Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy

Identification and characterization of umami-ACE inhibitory peptides from traditional fermented soybean curds

Fermented soybean curds (FSC) are popular because of its umami taste. Its bioactivities are of interest. Peptides in FSC were identified using nano-HPLC-MS/MS, and 11 candidate peptides showing potential umami and ACE inhibitory activities were screened using various databases. Pharmacophore model analysis showed their high probability of ACE inhibition with fit values >2, which showed the peptides bound to umami receptors and ACE mainly through hydrogen bond, and electrostatic and hydrophobic interactions. Additionally, their docking and interaction energy were independent of the peptide length. Three high umami-ACE inhibitory peptides (VE, FEF, and WEEF) were synthesized. Their umami thresholds were WEEF (0.32 mM) < FEF (0.55 mM) < VE (1.10 mM), while their IC50 were WEEF (85 ± 2 μM) < FEF (170 ± 10 μM) < VE (205 ± 5 μM). NO and ET-1 production were dose-dependent with WEEF showing the best ACE inhibitory activity. The results allowed identification of effective umami agents and ACE inhibitory peptides from fermented soybean products. It could also be useful method for screening potential umami-ACE inhibitory peptides.

DSDPFlex: Flexible-Receptor Docking with GPU Acceleration

Molecular docking is an essential tool in structure-based drug discovery, widely utilized to model ligand-protein interactions and enrich potential hits. Among the different docking strategies, semiflexible docking (rigid-receptor and flexible-ligand model) is the most popular, benefiting from its balance of docking accuracy and speed. However, this approach ignores the conformational changes of proteins and hence demands suitable protein conformations as input. When the binding interaction adheres to an induced-fit model, flexible methods such as molecular dynamics simulation can be utilized, but they are computationally demanding. To balance between speed and accuracy, the flexible docking approach is an effective choice, as exemplified by AutoDock Vina and AutoDockFR, which treat selected protein side chains as flexible parts. However, the efficiency of flexible docking methods is yet to be improved for virtual screening usage. In this article, we introduce DSDPFlex, an improved flexible-receptor docking method accelerated by GPU parallelization. Beyond acceleration, optimizations with respect to sampling, scoring, and search space are implemented in DSDPFlex to further improve its capability in flexible tasks. In cross-docking evaluation, DSDPFlex demonstrates superior accuracy compared to AutoDock Vina and is 100 times faster than Vina in flexible-receptor tasks. We also show the advantage of flexible-receptor methods on suboptimal pockets and validate the advantage of DSDPFlex in screening on apo and AlphaFold2-predicted structures. With improvements in both efficiency and accuracy, DSDPFlex is expected to hold potential in future docking-based studies.

PbsNRs: predict the potential binders and scaffolds for nuclear receptors

Nuclear receptors (NRs) are a class of essential proteins that regulate the expression of specific genes and are associated with multiple diseases. In silico methods for prescreening potential NR binders with predictive binding ability are highly desired for NR-related drug development but are rarely reported. Here, we present the PbsNRs (Predicting binders and scaffolds for Nuclear Receptors), a user-friendly web server designed to predict the potential NR binders and scaffolds through proteochemometric modeling. The utility of PbsNRs was systemically evaluated using both chemical compounds and natural products. Results indicated that PbsNRs achieved a good prediction performance for chemical compounds on internal (ROC-AUC = 0.906, where ROC is Receiver-Operating Characteristic curve and AUC is the Area Under the Curve) and external (ROC-AUC = 0.783) datasets, outperforming both compound-ligand interaction tools and NR-specific predictors. PbsNRs also successfully identified bioactive chemical scaffolds for NRs by screening massive natural products. Moreover, the predicted bioactive and inactive natural products for NR2B1 were experimentally validated using biosensors. PbsNRs not only aids in screening potential therapeutic NR binders but also helps discover the essential molecular scaffold and guide the drug discovery for multiple NR-related diseases. The PbsNRs web server is available at http://pbsnrs.badd-cao.net.

Ancestral Sequence Reconstruction to Enable Biocatalytic Synthesis of Azaphilones

Biocatalysis can be powerful in organic synthesis but is often limited by enzymes' substrate scope and selectivity. Developing a biocatalytic step involves identifying an initial enzyme for the target reaction followed by optimization through rational design, directed evolution, or both. These steps are time consuming, resource-intensive, and require expertise beyond typical organic chemistry. Thus, an effective strategy for streamlining the process from enzyme identification to implementation is essential to expanding biocatalysis. Here, we present a strategy combining bioinformatics-guided enzyme mining and ancestral sequence reconstruction (ASR) to resurrect enzymes for biocatalytic synthesis. Specifically, we achieve an enantioselective synthesis of azaphilone natural products using two ancestral enzymes: a flavin-dependent monooxygenase (FDMO) for stereodivergent oxidative dearomatization and a substrate-selective acyltransferase (AT) for the acylation of the enzymatically installed hydroxyl group. This cascade, stereocomplementary to established chemoenzymatic routes, expands access to enantiomeric linear tricyclic azaphilones. By leveraging the co-occurrence and coevolution of FDMO and AT in azaphilone biosynthetic pathways, we identified an AT candidate, CazE, and addressed its low solubility and stability through ASR, obtaining a more soluble, stable, promiscuous, and reactive ancestral AT (AncAT). Sequence analysis revealed AncAT as a chimeric composition of its descendants with enhanced reactivity likely due to ancestral promiscuity. Flexible receptor docking and molecular dynamics simulations showed that the most reactive AncAT promotes a reactive geometry between substrates. We anticipate that our bioinformatics-guided, ASR-based approach can be broadly applied in target-oriented synthesis, reducing the time required to develop biocatalytic steps and efficiently access superior biocatalysts.

