Drug-protein Binding Research Articles

Protein Binding in Translational Antimicrobial Development-Focus on Interspecies Differences.

Background/Introduction: Plasma protein binding (PPB) continues to be a key aspect of antibiotic development and clinical use. PPB is essential to understand several properties of drug candidates, including antimicrobial activity, drug-drug interaction, drug clearance, volume of distribution, and therapeutic index. Focus areas of the review: In this review, we discuss the basics of PPB, including the main drug binding proteins i.e., Albumin and α-1-acid glycoprotein (AAG). Furthermore, we present the effects of PPB on the antimicrobial activity of antibiotics and the current role of PPB in in vitro pharmacodynamic (PD) models of antibiotics. Moreover, the effect of PPB on the PK/PD of antibiotics has been discussed in this review. A key aspect of this paper is a concise evaluation of PPB between animal species (dog, rat, mouse, rabbit and monkey) and humans. Our statistical analysis of the data available in the literature suggests a significant difference between antibiotic binding in humans and that of dogs or mice, with the majority of measurements from the pre-clinical species falling within five-fold of the human plasma value. Conversely, no significant difference in binding was found between humans and rats, rabbits, or monkeys. This information may be helpful for drug researchers to select the most relevant animal species in which the metabolism of a compound can be studied for extrapolating the results to humans. Furthermore, state-of-the-art methods for determining PPB such as equilibrium dialysis, ultracentrifugation, microdialysis, gel filtration, chromatographic methods and fluorescence spectroscopy are highlighted with their advantages and disadvantages.

Epigenetic drug (XL019) JAK2 inhibitor increases mitochondrial function in brown adipocytes by upregulating mitochondrial uncoupling protein 1 (UCP1), screening of epigenetic drug libraries, cell viability, and in-silico studies

BackgroundAt present lacking of effective and safe anti-obesity drugs available leads to initiate obesity worldwide that promotes several diseases like cardiovascular diseases, liver diseases, and NASH. The development of new therapeutics is an emergency demand to cure obesity-related diseases. Mitochondrial uncoupling protein 1 (UCP1) gene could be a potential target to develop new drug moieties that can treat obesity-related diseases. MethodsWe used a GFP reporter cell line to screen epigenetic drug libraries to identify UCP1 regulators that could be effective drug candidates to treat obesity-related diseases. In this study, we employed an in-silico study that revealed drug-protein interaction and stability of drugs with protein. ResultsScreening epigenetic drug libraries, we identified XL019 significant TYK2, JAK2, and JAK3, inhibitors that can significantly promote UCP1 gene expression in brown adipocytes. Here, we found that XL019 plays a vital role to modulates mitochondrial function and could be beneficial against obesity. Further analysis shows that XL019 significantly improved mitochondrial ATP production and mitochondrial DNA copy number of adipocytes compared with the control group. The in-silico study demonstrated drug-protein interaction and binding side with UCP1 gene. Thus XL019 improves mitochondrial function that would be effective drug candidate to treat metabolic diseases and obesity-related diseases. ConclusionIn this study, we confirm the potential effect of the XL019 epigenetic drug that modulates mitochondrial function and in-silico study on drug-likeness, stability, and safety profile. Further investigation will reveal the new insight into the mechanism of action against obesity, metabolic diseases ( NASH, Fibrosis, cardiac diseases and so on), by modulation of the mitochondrial UCP1 gene and mitochondrial function.

Free energy for drug-protein binding from human-guided atomistic simulations in virtual reality - Supplementary Files

Free energy for drug-protein binding from human-guided atomistic simulations in virtual reality - Supplementary Files

Abstract 2926: Structure-based design of quadruplex-binding small molecule compounds: The essential role of water molecules

