Interaction studies of cannabidiol with human serum albumin by surface plasmon resonance, spectroscopy, and molecular docking

The binding interaction of cannabidiol (CBD) and human serum albumin (HSA) under physiological blood pH conditions (pH 7.4) was conducted by surface plasmon resonance (SPR), fluorescence spectroscopy, UV-Visible spectrophotometry, and molecular docking. The responses from SPR measurement increased with the increase in CBD concentration until equilibrium was reached at the equilibrium dissociation constant (KD) of 9.8 × 10-4 M. The results from fluorescence and UV-Visible spectroscopy showed that CBD bound to HSA at one site in a spontaneous manner to form protein-CBD complexes. The quenching process involved both static and dynamic mechanisms while the static mechanism contributed predominantly to the binding between CBD and albumin. The binding constants estimated from the fluorescence studies were from 0.16 × 103 to 8.10 × 103 M-1, which were calculated at different temperature conditions using Stern-Volmer plots. The thermodynamic parameters demonstrated that the binding interaction was a spontaneous reaction as Gibbs free energy had negative values (ΔG = -12.57 to -23.20 kJ.mol-1). Positive ΔH and ΔS values (ΔH = 2.46 × 105 J.mol-1 and ΔS = 869.81 J.mol-1K-1) indicated that the hydrophobic force was the major binding interaction. Finally, confirmation of the type and extent of interaction was provided using UV-spectroscopy and molecular docking studies. The outcomes of this study are expected to serve as a platform to conduct future studies on binding interactions and toxicological research of CBD.Communicated by Ramaswamy H. Sarma.

In silico evaluation of the inhibitory potential of nucleocapsid inhibitors of SARS-CoV-2: a binding and energetic perspective

The COVID-19 outbreak brought on by the SARS-CoV-2 virus continued to infect a sizable population worldwide. The SARS-CoV-2 nucleocapsid (N) protein is the most conserved RNA-binding structural protein and is a desirable target because of its involvement in viral transcription and replication. Based on this aspect, this study focused to repurpose antiviral compounds approved or in development for treating COVID-19. The inhibitors chosen are either FDA-approved or are currently being studied in clinical trials against COVID-19. Initially, they were designed to target stress granules and other RNA biology. We have utilized structure-based molecular docking and all-atom molecular dynamics (MD) simulation approach to investigate in detail the binding energy and binding modes of the different anti-N inhibitors to N protein. The result showed that five drugs including Silmitasterib, Ninetanidinb, Ternatin, Luteolin, Fedratinib, PJ34, and Zotatafin were found interacting with RNA binding sites as well as to predicted protein interface with higher binding energy. Overall, drug binding increases the stability of the complex with maximum stability found in the order, Silmitasertib > PJ34 > Zotatatafin. In addition, the frustration changes due to drug binding brings a decrease in local frustration and this decrease is mainly observed in α-helix, β3, β5, and β6 strands and are important for drug binding. Our in-silico data suggest that an effective interaction occurs for some of the tested drugs and prompt their further validation to reduce the rapid outspreading of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Drug-Protein Interactions Prediction Models Using Feature Selection and Classification Techniques

BACKGROUND

Drug-Protein Interaction (DPI) identification is crucial in drug discovery. The high dimensionality of drug and protein features poses challenges for accurate interaction prediction, necessitating the use of computational techniques. Docking-based methods rely on 3D structures, while ligand-based methods have limitations such as reliance on known ligands and neglecting protein structure. Therefore, the preferred approach is the chemogenomics-based approach using machine learning, which considers both drug and protein characteristics for DPI prediction.

METHODS

In machine learning, feature selection plays a vital role in improving model performance, reducing overfitting, enhancing interpretability, and making the learning process more efficient. It helps extract meaningful patterns from drug and protein data while eliminating irrelevant or redundant information, resulting in more effective machine-learning models. On the other hand, classification is of great importance as it enables pattern recognition, decision-making, predictive modeling, anomaly detection, data exploration, and automation. It empowers machines to make accurate predictions and facilitates efficient decision-making in DPI prediction. For this research work, protein data was sourced from the KEGG database, while drug data was obtained from the DrugBank data machine-learning base.