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.

The Relationship Between Molecular Symmetry and Physicochemical Properties Involving Boiling and Melting of Organic Compounds

OBJECTIVE AND METHODS

The reliable estimation of phase transition physicochemical properties such as boiling and melting points can be valuable when designing compounds with desired physicochemical properties. This study explores the role of external rotational symmetry in determining boiling and melting points of select organic compounds. Using experimental data from the literature, the entropies of boiling and fusion were obtained for 541 compounds. The statistical significance of external rotational symmetry number on entropies of phase change was determined by using multiple linear regression. In addition, a series of aliphatic hydrocarbons, polysubstituted benzenes, and di-substituted napthalenes are used as examples to demonstrate the role of external symmetry on transition temperature.

RESULTS

The results reveal that symmetry is not well correlated with boiling point but is statistically significant in melting point.

CONCLUSION

The lack of correlation between the boiling point and the symmetry number reflects the fact that molecules have a high degree of rotational freedom in both the liquid and the vapor. On the other hand, the strong relationship between symmetry and melting point reflects the fact that molecules are rotationally restricted in the crystal but not in the liquid. Since the symmetry number is equal to the number of ways that the molecule can be properly oriented for incorporation into the crystal lattice, it is a significant determinant of the melting point.

Discovery of GABA Aminotransferase Inhibitors via Molecular Docking, Molecular Dynamic Simulation, and Biological Evaluation

γ-Aminobutyric acid aminotransferase (GABA-AT) is a pyridoxal 5'-phosphate (PLP)-dependent enzyme that degrades γ-aminobutyric (GABA) in the brain. GABA is an important inhibitory neurotransmitter that plays important neurological roles in the brain. Therefore, GABA-AT is an important drug target that regulates GABA levels. Novel and potent drug development to inhibit GABA-AT is still a very challenging task. In this study, we aimed to devise novel and potent inhibitors against GABA-AT using computer-aided drug design (CADD) tools. Since the crystal structure of human GABA-AT was not yet available, we utilized a homologous structure derived from our previously published paper. To identify highly potent compounds relative to vigabatrin, an FDA-approved drug against human GABA-AT, we developed a pharmacophore analysis protocol for 530,000 Korea Chemical Bank (KCB) compounds and selected the top 50 compounds for further screening. Preliminary biological analysis was carried out for these 50 compounds and 16 compounds were further assessed. Subsequently, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were carried out. In the results, four predicted compounds, A07, B07, D08, and H08, were found to be highly potent and were further evaluated by a biological activity assay to confirm the results of the GABA-AT activity inhibition assay.

Generation of broad-spectrum recombinant antibody and construction of colorimetric immunoassay for tropane alkaloids: Recognition mechanism and application

Tropane alkaloids (TAs) have emerged as plant toxins, related to poisoning events. The development of stable antibodies is crucial to ensure the effectiveness of immunological methods in quickly and accurately monitoring these alkaloids. In this study, based on hybridoma, the variable region gene of monoclonal antibody (mAb) was amplified, and the recombinant antibody (rAb) gene sequence (VH-Linker-VL) was successfully constructed and expressed in HEK293F. The obtained rAb has kept the same performance as mAb, and the IC50 of 29 TAs ranged from 0.12 to 2642.78 ng/mL. In the recognition mechanism, the docking and dynamics model identified hydrophobic interaction as the most critical force. Substituent will impact recognition by influencing the spatial structure and hydrophobic properties. Then, a colorimetric immunoassay based on rAb was established, five types of water and thirty-nine nectars of honey were tested. The results demonstrated the absence of TAs in environmental water, whereas atropine was detected in more than 13.47% of honey samples at concentrations exceeding 1 μg/kg. The results show a good correlation with UHPLC-MS/MS, suggesting that the immunoassay has excellent screening ability. The data on TAs in honey and water could serve as a foundation for developing relevant policies.

Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis

Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model's liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.

A TNF-α blocking peptide that reduces NF-κB and MAPK activity for attenuating inflammation

Overexpression of tumor necrosis factor-α (TNF-α) is implicated in many inflammatory diseases, including septic shock, hepatitis, asthma, insulin resistance and autoimmune diseases, such as rheumatoid arthritis and Crohn's disease. The TNF-α signaling pathway is a valuable target, and anti-TNF-α drugs are successfully used to treat autoimmune and inflammatory diseases. Here, we study anti-inflammatory activity of an anti-TNF-α peptide (SN1-13, DEFHLELHLYQSW). In the cellular level assessment, SN1-13 inhibited TNF-α-induced cytotoxicity and blocks TNF-α-triggered signaling activities (IC50 = 15.40 μM). Moreover, the potential binding model between SN1-13 and TNF-α/TNFRs conducted through molecular docking revealed that SN1-13 could stunt TNF-α mediated signaling thought blocking TNF-α and its receptor TNFR1 and TNFR2. These results suggest that SN1-13 would be a potential lead peptide to treat TNF-α-mediated inflammatory diseases.

pyCHARMM: Embedding CHARMM Functionality in a Python Framework

CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks.