Abstract The compounds CM03 and SOP1812 are tri- and tetra-substituted naphthalene diimide derivatives with high affinity for several human DNA quadruplexes. They have been designed by computer modelling based on co-crystal structures of 1st-generation substituted naphthalene diimides with human intramolecular telomeric quadruplexes. CM03 and SOP1812 have single-digit 1-20 nM anti-proliferative activity in a panel of human pancreatic ductal adenocarcinoma (PDAC) cell lines (Marchetti et al., J. Med. Chem, 2018, 61, 2500-2517: Ahmed et al., ACS Med Chem Lett, 2020, 11, 1634-1644) and significant anti-tumor activity in the MIA-PACA2 xenograft model for PDAC as well as in the more demanding KPC model. SOP1812 is bio-available at therapeutic doses and is well tolerated at these levels in animal models. It is currently being evaluated as a candidate for clinical development by Qualigen Therapeutics Inc. The role of water in protein-drug binding is well documented and known to be important for accurate estimates of drug binding energies and for docking/virtual screening studies. To date this factor has not been seriously considered in studies of ligand-quadruplex interactions even though virtual screening has emerged as an important tool for finding new chemotypes and optimizing compound libraries in silico. A high-resolution crystal structure of a naphthalene diimide quadruplex complex (PDB id 3UYH: Micco et al., J. Med. Chem., 2013, 56, 2959-2974) has now been analyzed in detail. Patterns of water molecules have been found in each of the four quadruplex grooves, associated with the cationic side chains. Only a nitrogen atom on each N-methyl-piperazine cationic end groups is in direct hydrogen bonded contact with a phosphate group. The morpholino groups each have a small and well-defined cluster of water molecules mediating between cationic nitrogen atom and phosphate group. The rest of each groove is filled by a second cluster of waters, suggesting that larger substituents can be accommodated. Data from a library of naphthalene diimide derivatives is being examined to validate the role of these waters, especially with respect to their relative mobilities and ease of displacement. Citation Format: Stephen Neidle. Structure-based design of quadruplex-binding small molecule compounds: The essential role of water molecules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2926.

Abstract 2136: Prediction of small molecule-protein binding events for BRD4 and EGFR inhibitors using HR-LiP, a novel structural proteomics approach

Abstract Background: Beyond phenotypic efficacy and safety categorization, high resolution profiling of drug-protein interactions and binding mechanisms remains a major hurdle during lead selection and optimization. A key milestone in structure-based drug design is compound binding site identification and characterization. Structure-activity relationship (SAR) studies utilize techniques such as nuclear magnetic resonance (NMR), x-ray crystallography (X-ray), cryo-electron microscopy (cryo-EM) and the mass spectrometry-based hydrogen-deuterium exchange (HDX) to address these hurdles but they are labor, time and cost intensive. Further, SAR studies are often complicated by protein size (i.e. large proteins) and location (i.e. membrane proteins), which can lead to protocol adaptations (e.g. recombinant protein usage and/or protein truncation) that can introduce artifacts. Using limited proteolysis (LiP) coupled to next-generation mass spectrometry we have developed a high-throughput, high-resolution approach (HR-LiP) that utilizes peptide-level resolution to characterize drug-protein interactions including for proteins that hindered by the previously mentioned limitations. Methods: To boost protein abundance in their native environment, proteins of interest were overexpressed in HEK293 cells using a simple plasmid construct. Cell lysates were incubated with the compounds of interest at increasing concentrations. Samples were then subjected to a limited digest using proteinase K and further processed for data independent acquisition (DIA)-MS analysis using trypsin. Data was analyzed using a directDIA workflow in Spectronaut. Results: Two well-characterized drug target proteins, bromodomain-containing protein 4 (BRD4) and epidermal growth factor receptor (EGFR), were selected for analysis. Using HR-LiP we identify the binding site of the BRD4 inhibitor JQ1 in the full-length protein, which is typically too large to be used directly in with conventional methods. Further, we map the intracellular binding location of both gefitinib and afatinib, two inhibitors of the membrane protein EGFR. Our data for both proteins are in good accordance with orthogonal data obtained by HDX-MS, NMR and X-ray studies. Conclusions: We demonstrate that HR-LiP can be used to dissect small molecule-protein binding events, including compound binding site prediction for protein targets classically considered to be difficult. Given its biological power, broad applicability and ease of implementation, we envision the use of HR-LiP as a routine approach for target validation and lead optimization in small molecule drug discovery pipelines. Citation Format: Nigel Beaton, Jagat Adhikari, Roland Bruderer, Ron Tomlinson, Yuehan Feng, Ivan Cornella-Taracido, Lukas Reiter. Prediction of small molecule-protein binding events for BRD4 and EGFR inhibitors using HR-LiP, a novel structural proteomics approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2136.

