Flexible Docking Research Articles

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.

Research on the pharmacological potential of 1-alkyl derivatives of 3,5-dimethyl-4-((4-nitrobenzylidene)amino)-1,2,4-triazolium bromide

The heterocyclic system of 1,2,4-triazole and its derivatives is one of the leaders in the development of highly promising biologically active compounds. The peculiarities of the chemical structure of the derivatives of this heterocycle provide a wide range of possibilities for chemical transformations that allow to obtain really effective drugs. The involvement of several substituents in chemical transformations simultaneously, which have the properties of highly reactive centers, additionally creates favorable conditions for the formation of rational ways to create a biologically active compound. Amino-, mercapto- or hydroxogroups often play the role of such groups in chemistry. The use of these groups as substituents of 1,2,4-triazole synthon provides multifaceted opportunities for directed chemical transformation. The ability of such structural fragments to form chemical interactions and bonds with biological targets has an additional positive effect in the sense of their involvement in chemical transformations on the way to the targeted production of a biologically active substance. Thus, the combination of a heterocyclic structure with a highly reactive chemical center is endowed with theoretically sound and practically significant meaning. The aim of the work is to preliminary determine the potential for creating a biologically active substance with antifungal action based on 1-alkyl derivatives of 3,5-dimethy-l-4-((4-nitrobenzylidene)amino)-1,2,4-triazolium bromide. Materials and methods. The toxicity of the studied compounds has been predicted using the TEST program (Toxicity Estimation Software Tool), which allowed to determine the predictive level of acute toxicity, ecotoxicity and mutagenicity. The physicochemical and pharmacokinetic parameters have been predicted, and the drug-like properties and availability of the investigated substances have been assessed using the online resource SwissADME. The determination of the most favorable spatial configuration of the ligand relative to the active site of the protein and the assessment of the strength of their interaction have been realized using the computational method of molecular docking. The ligands have been prepared using MarvinSketch 6.3.0, HyperChem 8 and AutoDock Tools-1.5.6 software. The preparation of the model enzyme has been based on the use of Discovery Studio 4.0 and AutoDock Tools-1.5.6. The practical implementation of flexible molecular docking has been carried out using the software tools of the AutoDock/Vina platform. Results. In the process of step-by-step prescreening of the formed structures of a number of 1-alkyl derivatives of 3,5-dimethyl-4-((4-nitrobenzylidene)amino)-1,2,4-triazolium bromide, a number of qualitative and quantitative indicators related to the physicochemical characteristics and pharmacokinetic parameters of the studied substances have been obtained. According to the results of the first stage of research, the group of substances under consideration can be predictively considered low-toxic, but with a high risk of mutagenic properties. The next stage of the work, which involved the analysis of physicochemical parameters, pharmacokinetic parameters, general drug-like properties and bioavailability, allowed us to identify 1-alkyl derivatives of 3,5-dimethyl-4-((4-nitrobenzylidene)amino)-1,2,4-triazolium bromide as substances with a rather positive pharmacological profile. The final stage in the form of molecular docking of the structure of the studied compounds to the active site of lanosterol 14α-demethylase allowed us to determine the nature of the chemical interaction and the type of amino acid residues that may be involved in the antifungal properties of the key ligands. The analysis of the docking results allows us to determine the privileged nature of the nonyl substituent at the first Nitrogen atom of the 1,2,4-triazole synthon in the structure of the presented series of compounds for the formation of antifungal properties. Conclusions. The general prospects for the creation of a biologically active substance with antifungal properties using 1-alkyl derivatives of 3,5-dimethyl-4-((4-nitrobenzylidene)amino)-1,2,4-triazolium bromide look quite realistic. Particular attention should be paid to 3,5-dimethyl-1-nonyl-4-((4-nitrobenzylidene)amino)-1,2,4-triazolium bromide as a substance with significant potential for antifungal properties, which allows us to recommend this compound for further more constructive and extended in vitro and in vivo studies.

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.