RESULTS

To address the issue of imbalanced Drug Protein Pairs (DPP), different balancing techniques like Random Over Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive SMOTE were employed. Given the large number of features associated with drugs and proteins, feature selection becomes necessary. Various feature selection methods were evaluated: Correlation, Information Gain (IG), Chi-Square (CS), and Relief. Multiple classification methods, including Support Vector Machines (SVM), Random Forest (RF), Adaboost, and Logistic Regression (LR), were used to predict DPI. Finally, this research identifies the best balancing, feature selection, and classification methods for accurate DPI prediction.

CONCLUSION

This comprehensive approach aims to overcome the limitations of existing methods and provide more reliable and efficient predictions in drug-protein interaction studies.

FragDPI: a novel drug-protein interaction prediction model based on fragment understanding and unified coding

Prediction of drug-protein binding is critical for virtual drug screening. Many deep learning methods have been proposed to predict the drug-protein binding based on protein sequences and drug representation sequences. However, most existing methods extract features from protein and drug sequences separately. As a result, they can not learn the features characterizing the drug-protein interactions. In addition, the existing methods encode the protein (drug) sequence usually based on the assumption that each amino acid (atom) has the same contribution to the binding, ignoring different impacts of different amino acids (atoms) on the binding. However, the event of drug-protein binding usually occurs between conserved residue fragments in the protein sequence and atom fragments of the drug molecule. Therefore, a more comprehensive encoding strategy is required to extract information from the conserved fragments. In this paper, we propose a novel model, named FragDPI, to predict the drug-protein binding affinity. Unlike other methods, we encode the sequences based on the conserved fragments and encode the protein and drug into a unified vector. Moreover, we adopt a novel two-step training strategy to train FragDPI. The pre-training step is to learn the interactions between different fragments using unsupervised learning. The fine-tuning step is for predicting the binding affinities using supervised learning. The experiment results have illustrated the superiority of FragDPI.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material is available for this article at 10.1007/s11704-022-2163-9 and is accessible for authorized users.

In vitro and In silico Interactions of Antiulcer, Glucocorticoids and Urological Drugs on Human Carbonic Anhydrase I and II isozymes

Carbonic anhydrases (CAs, EC 4.2.1.1) convert carbon dioxide to bicarbonate in metabolism and use Zn²⁺ ions as a cofactor for their catalytic activity. The activators or inhibitors of CA-I and CA-II, which are the most abundant CA isozymes in erythrocytes, have pharmacological applications in medicine. So, investigation of drug-protein interaction of these isozymes is significant. On this basis, the objective of this study was to clarify the primer effects of widely used drugs on the activity of human CA-I and CA-II enzymes and elucidate the inhibition mechanism through molecular docking studies. For this aim isozymes were purified from human erythrocytes by affinity chromatography technique. Then inhibition profiles of antiulcer, glucocorticoids, and urological drugs were investigated. As a result, while budesonide had the highest inhibitory potency on hydratase activity of hCA-I with the IC₅₀ of 0.08 mM, levofloxacin showed the highest inhibition effect on hCA-II with the IC₅₀ of 0.886 mM. The most effective inhibitor on the esterase activity of isozymes was found as fluticasone propionate with the K_i values of 0.0365±0.016 mM and 0.054±0.018 mM respectively. However, by molecular docking study, it was estimated that budesonide showed maximum inhibition potency for both isozymes with the free binding energy of -7.58 and -6.97 kcal/mol respectively. Consequently, it was observed that some of the drugs studied did not show any inhibitory effect. Drug-enzyme interactions were also estimated by molecular docking. This study could contribute to the discovery of new drug candidates and as well as target proteins. This article is protected by copyright. All rights reserved.

Large-scale Prediction of Drug-Protein Interactions Based on Network Information

BACKGROUND

The prediction of drug-protein interaction (DPI) plays an important role in drug discovery and re-positioning. Unfortunately, traditional experimental validation of DPIs is expensive and time-consuming. Therefore, it is necessary to develop in silico methods for the identification of potential DPIs.

METHOD

In this work, the identification of DPIs was performed by the generated recommendation of the unexplored interaction of the drug-protein bipartite graph. Three kinds of recommenders were proposed to predict the potential DPIs.

RESULTS

The simulation results showed that the proposed models obtained good performance in cross validation and independent test.

CONCLUSION

Our recommendation strategy based on collaborative filtering can effectively improve the DPI identification performance, especially for certain DPIs lacking chemical structure similarity or genomic sequence similarity.

