The Potential Role of Phenolic Acids from Salvia miltiorrhiza and Cynara scolymus and Their Derivatives as JAK Inhibitors: An In Silico Study

JAK inhibition is a new strategy for treating autoimmune and inflammatory diseases. Previous studies have shown the immunoregulatory and anti-inflammatory effects of Salvia miltiorrhiza and Cynara scolymus and suggest that the bioactivity of their phenolic acids involves the JAK-STAT pathway, but it is unclear whether these effects occur through JAK inhibition. The JAK binding affinities obtained by docking Rosmarinic acid (RosA), Salvianolic acid A (SalA), Salvianolic acid C (SalC), Lithospermic acid, Salvianolic acid B and Cynarin (CY) to JAK (PDB: 6DBN) with AutoDock Vina are −8.8, −9.8, −10.7, −10.0, −10.3 and −9.7 kcal/mol, respectively. Their predicted configurations enable hydrogen bonding with the hinge region and N- and C-terminal lobes of the JAK kinase domain. The benzofuran core of SalC, the compound with the greatest binding affinity, sits near Leu959, such as Tofacitinib’s pyrrolopyrimidine. A SalC derivative with a binding affinity of −12.2 kcal/mol was designed while maintaining this relationship. The docking results show follow-up studies of these phenolic acids as JAK inhibitors may be indicated. Furthermore, derivatives of SalC, RosA, CY and SalA can yield better binding affinity or bioavailability scores, indicating that their structures may be suitable as scaffolds for the design of new JAK inhibitors.     

Evaluating Molecular Docking Software for Small Molecule Binding to G-Quadruplex DNA

G-quadruplexes are four-stranded nucleic acid secondary structures of biological significance and have emerged as an attractive drug target. The G4 formed in the MYC promoter (MycG4) is one of the most studied small-molecule targets, and a model system for parallel structures that are prevalent in promoter DNA G4s and RNA G4s. Molecular docking has become an essential tool in structure-based drug discovery for protein targets, and is also increasingly applied to G4 DNA. However, DNA, and in particular G4, binding sites differ significantly from protein targets. Here we perform the first systematic evaluation of four commonly used docking programs (AutoDock Vina, DOCK 6, Glide, and RxDock) for G4 DNA-ligand binding pose prediction using four small molecules whose complex structures with the MycG4 have been experimentally determined in solution. The results indicate that there are considerable differences in the performance of the docking programs and that DOCK 6 with GB/SA rescoring performs better than the other programs. We found that docking accuracy is mainly limited by the scoring functions. The study shows that current docking programs should be used with caution to predict G4 DNA-small molecule binding modes.     

Identification of African Swine Fever Virus Inhibitors through High Performance Virtual Screening Using Machine Learning

African swine fever virus (ASFV) is a highly contagious virus that causes severe hemorrhagic viral disease resulting in high mortality in domestic and wild pigs, until few antiviral agents can inhibit ASFV infections. Thus, new anti-ASFV drugs need to be urgently identified. Recently, we identified pentagastrin as a potential antiviral drug against ASFVs using molecular docking and machine learning models. However, the scoring functions are easily influenced by properties of protein pockets, resulting in a scoring bias. Here, we employed the 5′-P binding pocket of AsfvPolX as a potential binding site to identify antiviral drugs and classified 13 AsfvPolX structures into three classes based on pocket parameters calculated by the SiteMap module. We then applied principal component analysis to eliminate this scoring bias, which was effective in making the SP Glide score more balanced between 13 AsfvPolX structures in the dataset. As a result, we identified cangrelor and fostamatinib as potential antiviral drugs against ASFVs. Furthermore, the classification of the pocket properties of AsfvPolX protein can provide an alternative approach to identify novel antiviral drugs by optimizing the scoring function of the docking programs. Here, we report a machine learning-based novel approach to generate high binding affinity compounds that are individually matched to the available classification of the pocket properties of AsfvPolX protein.     