Understanding drug-protein binding and ADME studies for DMPK.

BioanalysisVol. 14, No. 13 Technology DigestUnderstanding drug–protein binding and ADME studies for DMPKVivian XieVivian Xie*Author for correspondence: E-mail Address: v.xie@future-science-group.comFuture Science Group, Unitec House, 2 Albert Place, London, N3 1QB, UKSearch for more papers by this authorPublished Online:15 Jun 2022https://doi.org/10.4155/bio-2022-0112AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleKeywords: ADME studiesDMPKdrug–protein bindingHPLCQWBAradiolabeling

The Relationship between Hydrophobicity and Drug-Protein Binding in Human Serum Albumin: A Quartz Crystal Microbalance Study

In this paper, the quartz crystal microbalance with dissipation monitoring (QCM-D) was used to investigate hydrophobicity and binding strength (KD) for 10 different drugs interacting with human serum albumin (HSA). Quantitative structure activity relationship (QSAR) analysis was used to determine the relationship between drug hydrophobicity (ClogP) and HSA binding strength log(1/KD). The results are compared to prior knowledge on bovine serum albumin (BSA) binding. We demonstrate a positive correlation between drug hydrophobicity and the strength of ligand-protein binding to HSA and show a statistically significant similarity with the trend reported in BSA. The findings presented in this work provide insight into the role that bound water plays in ligand-protein interactions. Further, the comparison between HSA and BSA provides quantitative justification for the use of these proteins interchangeably in the analysis of drug-based binding kinetics.

Physiologic Targets and Modes of Action for CBL0137, a Lead for Human African Trypanosomiasis Drug Development.

CBL0137 is a lead drug for human African trypanosomiasis, caused by Trypanosoma brucei Herein, we use a four-step strategy to (a) identify physiologic targets and (b) determine modes of molecular action of CBL0137 in the trypanosome. First, we identified fourteen CBL0137-binding proteins using affinity chromatography. Second, we developed hypotheses of molecular modes of action, using predicted functions of CBL0137-binding proteins as guides. Third, we documented effects of CBL0137 on molecular pathways in the trypanosome. Fourth, we identified physiologic targets of the drug, by knocking down genes encoding CBL0137-binding proteins and comparing their molecular effects to those obtained when trypanosomes were treated with CBL0137. CBL0137-binding proteins included glycolysis enzymes (aldolase, glyceraldehyde-3-phosphate dehydrogenase, phosphofructokinase, phosphoglycerate kinase), and DNA-binding proteins (UMSBP2, RPA1, RPA2). In chemical biology studies CBL0137 did not reduce ATP level in the trypanosome, ruling out glycolysis enzymes as crucial targets for the drug. Thus, many CBL0137-binding proteins are not physiological targets of the drug. Instead, CBL0137 inhibited (i) nucleus mitosis, (ii) nuclear DNA replication, and (iii) polypeptide synthesis as the first carbazole inhibitor of eukaryote translation. RNAi against RPA1 inhibited both DNA synthesis and mitosis, whereas RPA2 knockdown inhibited mitosis, consistent with both proteins being physiologic targets of CBL0137. Principles used here to distinguish drug-binding proteins from physiological targets of CBL0137 can be deployed with different drugs in other biological systems. Significance Statement To distinguish drug-binding proteins from physiologic targets in the African trypanosome we devised and executed a multi-disciplinary approach involving biochemical, genetic, cell, and chemical biology experiments. The strategy we employed can be used for drugs in other biological systems.

Prediction of DNA-Binding Protein-Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature.