A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins

Background: Computational molecular docking plays an important role in determining the precise receptor-ligand conformation, which becomes a powerful tool for drug discovery. In the past 30 years, most computational docking methods have treated the receptor structure as a rigid body, although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher computational cost. Due to the fact that different protein pocket residues exhibit different degrees of flexibility, semi-flexible docking methods, balancing rigid docking and flexible docking, have demonstrated success in predicting highly accurate conformations with a relatively low computational cost. Methods: In our study, the number of flexible pocket residues was assessed by quantitative analysis, and a novel adaptive residue flexibility prediction method, named A-RFP, was proposed to improve the docking performance. Based on the homologous information, a joint strategy is used to predict the pocket residue flexibility by combining RMSD, the distance between the residue sidechain and the ligand, and the sidechain orientation. For each receptor-ligand pair, A-RFP provides a docking conformation with the optimal affinity. Results: By analyzing the docking affinities of 3507 target-ligand pairs in 5 different values ranging from 0 to 10, we found there is a general trend that the larger number of flexible residues inevitably improves the docking results by using Autodock Vina. However, a certain number of counterexamples still exist. To validate the effectiveness of A-RFP, the experimental assessment was tested in a small-scale virtual screening on 5 proteins, which confirmed that A-RFP could enhance the docking performance. And the flexible-receptor virtual screening on a low-similarity dataset with 85 receptors validates the accuracy of residue flexibility comprehensive evaluation. Moreover, we studied three receptors with FDA-approved drugs, which further proved A-RFP can play a suitable role in ligand discovery. Conclusion: Our analysis confirms that the screening performance of the various numbers of flexible residues varies wildly across receptors. It suggests that a fine-grained docking method would offset the aforementioned deficiency. Thus, we presented A-RFP, an adaptive pocket residue flexibility prediction method based on homologous information. Without considering computational resources and time costs, A-RFP provides the optimal docking result.

(-)-Epicatechin metabolites as a GPER ligands: a theoretical perspective.

Diet habits and nutrition quality significantly impact health and disease. Here is delve into the intricate relationship between diet habits, nutrition quality, and their direct impact on health and homeostasis. Focusing on (-)-Epicatechin, a natural flavanol found in various foods like green tea and cocoa, known for its positive effects on cardiovascular health and diabetes prevention. The investigation encompasses the absorption, metabolism, and distribution of (-)-Epicatechin in the human body, revealing a diverse array of metabolites in the circulatory system. Notably, (-)-Epicatechin demonstrates an ability to activate nitric oxide synthase (eNOS) through the G protein-coupled estrogen receptor (GPER). While the precise role of GPER and its interaction with classical estrogen receptors (ERs) remains under scrutiny, the study employs computational methods, including density functional theory, molecular docking, and molecular dynamics simulations, to assess the physicochemical properties and binding affinities of key (-)-Epicatechin metabolites with GPER. DFT analysis revealed distinct physicochemical properties among metabolites, influencing their reactivity and stability. Rigid and flexible molecular docking demonstrated varying binding affinities, with some metabolites surpassing (-)-Epicatechin. Molecular dynamics simulations highlighted potential binding pose variations, while MMGBSA analysis provided insights into the energetics of GPER-metabolite interactions. The outcomes elucidate distinct interactions, providing insights into potential molecular mechanisms underlying the effects of (-)-Epicatechin across varied biological contexts.

Could Mushrooms' Secondary Metabolites Ameliorate Alzheimer Disease? A Computational Flexible Docking Investigation.

Herein, we highlight the significance of molecular modeling approaches prior to in vitro and invivo studies; particularly, in diseases with no recognized treatments such as neurological abnormalities. Alzheimer disease is a neurodegenerative disorder that causes irreversible cognitive decline. Toxicity and ADMET studies were conducted using the Qikprop platform in Maestro software and Discovery Studio 2.0, respectively, to select the promising skeletons from more than 45 reviewed compounds isolated from mushrooms in the last decade. Using rigid and flexible molecular docking approaches such as induced fit docking (IFD) in the binding sites of β-secretase (BACE1) and acetylcholine esterase (ACHE), promising structures were screened through high precision molecular docking compared with standard drugs donepezil and (2E)-2-imino-3-methyl-5,5-diphenylimidazolidin-4-one (OKK) using Maestro and Cresset Flare platforms. Molecular interactions, binding distances, and RMSD values were measured to reveal key interactions at the binding sites of the two neurodegenerative enzymes. Analysis of IFD results revealed consistent bindings of dictyoquinazol A and gensetin I in the pocket of 4ey7 while inonophenol A, ganomycin, and fornicin fit quite well in 4dju demonstrating binding poses very close to native ligands at ACHE and BACE1. Respective key amino acid contacts manifested the least steric problems according to their Gibbs free binding energies, Glide XP scores, RMSD values, and molecular orientation respect to the key amino acids. Molecular dynamics simulations further confirmed our findings and prospected these compounds to show significant in vitro results in their future pharmacological studies.

