Target–drug interactions: first principles and their application to drug discovery

Probing the Histamine H1 Receptor Binding Site to Explore Ligand Binding Kinetics

Analysis of structure-kinetic relationships (SKR) can contribute to an improved understanding of receptor-ligand interactions. Here, fragment 1 (4-(2-benzylphenoxy)-1-methylpiperidine) was used in different fragment growing approaches to mimic the putative binding mode of the long residence time (RT) ligands olopatadine, acrivastine, and levocetirizine at the histamine H1 receptor (H1R). SKR analyses reveal that introduction of a carboxylic acid moiety can increase RT at H1R up to 11-fold. Ligand efficiency (LE) decreases upon the introduction of the negatively charged group, whereas kinetic efficiency (KE) increases up to 8.5-fold. The olopatadine/acrivastine mimics give up to 15-fold differences in the RT, while the levocetirizine mimics afford similar RTs with only a 3-fold difference. Therefore, the levocetirizine mimics are less sensitive to structural changes. This study illustrates that for H1R, there are several ways to increase RT but the different strategies differ significantly in SKR.

Citronellol Induces Apoptosis via Differential Regulation of Caspase-3, NF-κB, and JAK2 Signaling Pathways in Glioblastoma Cell Line

Citronellol (CT) is a naturally occurring lipophilic monoterpenoid which has shown anticancer effects in numerous cancerous cell lines. This study was, therefore, designed to examine CT's potential as an anticancer agent against glioblastoma (GBM). Network pharmacology analysis was employed to identify potential anticancer targets of CT. A comprehensive data mining was carried out to assess CT and GBM-associated target genes. Protein-protein interaction network was constructed to identify hub genes and later GO and KEGG enrichment analysis was performed to elucidate the possible mechanism. Human glioblastoma cell line "SF767" was used to confirm in silico findings. MTT, crystal violet, and trypan blue assays were performed to assess the cytotoxic effects of various concentrations of CT. Subsequently, ELISA and qPCR were performed to analyze the effects of CT on proapoptotic and inflammatory mediators. In silico findings indicated that CT differentially regulated proapoptotic and inflammatory pathways by activating caspase-3 and 8 and inhibiting nuclear factor-kappa B (NF-κB), tumor necrosis factor-α, Janus kinase 2 (JAK2). Molecular docking also demonstrated strong binding affinities of CT with the above-mentioned mediators when compared to 5-fluorouracil or temozolomide. In SF767 cell line, CT displayed dose-dependent cytotoxic and antioxidant effects, and upregulation of annexin-V, caspase-3, and 8 along with downregulation of inflammatory modulators. In a nutshell, it can be concluded from these findings that CT possesses robust anticancer activity which is mediated via differential regulation of caspase-3, JAK2, and NF-κB pathways.

Integrative metabolomic-proteomic analysis uncovers a new therapeutic approach in targeting rheumatoid arthritis

OBJECTIVE

Rheumatoid arthritis (RA) is a chronic inflammatory condition that, despite available approaches to manage the disease, lacks an efficient treatment and timely diagnosis. Using the most advanced omics technique, metabolomics and proteomics approach, we explored varied metabolites and proteins to identify unique metabolite-protein signatures involved in the disease pathogenesis of RA.

METHODS

Untargeted metabolomics (n = 20) and proteomics (n = 60) of RA patients' plasma were carried out by HPLC/LC-MS/MS and SWATH, respectively and analyzed by Metaboanalyst. The targets of metabolite retrieved by PharmMapper were matched with SWATH data, and joint pathway analysis was carried out. An in-vitro study of metabolites in TNF-α induced SW982 cells was conducted by Western, RT-PCR, scratch, and ROS scavenging assay. The effect of GUDCA was also evaluated in the CIA rat model.