Identification of protein–ligand binding sites plays a critical role in drug discovery. However, there is still a lack of targeted drug prediction for DNA-binding proteins. This study aims at the binding sites of DNA-binding proteins and drugs, by mining the residue interaction network features, which can describe the local and global structure of amino acids, combined with sequence feature. The predictor of DNA-binding protein–drug-binding sites is built by employing the Extreme Gradient Boosting (XGBoost) model with random under-sampling. We found that the residue interaction network features can better characterize DNA-binding proteins, and the binding sites with high betweenness value and high closeness value are more likely to interact with drugs. The model shows that the residue interaction network features can be used as an important quantitative indicator of drug-binding sites, and this method achieves high predictive performance for the binding sites of DNA-binding protein–drug. This study will help in drug discovery research for DNA-binding proteins.

A Novel Methodology for Human Plasma Protein Binding: Prediction, Validation, and Applicability Domain

Background: Plasma protein binding is a key component in drug therapy as it affects the pharmacokinetics and pharmacodynamics of drugs. Objectives: This study aimed to predict the fraction of plasma protein binding. Methods: A quantitative structure-activity relationship, convolutional neural network, and feed-forward neural network (QSAR-CNN-FFNN) methodology was used. CNN was used for feature selection, which is known as a difficult task in QSAR studies. The values of the descriptors acquired without the preprocessing procedures were rearranged into matrices, and features from a deep fully connected layer of a pre-trained CNN (ALEXNET) were extracted. Then, the latest features learned from the CNN layers were flattened out and passed through an FFNN to make predictions. Results: The external accuracy of the validation set (Q2=0.945, RMSE=0.085) showed the performance of this methodology. Another extremely favorable circumstance of this method is that it does not take a lot of time (only a few minutes) compared to the QSAR-Wrapper-FFNN method (days of hard work and concentration) and it automatically gives us the characteristics that are the best representations of our input. Conclusion: We can say that this model can be used to predict the fraction of human plasma protein binding for drugs that have not been tested to avoid chemical synthesis and reduce expansive laboratory tests.

Estimation of Drug-Protein Binding Parameters using Amino Acids as Low Molar Mass Model Compounds: A Review

Estimation of Drug-Protein Binding Parameters using Amino Acids as Low Molar Mass Model Compounds: A Review

Estimation of Drug-Protein Binding Parameters using Amino Acids as Low Molar Mass Model Compounds: A Review

Estimation of Drug-Protein Binding Parameters using Amino Acids as Low Molar Mass Model Compounds: A Review

Glycyrrhizin (Glycyrrhizic Acid) HMGB1 (high mobility group box 1) inhibitor upregulate mitochondrial function in adipocyte, cell viability and in-silico study

BackgroundMitochondrial plays a vital role in regulating obesity and related comorbidity. Targeting mitochondrial function could be a potent therapeutic approach to inhibit metabolic-related diseases like obesity, liver disease. Prolonged use of existing drug moieties demonstrated severe adverse effects. MethodsWe apply Ucp1-A-GFP immortalized reporter cell lines and HEK293T cell lines to evaluate cell viability, mitochondrial ATP production, and the in-silico model. ResultsWe found Glycyrrhizin, an HMGB1 (high mobility group box 1) inhibitor, plays a significant role in modulating mitochondrial function against obesity. At the cellular level, the adipocytes treated with Glycyrrhizin have increased mitochondrial function. Further analysis shows that compared with the control group, the cells in the treatment group contain more mitochondria. Glycyrrhizin demonstrated a nontoxic effect on the HEK293T cell line, upregulating mitochondrial DNA and reducing mitochondrial ATP production levels. In-silico study exhibited drug-protein interaction and binding side with UCP1. ConclusionGlycyrrhizin improves mitochondrial function that would be an effective drug candidate to treat metabolic diseases and obesity-related diseases. Further investigation will require both the human and animal models to reveal new insight into the mechanism against obesity, metabolic diseases or mitochondrial dysfunction-related diseases.

Characterization of Human-malarial Parasite Species based on DHFR and GST Targets Resulting in Changes in Anti-malarial Drug Binding Conformations.