Direct Binding of Cisplatin to p22phox, an Endoplasmic Reticulum (ER) Membrane Protein, Contributes to Cisplatin Resistance in Oral Squamous Cell Carcinoma (OSCC) Cells

Prolonged treatment with cisplatin (CDDP) frequently develops chemoresistance. We have previously shown that p22phox, an endoplasmic reticulum (ER) membrane protein, confers CDDP resistance by blocking CDDP nuclear entry in oral squamous cell carcinoma (OSCC) cells; however, the underlying mechanism remains unresolved. Using a fluorescent dye-labeled CDDP, here we show that CDDP can bind to p22phox in both cell-based and cell-free contexts. Subsequent detection of CDDP-peptide interaction by the Tris-Tricine-based electrophoresis revealed that GA-30, a synthetic peptide matching a region of the cytosolic domain of p22phox, could interact with CDDP. These results were further confirmed by liquid chromatography–mass spectrometry (LC–MS) analysis, from which MA-11, an 11-amino acid subdomain of the GA-30 domain, could largely account for the interaction. Amino acid substitutions at Cys50, Met65 and Met73, but not His72, significantly impaired the binding between CDDP and the GA-30 domain, thereby suggesting the potential CDDP-binding residues in p22phox protein. Consistently, the p22phox point mutations at Cys50, Met65 and Met73, but not His72, resensitized OSCC cells to CDDP-induced cytotoxicity and apoptosis. Finally, p22phox might have binding specificity for the platinum drugs, including CDDP, carboplatin and oxaliplatin. Together, we have not only identified p22phox as a novel CDDP-binding protein, but further highlighted the importance of such a drug-protein interaction in drug resistance.

Ginkgo biloba Extract Protects against Methotrexate-Induced Hepatotoxicity: A Computational and Pharmacological Approach

Ginkgo biloba extract possess several promising biological activities; currently, it is clinically employed in the management of several diseases. This research work aimed to extrapolate the antioxidant and anti-inflammatory effects of Ginkgo biloba (Gb) in methotrexate (MTX)-induced liver toxicity model. These effects were analyzed using different in vivo experimental approaches and by bioinformatics analysis. Male SD rats were grouped as follows: saline; MTX; Gb (pretreated for seven days with 60, 120, and 180 mg/kg daily dose before MTX treatment); silymarin (followed by MTX treatment); Gb 180 mg/kg daily only; and silymarin only. Histopathological results revealed that MTX induced marked hepatic injury, associated with a substantial surge in various hepatic enzymes such as alanine transaminase (ALT), aspartate transaminase (AST), and serum alkaline phosphatase (ALP). Furthermore, MTX caused the triggering of oxidative distress associated with a depressed antioxidant system. All these injury markers contributed to a significant release of apoptotic (caspase-3 and c-Jun N-terminal kinases (JNK)) and tumor necrosis factor (TNF-α)-like inflammatory mediators. Treatment with Gb counteracts MTX-mediated apoptosis and inflammation dose-dependently along with modulating the innate antioxidative mechanisms such as glutathione (GSH) and glutathione S-transferase (GST). These results were further supplemented by in silico study to analyze drug-receptor interactions (for several Gb constituents and target proteins) stabilized by a low energy value and with a good number of hydrogen bonds. These findings demonstrated that Gb could ameliorate MTX-induced elevated liver reactive oxygen species (ROS) and inflammation, possibly by JNK and TNF-α modulation.

Novel guanosine derivatives against Zika virus polymerase in silico

The Zika virus (ZIKV) outbreak, which started in the year 2015, is considered the fastest and most widely spread outbreak reported for this flavivirus. The polymerase domain of the NS5 protein has been targeted in other viral infections and is recognized as a suitable target in ZIKV infection. Different novel modified compounds against ZIKV NS5 have been tested in silico. A few structures have been solved for ZIKV polymerase and deposited in the protein data bank website. Two of these solved structures (with a resolution of less than 1.9  A) are used in this study to test the binding of 74 novel compounds in silico. Molecular docking is used to quantify the binding affinities of ZIKV polymerase and compare it to the hepatitis C virus NS5B. A total of 19 novel compounds revealed results that are either similar to or better than the physiological molecule, guanosine triphosphate. Water molecules are found to facilitate the binding of the compounds to ZIKV RNA-dependent RNA polymerase (RdRp) structures. The presented 19 novel compounds represent good binders to ZIKV RdRp and could be suitable candidates for developing a new and effective anti-ZIKV polymerase nucleotide inhibitor.