Ligand-Enhanced Negative Images Optimized for Docking Rescoring

Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein’s inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach—actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.     

Network Pharmacology Study on Morus alba L. Leaves: Pivotal Functions of Bioactives on RAS Signaling Pathway and Its Associated Target Proteins against Gout

M. alba L. is a valuable nutraceutical plant rich in potential bioactive compounds with promising anti-gouty arthritis. Here, we have explored bioactives, signaling pathways, and key proteins underlying the anti-gout activity of M. alba L. leaves for the first-time utilizing network pharmacology. Bioactives in M. alba L. leaves were detected through GC-MS (Gas Chromatography-Mass Spectrum) analysis and filtered by Lipinski’s rule. Target proteins connected to the filtered compounds and gout were selected from public databases. The overlapping target proteins between bioactives-interacted target proteins and gout-targeted proteins were identified using a Venn diagram. Bioactives-Proteins interactive networking for gout was analyzed to identify potential ligand-target and visualized the rich factor on the R package via the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway on STRING. Finally, a molecular docking test (MDT) between bioactives and target proteins was analyzed via AutoDock Vina. Gene Set Enrichment Analysis (GSEA) demonstrated that mechanisms of M. alba L. leaves against gout were connected to 17 signaling pathways on 26 compounds. AKT1 (AKT Serine/Threonine Kinase 1), γ-Tocopherol, and RAS signaling pathway were selected as a hub target, a key bioactive, and a hub signaling pathway, respectively. Furthermore, three main compounds (γ-Tocopherol, 4-Dehydroxy-N-(4,5-methylenedioxy-2-nitrobenzylidene) tyramine, and Lanosterol acetate) and three key target proteins—AKT1, PRKCA, and PLA2G2A associated with the RAS signaling pathway were noted for their highest affinity on MDT. The identified three key bioactives in M. alba L. leaves might contribute to recovering gouty condition by inactivating the RAS signaling pathway.     

Multipose Binding in Molecular Docking

Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking.     

Advances and challenges in protein-ligand docking

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions-the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion.     

AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability

Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets.     

SARS-CoV-2 Variants,RBD Mutations,Binding Affinity,and Antibody Escape

Since 2020, the receptor-binding domain (RBD) of the spike protein of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been constantly mutating, producing most of the notable missense mutations in the context of “variants of concern”, probably in response to the vaccine-driven alteration of immune profiles of the human population. The Delta variant, in particular, has become the most prevalent variant of the epidemic, and it is spreading in countries with the highest vaccination rates, causing the world to face the risk of a new wave of the contagion. Understanding the physical mechanism responsible for the mutation-induced changes in the RBD’s binding affinity, its transmissibility, and its capacity to escape vaccine-induced immunity is the “urgent challenge” in the development of preventive measures, vaccines, and therapeutic antibodies against the coronavirus disease 2019 (COVID-19) pandemic. In this study, entropy–enthalpy compensation and the Gibbs free energy change were used to analyze the impact of the RBD mutations on the binding affinity of SARS-CoV-2 variants with the receptor angiotensin converting enzyme 2 (ACE2) and existing antibodies. Through the analysis, we found that the existing mutations have already covered almost all possible detrimental mutations that could result in an increase of transmissibility, and that a possible mutation in amino-acid position 498 of the RBD can potentially enhance its binding affinity. A new calculation method for the binding energies of protein–protein complexes is proposed based on the entropy–enthalpy compensation rule. All known structures of RBD–antibody complexes and the RBD–ACE2 complex comply with the entropy–enthalpy compensation rule in providing the driving force behind the spontaneous protein–protein docking. The variant-induced risk of breakthrough infections in vaccinated people is attributed to the L452R mutation’s reduction of the binding affinity of many antibodies. Mutations reversing the hydrophobic or hydrophilic performance of residues in the spike RBD potentially cause breakthrough infections of coronaviruses due to the changes in geometric complementarity in the entropy–enthalpy compensations between antibodies and the virus at the binding sites.     