3D physiologically-informed deep learning for drug discovery of a novel vascular endothelial growth factor receptor-2 (VEGFR2)

Angiogenesis is an essential process in tumorigenesis, tumor invasion, and metastasis, and is an intriguing pathway for drug discovery. Targeting vascular endothelial growth factor receptor 2 (VEGFR2) to inhibit tumor angiogenic pathways has been widely explored and adopted in clinical practice. However, most drugs, such as the Food and Drug Administration –approved drug axitinib (ATC code: L01EK01), have considerable side effects and limited tolerability. Therefore, there is an urgent need for the development of novel VEGFR2 inhibitors. In this study, we propose a novel strategy to design potential candidates targeting VEGFR2 using three-dimensional (3D) deep learning and structural modeling methods. A geometric-enhanced molecular representation learning method (GEM) model employing a graph neural network (GNN) as its underlying predictive algorithm was used to predict the activity of the candidates. In the structural modeling method, flexible docking was performed to screen data with high affinity and explore the mechanism of the inhibitors. Small -molecule compounds with consistently improved properties were identified based on the intersection of the scores obtained from both methods. Candidates identified using the GEM-GNN model were selected for in silico modeling using molecular dynamics simulations to further validate their efficacy. The GEM-GNN model enabled the identification of candidate compounds with potentially more favorable properties than the existing drug, axitinib, while achieving higher efficacy.

Study of bromelain and carboxymethyl cellulose surface interaction features in the process of their adsorption immobilisation

Adsorption immobilisation is a common approach to stabilise the catalytic activity of enzymes. It involves the attachment of their macromolecules to the surface of a carrier due to weak physical interactions and hydrogen bonds. According to modern concepts, this process is the ‘gentlest’ immobilisation technique in terms of the structure of the enzyme globule. Despite this fact, the activity of the immobilised preparation is often lower compared to the native enzyme. To understand the reasons, it is necessary to study the specific features of interactions within the enzyme-carrier system and to identify the types of interactions occurring between its components. When studying the structure of enzymes, it is critical to determine the nature and qualitative composition of amino acid residues on the surface of the enzyme macromolecule that interact with the carrier. If catalytically important residues are involved in the process, there may be a significant decrease in the enzyme activity. In this regard, the aim of the work was to study the features of interaction between bromelain, which is a cysteine protease, and carboxymethyl cellulose by desorption of the enzyme from the formed complex under different conditions (in the presence of ammonium sulphate or surfactant Triton X-100, including at different temperatures), as well as by flexible molecular docking. The studied substances are promising components for the production of biocatalysts for food or biomedical applications, so the study of their interaction features will expand the applications of bromelain. We determined that incubation of bromelain immobilised on carboxymethyl cellulose in solutions of ammonium sulphate with a concentration of 32 mM or higher and Triton X-100 with a concentration of 100 mM or higher resulted in the destruction of the complex and the desorption of the enzyme. It confirms that non-covalent interactions contribute to the formation of immobilised preparation. When we increased the incubation temperature of the complex above 60 °C, we also observed the release of the enzyme from the preparation, indicating the formation of hydrogen bonds between the enzyme and the carrier. In silico study confirmed the formation of these types of bonds and interactions. It was determined that the amino acid residues forming the active site of bromelain also formed bonds with the carrier.