In this study, we focused primarily on three anti-malarial drugs, namely chloroquine, mefloquine, and proguanil, and these were tested against two malarial targets DHFR and GST. The species Plasmodium falciparum, Plasmodium malariae, Plasmodium ovale, Plasmodium vivax were used for the study. The purpose of this study was to determine the sequence and structural similarity of the proteins DHFR and GST among four Plasmodium species as well as to discover the in silico interactions with the aforementioned drug candidates. Bioinformatics databases, such as PDB, UniProt, DrugBank, PubChem, and tools, and software like Phyre 2.0, Clustal O (1.2.4), AutoDock 4, AutoDock Vina, and Discovery Studio Visualizer were used to determine the evolutionary significance of the Plasmodium species. The variations showed a difference in the binding patterns of drugs with our target proteins. Our finding reveals the Plasmodium spp divergence or convergence as well as the structural and sequential similarity or dissimilarity features. Our result suggests that due to the deviation in the sequences and structures, variations in protein-drug binding patterns have emerged.

Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network.

Drug discovery pipelines typically involve high-throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a "navigation" over it urges for fast and lightweight computational methods, thus promoting machine-learning approaches for processing huge pools of candidates. In this contribution, we present a graph-based deep neural network for prediction of protein-drug binding affinity and assess its predictive power under thorough testing conditions. Within the suggested approach, both protein and drug molecules are represented as graphs and passed to separate graph sub-networks, then concatenated and regressed towards a binding affinity. The neural network is trained on two binding affinity datasets-PDBbind and data imported from RCSB Protein Data Bank. In order to explore the generalization capabilities of the model we go beyond traditional random or leave-cluster-out techniques and demonstrate the need for more elaborate model performance assessment - six different strategies for test/train data partitioning (random, time- and property-arranged, protein- and ligand-clustered) with a k-fold cross-validation are engaged. Finally, we discuss the model performance in terms of a set of metrics for different split strategies and fold arrangement. Our code is available at https://github.com/SoftServeInc/affinity-by-GNN.

Sequence-based prediction of protein binding regions and drug\u2013target interactions

Identifying drug–target interactions (DTIs) is important for drug discovery. However, searching all drug–target spaces poses a major bottleneck. Therefore, recently many deep learning models have been proposed to address this problem. However, the developers of these deep learning models have neglected interpretability in model construction, which is closely related to a model’s performance. We hypothesized that training a model to predict important regions on a protein sequence would increase DTI prediction performance and provide a more interpretable model. Consequently, we constructed a deep learning model, named Highlights on Target Sequences (HoTS), which predicts binding regions (BRs) between a protein sequence and a drug ligand, as well as DTIs between them. To train the model, we collected complexes of protein–ligand interactions and protein sequences of binding sites and pretrained the model to predict BRs for a given protein sequence–ligand pair via object detection employing transformers. After pretraining the BR prediction, we trained the model to predict DTIs from a compound token designed to assign attention to BRs. We confirmed that training the BRs prediction model indeed improved the DTI prediction performance. The proposed HoTS model showed good performance in BR prediction on independent test datasets even though it does not use 3D structure information in its prediction. Furthermore, the HoTS model achieved the best performance in DTI prediction on test datasets. Additional analysis confirmed the appropriate attention for BRs and the importance of transformers in BR and DTI prediction. The source code is available on GitHub (https://github.com/GIST-CSBL/HoTS).

A multiscale molecular simulation approach for assessing the effects of mutations on functional activity and drug sensitivity in kinases

Recently, precision medicine based on an individual’s genomic background has been advanced for diverse diseases such as cancer. It has been reported that some of gene mutations identified from patients are closely associated with pathological conditions, however the functional effect of these mutations at the molecular level remains poorly understood. Here, we focus on four oncogenic kinases (EGFR, PI3Kα, ALK, and RET), and examine the effects of mutations on kinase activity and drug sensitivity by combining molecular dynamics (MD) simulation, protein-drug binding free energy computation, and quantum mechanical calculation.