Design,Synthesis, in vitro MAO‐B Inhibitory Evaluation,and Computational Studies of Some 6‐Nitrobenzothiazole‐Derived Semicarbazones

Monoamine oxidase B (MAO‐B) is an important drug target for the treatment of neurological disorders. A series of 6‐nitrobenzothiazole‐derived semicarbazones were designed, synthesized, and evaluated as inhibitors of the rat brain MAO‐B isoenzyme. Most of the compounds were found to be potent inhibitors of MAO‐B, with IC₅₀ values in the nanomolar to micromolar range. Molecular docking studies were performed with AutoDock 4.2 to deduce the affinity and binding mode of these inhibitors toward the MAO‐B active site. The free energies of binding (ΔG) and inhibition constants (K_i) of the docked compounds were calculated by the Lamarckian genetic algorithm (LGA) of AutoDock 4.2. Good correlations between the calculated and experimental results were obtained. 1‐[(4‐Chlorophenyl)(phenyl)methylene]‐4‐(6‐nitrobenzothiazol‐2‐yl)semicarbazide emerged as the lead MAO‐B inhibitor, with top ranking in both the experimental MAO‐B assay (IC₅₀: 0.004±0.001 μM ) and in computational docking studies (K_i: 1.08 μM ). Binding mode analysis of potent inhibitors suggests that these compounds are well accommodated by the MAO‐B active site through stable hydrophobic and hydrogen bonding interactions. Interestingly, the 6‐nitrobenzothiazole moiety is stabilized in the substrate cavity with the aryl or diaryl residues extending up into the entrance cavity of the active site. According to our results, docking experiments could be an interesting approach for predicting the activity and binding interactions of this class of semicarbazones against MAO‐B. Thus, a binding site model consisting of three essential pharmacophoric features is proposed, and this can be used for the design of future MAO‐B inhibitors.     

Insights into Interactions between Interleukin-6 and Dendritic Polyglycerols

Interleukin-6 (IL-6) is involved in physiological and pathological processes. Different pharmacological agents have been developed to block IL-6 deleterious effects and to recover homeostatic IL-6 signaling. One of the proposed nanostructures in pre-clinical investigations which reduced IL-6 concentrations is polyglycerol dendrimer, a nano-structure with multiple sulfate groups. The aim of the present study was to uncover the type of binding between critical positions in the human IL-6 structure available for binding dPGS and compare it with heparin sulfate binding. We studied these interactions by performing docking simulations of dPGS and heparins with human IL-6 using AutoDock Vina. These molecular docking analyses indicate that the two ligands have comparable affinities for the positively charged positions on the surface of IL-6. All-atom molecular dynamics simulations (MD) employing Gromacs were used to explore the binding sites and binding strengths. Results suggest two major binding sites and show that the strengths of binding are similar for heparin and dPGS (−5.5–6.4 kcal/ mol). dPGS or its analogs could be used in the therapeutic intervention in sepsis and inflammatory disorders to reduce unbound IL-6 in the plasma or tissues and its binding to the receptors. We propose that analogs of dPGS could specifically block IL-6 binding in the desired signaling mode and would be valuable new probes to establish optimized therapeutic intervention in inflammation.     

Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.     

Effect of Ionic Strength on Thioflavin-T Affinity to Amyloid Fibrils and Its Fluorescence Intensity

The formation of amyloid fibrils is linked to multiple neurodegenerative disorders, including Alzheimer’s and Parkinson’s disease. Despite years of research and countless studies on the topic of such aggregate formation, as well as their resulting structure, the current knowledge is still fairly limited. One of the main aspects prohibiting effective aggregation tracking is the environment’s effect on amyloid-specific dyes, namely thioflavin-T (ThT). Currently, there are only a few studies hinting at ionic strength being one of the factors that modulate the dye’s binding affinity and fluorescence intensity. In this work we explore this effect under a range of ionic strength conditions, using insulin, lysozyme, mouse prion protein, and α-synuclein fibrils. We show that ionic strength is an extremely important factor affecting both the binding affinity, as well as the fluorescence intensity of ThT.     