RESULTS

A Total of 82 metabolites and 231 differential proteins were revealed. Porphyrin and chlorophyll pathway and its metabolite Glycoursodeoxycholic acid (GUDCA) was found to be significantly altered. In vitro analysis has shown that GUDCA reduces inflammation thus offering protection against ROS production and cell proliferation. PharmMapper analysis revealed that GUDCA was significantly linked with identified SWATH proteins insulin like growth factor-1(IGF1), and Transthyretin (TTR) and it upregulates the expression of IGF1 and downregulates the expression of TTR in both in vitro and in vivo models.

CONCLUSION

GUDCA was found to possess antioxidative, antiproliferative properties and an effective anti-inflammatory property at a low dosage. It may be considered as a potential therapeutic option for reducing the inflammatory parameters associated with RA.

Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.

Enzyme-based sensor for the real-time detection of atrazine: Evidence from electrochemical and docking studies

This study focused on strategically employing the carboxylesterase enzyme Ha006a, derived from the pesticide-resistant microorganism Helicoverpa armigera, to detect atrazine. A comprehensive analysis through biochemical, biophysical and bioinformatics approaches was conducted to determine the interaction between the Ha006a protein and the herbicide atrazine. These experimental findings elucidated the potential of leveraging the inherent pesticide sequestration mechanism of the Ha006a enzyme for sensor fabrication. Numerous optimizations were undertaken to ensure the precision, reproducibility and convenient storage of the resulting electrochemical sensor, Ha006a/MCPE. This biosensor exhibited exceptional performance in detecting atrazine, demonstrating outstanding selectivity with a lower limit of detection of 5.4 µM. The developed biosensor has emerged as a reliable and cost-effective green tool for the detection of atrazine from diverse environmental samples. The Ha006a-based biosensor fabrication has expanded the possibilities for the efficient integration of insect enzymes as analytical tools, paving the way for the design of cost-effective biosensors capable of detecting and quantifying pesticides.

Calculating Protein-Ligand Residence Times through State Predictive Information Bottleneck Based Enhanced Sampling

Understanding drug residence times in target proteins is key to improving drug efficacy and understanding target recognition in biochemistry. While drug residence time is just as important as binding affinity, atomic-level understanding of drug residence times through molecular dynamics (MD) simulations has been difficult primarily due to the extremely long time scales. Recent advances in rare event sampling have allowed us to reach these time scales, yet predicting protein-ligand residence times remains a significant challenge. Here we present a semi-automated protocol to calculate the ligand residence times across 12 orders of magnitude of time scales. In our proposed framework, we integrate a deep learning-based method, the state predictive information bottleneck (SPIB), to learn an approximate reaction coordinate (RC) and use it to guide the enhanced sampling method metadynamics. We demonstrate the performance of our algorithm by applying it to six different protein-ligand complexes with available benchmark residence times, including the dissociation of the widely studied anticancer drug Imatinib (Gleevec) from both wild-type Abl kinase and drug-resistant mutants. We show how our protocol can recover quantitatively accurate residence times, potentially opening avenues for deeper insights into drug development possibilities and ligand recognition mechanisms.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Calculating Protein-Ligand Residence Times Through State Predictive Information Bottleneck based Enhanced Sampling

Understanding drug residence times in target proteins is key to improving drug efficacy and understanding target recognition in biochemistry. While drug residence time is just as important as binding affinity, atomic-level understanding of drug residence times through molecular dynamics (MD) simulations has been difficult primarily due to the extremely long timescales. Recent advances in rare event sampling have allowed us to reach these timescales, yet predicting protein-ligand residence times remains a significant challenge. Here we present a semi-automated protocol to calculate the ligand residence times across 12 orders of magnitudes of timescales. In our proposed framework, we integrate a deep learning-based method, the state predictive information bottleneck (SPIB), to learn an approximate reaction coordinate (RC) and use it to guide the enhanced sampling method metadynamics. We demonstrate the performance of our algorithm by applying it to six different protein-ligand complexes with available benchmark residence times, including the dissociation of the widely studied anti-cancer drug Imatinib (Gleevec) from both wild-type Abl kinase and drug-resistant mutants. We show how our protocol can recover quantitatively accurate residence times, potentially opening avenues for deeper insights into drug development possibilities and ligand recognition mechanisms.

QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations

Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor-ligand complexes include the size and flexibility of the ligands, large-scale conformational rearrangements of the receptor, accurate force field parameters, simulation efficiency, and sufficient sampling associated with rare events. Our recently developed multiscale milestoning simulation approach, SEEKR2 (Simulation Enabled Estimation of Kinetic Rates v.2), has demonstrated success in predicting unbinding (koff) kinetics by employing molecular dynamics (MD) simulations in regions closer to the binding site. The MD region is further subdivided into smaller Voronoi tessellations to improve the simulation efficiency and parallelization. To date, all MD simulations are run using general molecular mechanics (MM) force fields. The accuracy of calculations can be further improved by incorporating quantum mechanical (QM) methods into generating system-specific force fields through reparameterizing ligand partial charges in the bound state. The force field reparameterization process modifies the potential energy landscape of the bimolecular complex, enabling a more accurate representation of the intermolecular interactions and polarization effects at the bound state. We present QMrebind (Quantum Mechanical force field reparameterization at the receptor-ligand binding site), an ORCA-based software that facilitates reparameterizing the potential energy function within the phase space representing the bound state in a receptor-ligand complex. With SEEKR2 koff estimates and experimentally determined kinetic rates, we compare and interpret the receptor-ligand unbinding kinetics obtained using the newly reparameterized force fields for model host-guest systems and HSP90-inhibitor complexes. This method provides an opportunity to achieve higher accuracy in predicting receptor-ligand koff rate constants.

Luteolin is a potential inhibitor of COVID-19: An in silico analysis

The severe respiratory syndrome 2019 novel coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread explosively, raising global health concerns. Luteolin shows antiviral properties, but its effect on SARS-CoV-2 and the associated mechanisms are not elucidated. We used network pharmacology, molecular docking and molecular dynamics to provide potential molecular support of luteolin (3,4,5,7-tetrahydroxyflavone) (LUT) against COVID-19. We employed network pharmacology, molecular docking, and molecular dynamics techniques to investigate how LUT affected COVID-19. Several databases were queried to determine potential target proteins related to LUT and COVID-19. Protein-protein interaction network was constructed, and core targets were filtered by degree value. Following that, functional enrichment was conducted. Molecular docking was utilized to ensure LUT was compatible with core target proteins. Finally, molecular dynamics was used to analyze the effects of the LUT on the optimal hub target. A total of 64 potential target genes for treating COVID-19 were identified, of which albumin, RAC-alpha serine/threonine-protein kinase, caspase-3, epidermal growth factor receptor, heat shock protein HSP 90-alpha, and mitogen-activated protein kinase 1 might be the most promising. In addition, molecular docking results showed that LUT could interact with SARS-CoV-2 major protease 3CL. LUT can bind to the active sites of 3CL protease and mitogen-activated protein kinase 1, showing an anti-SARS-CoV-2 potential.

Affinity Chromatographic Method for Determining Drug-Protein Interaction with Enhanced Speed Than Typical Frontal Analysis