Navigating the complexities of docking tools with nicotinic receptors and acetylcholine binding proteins in the realm of neonicotinoids

Molecular docking, pivotal in predicting small-molecule ligand binding modes, struggles with accurately identifying binding conformations and affinities. This is particularly true for neonicotinoids, insecticides whose impacts on ecosystems require precise molecular interaction modeling. This study scrutinizes the effectiveness of prominent docking software (Ledock, ADFR, Autodock Vina, CDOCKER) in simulating interactions of environmental chemicals, especially neonicotinoid-like molecules with nicotinic acetylcholine receptors (nAChRs) and acetylcholine binding proteins (AChBPs). We aimed to assess the accuracy and reliability of these tools in reproducing crystallographic data, focusing on semi-flexible and flexible docking approaches. Our analysis identified Ledock as the most accurate in semi-flexible docking, while Autodock Vina with Vinardo scoring function proved most reliable. However, no software consistently excelled in both accuracy and reliability. Additionally, our evaluation revealed that none of the tools could establish a clear correlation between docking scores and experimental dissociation constants (Kd) for neonicotinoid-like compounds. In contrast, a strong correlation was found with drug-like compounds, bringing to light a bias in considered software towards pharmaceuticals, thus limiting their applicability to environmental chemicals. The comparison between semi-flexible and flexible docking revealed that the increased computational complexity of the latter did not result in enhanced accuracy. In fact, the higher computational cost of flexible docking with its lack of enhanced predictive accuracy, rendered this approach useless for this class of compounds. Conclusively, our findings emphasize the need for continued development of docking methodologies, particularly for environmental chemicals. This study not only illuminates current software capabilities but also underscores the urgency for advancements in computational molecular docking as it is a relevant tool to environmental sciences.

Covalent Flexible Peptide Docking in Drug Discovery: Current Challenges and Potential Interventions

Recently, there has been a significant increase in the search for new covalent inhibitors through drug discovery platforms, which has led to the development and implementation of new computational tools, including covalent docking methods to predict the binding mode and affinity of covalent ligands. Since the discovery of insulin nearly a century ago, more than 80 peptide medications have hit the market to treat a wide range of diseases, including diabetes, cancer, osteoporosis, multiple sclerosis, HIV infection, and chronic pain. Electrophilic peptides that form covalent bonds with target proteins have great potential for binding targets that have been previously considered undruggable. Despite the recent advancements in computational performance and docking algorithms, covalent docking still poses a number of challenges. Peptides covalent docking presents additional challenges, the main ones being the choice of the optimal peptide sequence, incorporation of electrophilic warheads, the inherent flexibility of peptide structures, and the fact that, unlike small molecules, peptides do fold. In this review, we present a brief overview of the current state of peptide therapeutics in drug discovery, covalent docking in general, covalent peptide binders, and-with particular emphasis-the difficulties that covalent peptide-protein docking is currently facing and some potential solutions.

Computational Study of Lactucine and its Derivatives to Investigate its Anti-cancerous Properties Targeting Apoptosis-inducing Protein

Background: Lactucine is related to the sesquiterpene lactone group of naturally occurring compounds and has a variety of pharmacological effects including anticancer properties found in Chicory, Wormwood, Laurus nobilis, Pyrethrum, Chamomile, etc. Lactucine has an anticancer effect which may induce apoptosis in cancerous cells and protect other cells from getting infected. Objective: In this study, Lactucine and its derivatives were screened, and performed their in silico docking study with the proteins involved in the apoptosis-inducing effect in human leukemia cancer. Methods: The three-dimensional structure of lactucine and its derivatives were retrieved in the SDF format. Active sites of protein structures were determined by Sitemap. LigPrep module was used for geometrical refining of chemical structures of lactucine and its derivatives. The protein preparation wizard of Maestro (Schrodinger) was used for protein preparation. From the receptor-complex structure, the cocrystallized ligands were removed from the active site position of the receptor chain. All ligands were docked using default Glide settings for a grid centered on the ligand and structure. Flexible docking was performed using the extra precision (XP) feature of Glide module. The best docking poses for the lactucine and their derivatives were selected based on their docking score. The ADMET properties of lactucine 15- oxalate have been predicted by admetSAR software. Results: Proteins and ligands three-dimensional structures were retrieved from PDB and Pubchem databases, respectively. All lactucine derivatives suitably docked on the apoptosis-inducing proteins with ample Glide scores Lactucin 15-oxalate interacts with proteins which are responsible for apoptosis with a maximum of six H-bonds. Other types of interactions are also involved, like Pi-cation, Pi-Pi stacking, salt bridges, and halogen bonds. Protein CDK-4 has shown the highest number of H-bond (LYS142 salt bridges), ALA16, VAL14, ASP99, LYS35, TYR17, and ASN145) with the Lactucin 15-oxalate. ADMET properties of lactucin 15-oxalate met with the criteria of being eligible to be a novel drug for the treatment of human leukemia cancer. The Dock score of both the Dasatinib drug and the lactucine-15-oxalate with the apoptosis-inducing protein stipulates that the selected ligand has equitable interaction with the target proteins. Conclusion: In this study, lactucine derivatives were docked with apoptosis-inducing proteins for the prediction of its anticancer effect. Lactucin15-oxalate has shown the highest binding affinity for the CDK-4 target and can be used as a lead compound for cancer treatment. Glide and Dock score for docking of lactucin 15-oxalate with CDK-4, well as the number of hydrogen bonding, is in agreement to use this ligand for study. These in silico results are valuable to proceed with the in vitro and in vivo studies related to the anti-cancer role of lactucin 15-oxalate.

