell-based functional assays. In addition, we conducted in silico predictions to obtain molecular insights into the functional effects of eckol. Functional assays revealed that eckol had a concentration-dependent agonist effect on dopamine D3 and D4 receptors. The half maximal effective concentration (EC50) of eckol for the dopamine D3 and D4 receptors was 48.62 ± 3.21 and 42.55 ± 2.54 µM, respectively, while the EC50 values of dopamine as a reference agonist for these two receptors were 2.9 and 3.3 nM, respectively. In silico studies revealed that a low binding energy in addition to hydrophilic, hydrophobic, π⁻alkyl, and π⁻π T-shaped interactions are potential mechanisms by which eckol binds to the dopamine receptors to exert its agonist effects. Molecular dynamics (MD) simulation revealed that Phe346 of the dopamine receptors is important for binding of eckol, similar to eticlopride and dopamine. Our results collectively suggest that eckol is a potential D3/D4 agonist for the management of neurodegenerative diseases, such as Parkinson's disease.

Differentiation of selectively labeled peptides using solid-state nanopores.

Determination of the amino acid sequence of a protein is critical for understanding various biological processes. Mass spectrometry has mainly been used for protein identification; however, there are limitations to its sensitivity when detecting low abundance proteins. In this study, we attempted to distinguish between three similar peptide sequences (∼40 amino acids, ∼5 kDa) that differed only by the location or number of cysteine residues with solid-state nanopores. The cysteine residues are located at one end, one at the center, and at both ends for each of the three peptides. We found that differentiation of the three types of peptides by nanopore signals was difficult. However, when the cysteine residue was labeled with a negatively charged molecule, Flamma® 496, the labeled peptides showed distinct signals for each peptide. Comparing the relative current blockades of labeled peptides with applied voltages, we found that the label was able to change peptide conformations and the resulting ionic current signals from the three labeled peptides were distinguished based on the relative current blockade, full width at half-maximum of the current blockade distribution, and single-molecule level peak shape analysis. Our results suggest that solid-state nanopores combined with a targeted labeling strategy could be used to obtain characteristic peptide signatures that could ultimately be used for protein identification.

Comparative Analysis of TM and Cytoplasmic ß-barrel Conformations Using Joint Descriptor.

Macroscopic descriptors have become valuable as coarse-grained features of complex proteins and are complementary to microscopic descriptors. Proteins macroscopic geometric features provide effective clues in the quantification of distant similarity and close dissimilarity searches for structural comparisons. In this study, we performed a systematic comparison of ß-barrels, one of the important classes of protein folds in various transmembrane (TM) proteins against cytoplasmic barrels to estimate the conformational features using a joint-based descriptor. The approach uses joint coordinates and dihedral angles (ß and γ) based on the ß-strand joints and loops to determine the arrangements and propensities at the local and global levels. We then confirmed that there is a clear preference in the overall ß and γ distribution, arrangements of ß-strands and loops, signature patterns, and the number of strand effects between TM and cytoplasmic ß-barrel geometries. As a robust and simple approach, we determine that the joint-based descriptor could provide a reliable static structural comparison aimed at macroscopic level between complex protein conformations.