Structure/Activity Analysis of TASK-3 Channel Antagonists Based on a 5,6,7,8 tetrahydropyrido[4,3-d]pyrimidine

TASK-3 potassium (K⁺) channels are highly expressed in the central nervous system, regulating the membrane potential of excitable cells. TASK-3 is involved in neurotransmitter action and has been identified as an oncogenic K⁺ channel. For this reason, the understanding of the action mechanism of pharmacological modulators of these channels is essential to obtain new therapeutic strategies. In this study we describe the binding mode of the potent antagonist PK-THPP into the TASK-3 channel. PK-THPP blocks TASK-1, the closest relative channel of TASK-3, with almost nine-times less potency. Our results confirm that the binding is influenced by the fenestrations state of TASK-3 channels and occurs when they are open. The binding is mainly governed by hydrophobic contacts between the blocker and the residues of the binding site. These interactions occur not only for PK-THPP, but also for the antagonist series based on 5,6,7,8 tetrahydropyrido[4,3-d]pyrimidine scaffold (THPP series). However, the marked difference in the potency of THPP series compounds such as 20b, 21, 22 and 23 (PK-THPP) respect to compounds such as 17b, inhibiting TASK-3 channels in the micromolar range is due to the presence of a hydrogen bond acceptor group that can establish interactions with the threonines of the selectivity filter.

Drug Interactions With the Ca2+-ATPase From Sarco(Endo)Plasmic Reticulum (SERCA)

The sarco(endo)plasmic reticulum Ca²⁺-ATPase (SERCA) is an intracellular membrane transporter that utilizes the free energy provided by ATP hydrolysis for active transport of Ca²⁺ ions from the cytoplasm to the lumen of sarco(endo)plasmic reticulum. SERCA plays a fundamental role for cell calcium homeostasis and signaling in muscle cells and also in cells of other tissues. Because of its prominent role in many physiological processes, SERCA dysfunction is associated to diseases displaying various degrees of severity. SERCA transport activity can be inhibited by a variety of compounds with different chemical structures. Specific SERCA inhibitors were identified which have been instrumental in studies of the SERCA catalytic and transport mechanism. It has been proposed that SERCA inhibition may represent a novel therapeutic strategy to cure certain diseases by targeting SERCA activity in pathogens, parasites and cancer cells. Recently, novel small molecules have been developed that are able to stimulate SERCA activity. Such SERCA activators may also offer an innovative and promising therapeutic approach to treat diseases, such as heart failure, diabetes and metabolic disorders. In the present review the effects of pharmacologically relevant compounds on SERCA transport activity are presented. In particular, we will discuss the interaction of SERCA with specific inhibitors and activators that are potential therapeutic agents for different diseases.

Bovine Serum Albumin Interactions with Metal Complexes

The continuous search for new molecules with therapeutic abilities has led to the synthesis and characterization of a large number of metal complexes, proven to exhibit potential as pharmacological agents through their antibacterial, antiviral, antifungal and antineoplastic properties. As serum albumins play a key role in drug pharmacokinetics and pharmacodynamics, the study of coordination compounds affinity towards this class of proteins, as well as understanding the mechanism through which they interact is crucial. The aim of this review is to focus on the structure and biological functions of bovine serum albumin, the design of metal complexes that are able to bind to the biomolecule, as well as the experimental techniques employed in the study and evaluation of these interactions.

Interactive computer surface graphics approach to study of the active site of bovine trypsin

A descriptive medium for the presentation of protein structure has been developed and used to evaluate the structure of the active site of bovine trypsin (EC 3.4.21.4). This technique, involving advanced computer graphics technology, permits the facile display of a representation of the molecular surface of proteins of known structure and employs color to code the structural or chemical features of this surface. Benzamidine derivatives were inserted into the benzamidine-binding site of trypsin and the binary inhibitor-trypsin complex was evaluated by using the computer-generated structure. On the basis of qualitative assessments of the contribution of electrostatic and hydrophobic forces to the binding energy associated with complex formation, we made predictions concerning the effects of interaction of benzamidine substituents and amino acid side chains upon the binding energy associated with inhibitor-protein binding. The computer display of the molecular surfaces of the binary complex of substituted benzamidines and trypsin permitted unique insight into the identity and chemical properties of the atoms that participate at the interface of the molecular surfaces of the inhibitor and the protein. The computer-generated molecular surface display can potentially be combined with quantitative definition of the physical forces involved in the interaction of molecular surfaces. This technology should facilitate the study of the structure-activity relationship of substrates, inhibitors, and drugs that bind to proteins of known three-dimensional structure.