In Silico Exploration of Molecular Mechanisms for Inhibiting Inflammatory Responses by 3Н-Thiazolo[4,5-b]pyridin-2-one Derivatives.

Combined in silico strategy for molecular mechanisms exploration of a series 3H-thiazolo[4,5-b]pyridin-2-ones exhibiting strong anti-exudative action through QSAR analysis, molecular docking and pharmacophore modelling is reported. GA-ML technique was used for QSAR models generation with 2D autocorrelation descriptors. One- and two-parameter regressions revealed that certain structural patterns or heteroatoms contribute mutually to the anti-exudative activity potentiation. Possible action mechanisms were discovered through flexible docking simulations with cyclooxygenase pathway enzymes (COX-1, COX-2, mPGES-1). Docking results indicated the possibility of stable complexes formation with the effective docking scores and proper orientation of ligands within the enzymes active sites. Pharmacophore modelling was carried out using protein-ligand interaction fingerprints methodology. Two- and three-centre 3D pharmacophore queries were constructed. Their analysis indicated the functionality of bicyclic thiazolopyridine scaffold proved by the steric placement of heteroatoms in the corresponding pharmacophore centres.

Pose detection and docking control for autonomous dynamic docking mechanism with non-cooperative targets

PurposeThis study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.Design/methodology/approachFirstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.FindingsExtensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.Originality/valueThis research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

Synthesis, cytotoxicity and antioxidant activity of new conjugates of N-acetyl-d-glucosamine with α-aminophosphonates

A series of the first conjugates of N-acetyl-d-glucosamine with α-aminophosphonates was synthesized using the Kabachnik-Fields reaction, the Pudovik reaction, a copper(I)-catalyzed azide-alkyne cycloaddition reaction (CuAAC) and evaluated for the in vitro cytotoxicity against human cancer cell lines M − HeLa, HuTu-80, A549, PANC-1, MCF-7, T98G and normal lung fibroblast cells WI-38. The tested conjugates, with exception of compound 21b, considered as a lead compound, were either inactive against the used cancer cells or showed moderate cytotoxicity in the range of IC50 values 33−80 μM. The lead compound 21b, being non cytotoxic against normal human cells WI-38 (IC50 = 90 μM), demonstrated good activity (IC50 = 17 μM) against breast adenocarcinoma cells (MCF-7) which to be 1.5 times higher than the activity of the used reference anticancer drug tamoxifen (IC50 = 25.0 μM). A flexible receptor molecular docking simulation showed that the cytotoxicity of the synthesized conjugates of N-acetyl-d-glucosamine with α-aminophosphonates against breast adenocarcinoma MCF-7 cell line is due to their ability to inhibit EGFR kinase domain. In addition, it was found that conjugates 22a and 22b demonstrated antioxidant activity that was not typical for α-aminophosphonates.