Adamantane-Substituted Purines and Their β-Cyclodextrin Complexes: Synthesis and Biological Activity

Cyclin-dependent kinases (CDKs) play an important role in the cell-division cycle. Synthetic inhibitors of CDKs are based on 2,6,9-trisubstituted purines and are developed as potential anticancer drugs; however, they have low solubility in water. In this study, we proved that the pharmaco-chemical properties of purine-based inhibitors can be improved by appropriate substitution with the adamantane moiety. We prepared ten new purine derivatives with adamantane skeletons that were linked at position 6 using phenylene spacers of variable geometry and polarity. We demonstrated that the adamantane skeleton does not compromise the biological activity, and some of the new purines displayed even higher inhibition activity towards CDK2/cyclin E than the parental compounds. These findings were supported by a docking study, which showed an adamantane scaffold inside the binding pocket participating in the complex stabilisation with non-polar interactions. In addition, we demonstrated that β-cyclodextrin (CD) increases the drug’s solubility in water, although this is at the cost of reducing the biochemical and cellular effect. Most likely, the drug concentration, which is necessary for target engagement, was decreased by competitive drug binding within the complex with β-CD.     

手性咪唑环芳识别氨基酸甲酯的作用模式研究

采用Gaussian 03在B3LYP/6-31G(d)水平上分别优化手性咪唑环芳受体和氨基酸甲酯配体,并在同样条件下计算NBO电荷。用AutoDock Vina将优化后的受体、配体进行对接,获得相应复合物的最稳定构象。以复合物亲和能和配体侧链电子电荷指数,建立了表达复合物稳定常数的定量关系模型。从单体的NBO电荷分布、复合物最稳定构象等方面分析探讨,发现静电吸引、受体配体间的空间匹配和氢键作用等,是影响手性咪唑环芳选择性识别氨基酸甲酯的主要影响因素。     

Prediction of a New Ligand-Binding Site for Type 2 Motif based on the Crystal Structure of ALG-2 by Dry and Wet Approaches

ALG-2 is a penta-EF-hand Ca(2+)-binding protein and interacts with a variety of intracellular proteins. Two types of ALG-2-binding motifs have been determined: type 1, PXYPXnYP (X, variable; n = 4), in ALIX and PLSCR3; type 2, PXPGF, in Sec31A and PLSCR3. The previously solved X-ray crystal structure of the complex between ALG-2 and an ALIX peptide containing type 1 motif showed that the peptide binds to Pocket 1 and Pocket 2. Co-crystallization of ALG-2 and type 2 motif-containing peptides has not been successful. To gain insights into the molecular basis of type 2 motif recognition, we searched for a new hydrophobic cavity by computational algorithms using MetaPocket 2.0 based on 3D structures of ALG-2. The predicted hydrophobic pocket designated Pocket 3 fits with N-acetyl-ProAlaProGlyPhe-amide, a virtual penta-peptide derived from one of the two types of ALG-2-binding sites in PLSCR3 (type 2 motif), using the molecular docking software AutoDock Vina. We investigated effects of amino acid substitutions of the predicted binding sites on binding abilities by pulldown assays using glutathione-S-transferase -fused ALG-2 of wild-type and mutant proteins and lysates of cells expressing green fluorescent protein -fused PLSCR3 of wild-type and mutants. Substitution of either L52 with Ala or F148 with Ser of ALG-2 caused loss of binding abilities to PLSCR3 lacking type 1 motif but retained those to PLSCR3 lacking type 2 motif, strongly supporting the hypothesis that Pocket 3 is the binding site for type 2 motif.     