Use of biomimetic chromatography and in vitro assay to develop predictive GA-MLR model for use in drug-property prediction among anti-depressant drug candidates

In recent years mental disorders, such as depression, have become an alarmingly frequent problem in modern society. Therefore, a need for novel anti-depressant agents is ever-growing. With low drug acceptance rate, industry is struggling to introduce new anti-depressant agents, and every single tool helping to change this state of things is of immense value. In this study, a set of potential novel anti-depressant derivatives with confirmed pharmacological activity was subjected to a number of assays. A group of four drug properties, assessed in early stages of drug development were chosen for those assays. Lipophilicity, membrane permeability and drug-protein binding were estimated using biomimetic chromatography methods, while metabolic stability was estimated using in-vitro enzymatic assay followed by Liquid chromatography coupled with mass spectrometry (LC-MS) analysis. Afterwards, 3D models of studied compounds were geometrically optimized using Density Functional Theory (DFT) molecular mechanics protocol in order to resemble real-state energies and structure of those derivatives. After calculating molecular descriptors from optimized 3D models, a matrix combining data from assays and molecular descriptors was created. Using Genetic Algorithm Multiple Linear Regression (GA-MLR) on this matrix allowed to create four mathematical models, one for each of the studied properties, which accurately predicts properties crucial in early drug-development stages. Such models can severely help in reducing costs of drug-development, by allowing exclusion of extremely unfavorable derivatives – even before the stage of synthesis.

Calcium chloride linked camel milk derived casein nanoparticles for the delivery of sorafenib in hepatocarcinoma cells

Sorafenib, a multikinase inhibitor used for the treatment of hepatocellular carcinoma, is limited by its low oral bioavailability. To overcome this drawback, we have developed novel camel milk casein-derived nanoparticles as a drug delivery system. Camel milk casein is not only biocompatible on oral administration but is actually a dietary protein of pharmaceutical relevance. Casein is used because of its amphiphilic nature, self-assembling property, ability to show sustained release, and capability of encapsulating both hydrophilic and hydrophobic drugs. In this study, camel milk casein nanoparticles loaded with sorafenib were developed and characterized. Characterization of casein nanoparticles was done by dynamic light scattering (DLS), zeta potential analysis, scanning light microscopy (SEM), and FTIR. The drug content in nanoparticle and drug-protein binding studies were conducted by UV spectroscopy. The cytotoxicity and cellular uptake efficiency studies were performed in HepG2 cell lines. It was observed that the cytotoxic effect of sorafenib loaded camel milk casein nanoparticles was more than free sorafenib in HepG2 cells. This work suggests camel milk casein as a suitable drug delivery molecule for sorafenib. In the future, it may also be used in enhancing the efficacy and specific distribution of other water-insoluble anticancer drugs.

Calcium chloride linked camel milk derived casein nanoparticles for the delivery of sorafenib in hepatocarcinoma cells

Sorafenib, a multikinase inhibitor used for the treatment of hepatocellular carcinoma, is limited by its low oral bioavailability. To overcome this drawback, we have developed novel camel milk casein-derived nanoparticles as a drug delivery system. Camel milk casein is not only biocompatible on oral administration but is actually a dietary protein of pharmaceutical relevance. Casein is used because of its amphiphilic nature, self-assembling property, ability to show sustained release, and capability of encapsulating both hydrophilic and hydrophobic drugs. In this study, camel milk casein nanoparticles loaded with sorafenib were developed and characterized. Characterization of casein nanoparticles was done by dynamic light scattering (DLS), zeta potential analysis, scanning light microscopy (SEM), and FTIR. The drug content in nanoparticle and drug-protein binding studies were conducted by UV spectroscopy. The cytotoxicity and cellular uptake efficiency studies were performed in HepG2 cell lines. It was observed that the cytotoxic effect of sorafenib loaded camel milk casein nanoparticles was more than free sorafenib in HepG2 cells. This work suggests camel milk casein as a suitable drug delivery molecule for sorafenib. In the future, it may also be used in enhancing the efficacy and specific distribution of other water-insoluble anticancer drugs.