Enhancing plant-based cheese formulation through molecular docking and dynamic simulation of tocopherol and retinol complexes with zein, soy and almond proteins via SVM-machine learning integration

The current study addresses the growing demand for sustainable plant-based cheese alternatives by employing molecular docking and deep learning algorithms to optimize protein-ligand interactions. Focusing on key proteins (zein, soy, and almond protein) along with tocopherol and retinol, the goal was to improve texture, nutritional value, and flavor characteristics via dynamic simulations. The findings demonstrated that the docking analysis presented high accuracy in predicting conformational changes. Flexible docking algorithms provided insights into dynamic interactions, while analysis of energetics revealed variations in binding strengths. Tocopherol exhibited stronger affinity (−5.8Kcal/mol) to zein compared to retinol (−4.1Kcal/mol). Molecular dynamics simulations offered comprehensive insights into stability and behavior over time. The integration of machine learning algorithms improved the classification and the prediction accuracy, achieving a rate of 71.59%. This study underscores the significance of molecular understanding in driving innovation in the plant-based cheese industry, facilitating the development of sustainable alternatives to traditional dairy products.

QUBO Problem Formulation of Fragment-Based Protein-Ligand Flexible Docking.

Protein-ligand docking plays a significant role in structure-based drug discovery. This methodology aims to estimate the binding mode and binding free energy between the drug-targeted protein and candidate chemical compounds, utilizing protein tertiary structure information. Reformulation of this docking as a quadratic unconstrained binary optimization (QUBO) problem to obtain solutions via quantum annealing has been attempted. However, previous studies did not consider the internal degrees of freedom of the compound that is mandatory and essential. In this study, we formulated fragment-based protein-ligand flexible docking, considering the internal degrees of freedom of the compound by focusing on fragments (rigid chemical substructures of compounds) as a QUBO problem. We introduced four factors essential for fragment-based docking in the Hamiltonian: (1) interaction energy between the target protein and each fragment, (2) clashes between fragments, (3) covalent bonds between fragments, and (4) the constraint that each fragment of the compound is selected for a single placement. We also implemented a proof-of-concept system and conducted redocking for the protein-compound complex structure of Aldose reductase (a drug target protein) using SQBM+, which is a simulated quantum annealer. The predicted binding pose reconstructed from the best solution was near-native (RMSD = 1.26 Å), which can be further improved (RMSD = 0.27 Å) using conventional energy minimization. The results indicate the validity of our QUBO problem formulation.

Network pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of ellagic acid against rotavirus

The target and mechanism of ellagic acid (EA) against rotavirus (RV) were investigated by network pharmacology, computational biology, and surface plasmon resonance verification. The target of EA was obtained from 11 databases such as HIT and TCMSP, and RV-related targets were obtained from the Gene Cards database. The relevant targets were imported into the Venny platform to draw a Venn diagram, and their intersections were visualized. The protein–protein interaction networks (PPI) were constructed using STRING, DAVID database, and Cytoscape software, and key targets were screened. The target was enriched by Gene Ontology (GO) and KEGG pathway, and the ‘EA anti-RV target-pathway network’ was constructed. Schrodinger Maestro 13.5 software was used for molecular docking to determine the binding free energy and binding mode of ellagic acid and target protein. The Desmond program was used for molecular dynamics simulation. Saturation mutagenesis analysis was performed using Schrodinger's Maestro 13.5 software. Finally, the affinity between ellagic acid and TLR4 protein was investigated by surface plasmon resonance (SPR) experiments. The results of network pharmacological analysis showed that there were 35 intersection proteins, among which Interleukin-1β (IL-1β), Albumin (ALB), Nuclear factor kappa-B1 (NF-κB1), Toll-Like Receptor 4 (TLR4), Tumor necrosis factor alpha (TNF-α), Tumor protein p53 (TP53), Recombinant SMAD family member 3 (SAMD3), Epidermal growth factor (EGF) and Interleukin-4 (IL-4) were potential core targets of EA anti-RV. The GO analysis consists of biological processes (BP), cellular components (CC), and molecular functions (MF). The KEGG pathways with the highest gene count were mainly related to enteritis, cancer, IL-17 signaling pathway, and MAPK signaling pathway. Based on the crystal structure of key targets, the complex structure models of TP53-EA, TLR4-EA, TNF-EA, IL-1β-EA, ALB-EA, NF-κB1-EA, SAMD3-EA, EGF-EA, and IL-4-EA were constructed by molecular docking (XP mode of flexible docking). The MMGBS analysis and molecular dynamics simulation were also studied. The Δaffinity of TP53 was highest in 220 (CYS → TRP), 220 (CYS → TYR), and 220 (CYS → PHE), respectively. The Δaffinity of TLR4 was highest in 136 (THR → TYR), 136 (THR → PHE), and 136 (THR → TRP). The Δaffinity of TNF-α was highest in 150 (VAL → TRP), 18 (ALA → GLU), and 144 (PHE → GLY). SPR results showed that ellagic acid could bind TLR4 protein specifically. TP53, TLR4, and TNF-α are potential targets for EA to exert anti-RV effects, which may ultimately provide theoretical basis and clues for EA to be used as anti-RV drugs by regulating TLR4/NF-κB related pathways.