Identification of Potential TMPRSS2 Inhibitors for COVID-19 Treatment in Chinese Medicine by Computational Approaches and Surface Plasmon Resonance Technology

BACKGROUND

Coronavirus disease-19 (COVID-19) pneumonia continues to spread in the entire globe with limited medication available. In this study, the active compounds in Chinese medicine (CM) recipes targeting the transmembrane serine protease 2 (TMPRSS2) protein for the treatment of COVID-19 were explored.

METHODS

The conformational structure of TMPRSS2 protein (TMPS2) was built through homology modeling. A training set covering TMPS2 inhibitors and decoy molecules was docked to TMPS2, and their docking poses were re-scored with scoring schemes. A receiver operating characteristic (ROC) curve was applied to select the best scoring function. Virtual screening of the candidate compounds (CCDs) in the six highly effective CM recipes against TMPS2 was conducted based on the validated docking protocol. The potential CCDs after docking were subject to molecular dynamics (MD) simulations and surface plasmon resonance (SPR) experiment.

RESULTS

A training set of 65 molecules were docked with modeled TMPS2 and LigScore2 with the highest area under the curve, AUC, value (0.886) after ROC analysis selected to best differentiate inhibitors from decoys. A total of 421 CCDs in the six recipes were successfully docked into TMPS2, and the top 16 CCDs with LigScore2 higher than the cutoff (4.995) were screened out. MD simulations revealed a stable binding between these CCDs and TMPS2 due to the negative binding free energy. Lastly, SPR experiments validated the direct combination of narirutin, saikosaponin B1, and rutin with TMPS2.

CONCLUSIONS

Specific active compounds including narirutin, saikosaponin B1, and rutin in CM recipes potentially target and inhibit TMPS2, probably exerting a therapeutic effect on COVID-19.

Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning

STAT3 belongs to a family of seven vital transcription factors. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contact Fingerprints and scoring values were implemented as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity to be translated into pharmacophore model(s). Two successful pharmacophores were deduced and subsequently used for in silico screening against the National Cancer Institute (NCI) database. A total of 26 hits were evaluated in vitro for their anti-STAT3 bioactivities. Out of which, three hits of novel chemotypes, showed cytotoxic IC50 values in the nanomolar range (35 nM to 6.7 μM). However, two are potent dihydrofolate reductase (DHFR) inhibitors and therefore should have significant indirect STAT3 inhibitory effects. The third hit (cytotoxic IC50 = 0.44 μM) is purely direct STAT3 inhibitor (devoid of DHFR activity) and caused, at its cytotoxic IC50, more than two-fold reduction in the expression of STAT3 downstream genes (c-Myc and Bcl-xL). The presented work indicates that the concept of data augmentation using multiple docked poses is a promising strategy for generating valid machine learning models capable of discriminating active from inactive compounds.

Identification of Novel Arachidonic Acid 15-Lipoxygenase Inhibitors Based on the Bayesian Classifier Model and Computer-Aided High-Throughput Virtual Screening

Ferroptosis is an iron-dependent lipid peroxidative form of cell death that is distinct from apoptosis and necrosis. ALOX15, also known as arachidonic acid 15-lipoxygenase, promotes ferroptosis by converting intracellular unsaturated lipids into oxidized lipid intermediates and is an important ferroptosis target. In this study, a naive Bayesian machine learning classifier with a structure-based, high-throughput screening approach and a molecular docking program were combined to screen for three compounds with excellent target-binding potential. In the absorption, distribution, metabolism, excretion, and toxicity characterization, three candidate molecules were predicted to exhibit drug-like properties. The subsequent molecular dynamics simulations confirmed their stable binding to the targets. The findings indicated that the compounds exhibited excellent potential ALOX15 inhibitor capacity, thereby providing novel candidates for the treatment of inflammatory ischemia-related diseases caused by ferroptosis.

Combining virtual screening and in vitro evaluation for the discovery of potential CYP11B2 inhibitors

Aim: To search for highly bioactive hits for CYP11B2 inhibitors by virtual screening and in vitro evaluation. Materials & methods: Virtual screening of potential CYP11B2 inhibitors was performed by molecular docking and molecular dynamics simulation. Compound activity was determined by in vitro evaluation using MTT and ELISA assays. Results & conclusion: Based on the results of molecular docking and molecular dynamics simulation, nine lead hits were selected for in vitro biochemical testing. All hits in in vitro experiments had lower inhibitory effects on cell proliferation and certain inhibitory effects on aldosterone secretion. These hits may be excellent candidates for CYP11B2 inhibitors.