Binding Studies and Lead Generation of Pteridin-7(8H)-one Derivatives Targeting FLT3

Ligand modification by substituting chemical groups within the binding pocket is a popular strategy for kinase drug development. In this study, a series of pteridin-7(8H)-one derivatives targeting wild-type FMS-like tyrosine kinase-3 (FLT3) and its D835Y mutant (FL3_D835Y) were studied using a combination of molecular modeling techniques, such as docking, molecular dynamics (MD), binding energy calculation, and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies. We determined the protein–ligand binding affinity by employing molecular mechanics Poisson–Boltzmann/generalized Born surface area (MM-PB/GBSA), fast pulling ligand (FPL) simulation, linear interaction energy (LIE), umbrella sampling (US), and free energy perturbation (FEP) scoring functions. The structure–activity relationship (SAR) study was conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and the results were emphasized as a SAR scheme. In both the CoMFA and CoMSIA models, satisfactory correlation statistics were obtained between the observed and predicted inhibitory activity. The MD and SAR models were co-utilized to design several new compounds, and their inhibitory activities were anticipated using the CoMSIA model. The designed compounds with higher predicted pIC₅₀ values than the most active compound were carried out for binding free energy evaluation to wild-type and mutant receptors using MM-PB/GBSA, LIE, and FEP methods.     

Pathomorphogenesis of Glycogen-Ground Glass Hepatocytic Inclusions (Polyglucosan Bodies) in Children after Liver Transplantation

Seventeen out of 764 liver biopsies from transplanted (Tx) livers in children showed glycogen-ground glass (GGG) hepatocytic inclusions. The inclusions were not present in pre-Tx or in the explanted or donor’s liver. Under the electron microscope (EM), the stored material within the cytosol appeared as non-membrane-bound aggregates of electron-lucent globoid or fibrillar granules, previously described as abnormally structured glycogen and identified as Polyglucosan bodies (PB). The appearance of GGG in our children was analogous to that of PB-GGG occurring in a number of congenital diseases due to gene mutations such as Lafora’s d., Andersen’s d., Adult Polyglucosan Body Disease and glycogenin deficiency. The same type of GGG was previously reported in the liver of patients undergoing transplants, immunosuppressive or antiblastic treatment. To explore the potential mechanism of GGG formation, we examined whether the drugs after whose treatment this phenomenon was observed could have a role. By carrying out molecular docking, we found that such drugs somehow present a high binding affinity for the active region of glycogenin, implicating that they can inactivate the protein, thus preventing its interaction with glycogen synthase (GS), as well as the maturation of the nascent glycogen towards gamma, beta or alfa glycogen granules. We could also demonstrate that PG inclusions consist of a complex of PAS positive material (glycogen) and glycogen-associated proteins, i.e., glicogenin-1 and -2 and ubiquitin. These features appear to be analogous to congenital GGG, suggesting that, in both cases, they result from the simultaneous dysregulation of glycogen synthesis and degradation. Drug-induced GGG appear to be toxic to the cell, despite their reversibility.     

A Fragment-Based Scoring Function to Re-rank ATP Docking Results

ATP is involved in numerous biochemical reactions in living cells interacting with different proteins. Molecular docking simulations provide considerable insight into the problem of molecular recognition of this substrate. To improve the selection of correct ATP poses among those generated by docking algorithms we propose a post-docking reranking criterion. The method is based on detailed analysis of the intermolecular interactions in 50 high-resolution 3D-structures of ATP-protein complexes. A distinctive new feature of the proposed method is that the ligand molecule is divided into fragments that differ in their physical properties. The placement of each of them into the binding site is judged separately by different criteria, thus avoiding undesirable averaging of the scoring function terms by highlighting those relevant for particular fragments. The scoring performance of the new criteria was tested with the docking solutions for ATPprotein complexes and a significant improvement in the selection of correct docking poses was observed, as compared to the standard scoring function.