Modeling of noncovalent inhibitors of the papain-like protease (PLpro) from SARS-CoV-2 considering the protein flexibility by using molecular dynamics and cross-docking.

The papain-like protease (PLpro) found in coronaviruses that can be transmitted from animals to humans is a critical target in respiratory diseases linked to Severe Acute Respiratory Syndrome (SARS-CoV). Researchers have proposed designing PLpro inhibitors. In this study, a set of 89 compounds, including recently reported 2-phenylthiophenes with nanomolar inhibitory potency, were investigated as PLpro noncovalent inhibitors using advanced molecular modeling techniques. To develop the work with these inhibitors, multiple structures of the SARS-CoV-2 PLpro binding site were generated using a molecular sampling method. These structures were then clustered to select a group that represents the flexibility of the site. Subsequently, models of the protein-ligand complexes were created for the set of inhibitors within the chosen conformations. The quality of the complex models was assessed using LigRMSD software to verify similarities in the orientations of the congeneric series and interaction fingerprints to determine the recurrence of chemical interactions. With the multiple models constructed, a protocol was established to choose one per ligand, optimizing the correlation between the calculated docking energy values and the biological activities while incorporating the effect of the binding site's flexibility. A strong correlation (R2 = 0.922) was found when employing this flexible docking protocol.

Synthesis of New Imidazo[1,2‐a]pyridine Triazole Hybrid Molecules as Potential Apoptotic Antitumor Agents

AbstractNovel imidazo[1,2‐a]pyridines bearing 1,2,3‐triazole moieties at the C3 position were synthesized. After the characterization of the synthesized compounds, their in vitro therapeutic activities were evaluated in various cancer cell lines (MCF7, A549, HePG2 and T98G). Methoxy substituted derivative was identified as the most potent compound based on the results of its anti‐proliferative activity on various cancer cell lines, as well as showing no cytotoxicity on the healthy human fibroblast cell line (MRC‐5). As an indicator of apoptosis, a significant decrease in the level of PARP protein was observed in the MCF7 cells treated with this derivative. Molecular docking studies were conducted on wide range of targets such as phosphoinositide 3‐kinase (PI3K), cyclin‐independent kinase 2 (CDK2), mitogen‐activated protein kinase (MEK), insulin‐like growth‐factor‐1 (IGF‐1), tubulin, DNA topoisomerase, poly (ADP‐ribose) polymerase (PARP) and B‐cell lymphoma‐2 (BCL2). All the compounds tested showed the lowest binding energies with target PARP1. Moreover, CDK2 and tubulin displayed relatively good binding scores. The docking poses and scores were cross‐checked with two different software and multiple protein conformations were included to incorporate flexible protein docking features. Finally, drug‐likeness properties of the compounds are further tested via Swiss‐ADME software.

Flexible protein-protein docking with a multitrack iterative transformer.

Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multitrack iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments, GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. On the Database of Interacting Protein Structures (DIPS) test set, GeoDock achieves a 43% top-1 success rate, outperforming all other tested methods. However, in the standard DIPS train/test splits, we discovered contamination of close homologs in the training set. After decontaminating the training set, the success rate is 31%. On the DB5.5 test set and a benchmark dataset of antibody-antigen complexes, GeoDock outperforms the deep learning models trained using the same dataset but falls behind most of the conventional methods and AlphaFold-Multimer. GeoDock attains an average inference speed of under 1 s on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.