Revealing drug-protein interaction is highly important to select a drug candidate with improved drug-like properties in the early stages of drug discovery. This highlights the urgent need to develop assays that enable the analysis of drug-protein interaction with high speed. Herein, this purpose was realized by the development of an affinity chromatographic method with a two-fold higher speed than typical assays like frontal analysis and zonal elution. The method involved synthesis of a stationary phase by immobilizing poly(ADP-ribose) polymerase-1 (PARP1) onto macroporous silica gel through a one-step bioorthogonal reaction, characterization of mutual displacement interaction of two canonical drugs to the immobilized PARP1, determination of the interaction between three (iniparib, rucaparib, and olaparib) drugs and the protein, and validation of these parameters by typical frontal analysis. The numbers of binding sites on the column were (2.85 ± 0.05) × 10-7, (1.89 ± 0.71) × 10-6, and (1.49 ± 0.06) × 10-7 M for iniparib, rucaparib, and olaparib, respectively. On these sites, the association constants of the three drugs to the protein were (9.85 ± 0.56) × 104, (2.85 ± 0.34) × 104, and (1.07 ± 0.35) × 105 M-1. The determined parameters presented a good agreement with the calculation by typical frontal analyses, which indicated that the current continuous competitive frontal analysis method was reliable for determining drug-protein interaction. Application of the methods was achieved by screening tubeimosides I and II as the bioactive compounds against breast cancer in Bolbostemma paniculatum. Their mechanism may be the interference of DNA repair via down-regulating PARP1 and meiotic recombination 11 expressions, thus leading to oncogene mutations and death of cancer cells. The method was high speed since it allowed simultaneous determination of binding parameters between two drugs and a protein with a smaller number of experiments to be performed. Such a feature made the method an attractive alternative for high-speed analysis of drug-protein interaction or the other bindings in a binary system.

Farnesol dehydrogenase from Helicoverpa armigera (Hübner) as a promising target for pest management: molecular docking, in vitro and insect bioassay studies using geranylgeraniol as potential inhibitor

Juvenile hormone (JH) plays pivotal roles in several critical developmental processes in insects, including metamorphosis and reproduction. JH-biosynthetic pathway enzymes are considered highly promising targets for discovering novel insecticides. The oxidation of farnesol to farnesal, catalysed by farnesol dehydrogenase (FDL), represents a rate-limiting step in JH biosynthesis. Here, we report farnesol dehydrogenase (HaFDL) from H. armigera as a promising insecticidal target. The inhibitory potential of natural substrate analogue geranylgeraniol (GGol) was tested in vitro, wherein it showed a high binding affinity (kd 595 µM) for HaFDL in isothermal titration calorimetry (ITC) and subsequently exhibited dose-dependent enzyme inhibition in GC-MS coupled qualitative enzyme inhibition assay. Moreover, the experimentally determined inhibitory activity of GGol was augmented by the in silico molecular docking simulation which showed that GGol formed a stable complex with HaFDL, occupied the active site pocket and interacted with key active site residues (Ser147 and Tyr162) as well as other residues that are crucial in determining the active site architecture. Further, the diet-incorporated oral feeding of GGol caused detrimental effects on larval growth and development, exhibiting a significantly reduced rate of larval weight gain (P < 0.01), aberrant pupal and adult morphogenesis, and a cumulative mortality of ~ 63%. To the best of our knowledge, the study presents the first report on evaluating GGol as a potential inhibitor for HaFDL. Overall, the findings revealed the suitability of HaFDL as a potential insecticidal target for the management H. armigera.

Anti-inflammatory potential of selective small compounds by targeting TNF-α & NF-kB signaling: a comprehensive molecular docking and simulation study

Tumor necrosis factor alpha (TNF-α) is the major cause of inflammation in autoimmune diseases like rheumatoid arthritis (RA). It's mechanisms of signal transduction through nuclear factor kappa B (NF-kB) pathway via small molecules such as metabolite crosstalk are still elusive. In this study, we have targeted TNF-α and NF-kB through metabolites of RA, to inhibit TNF-α activity and deter NF-kB signaling pathways, thereby mitigating the disease severity of RA. TNF-α and NF-kB structure was obtained from PDB database and metabolites of RA were selected from literature survey. In-silico studies were carried out by molecular docking using AutoDock Vina software and further, known TNF-α and NF-kB inhibitors were compared and revealed metabolite's capacity to targets the respective proteins. Most suitable metabolite was then validated by MD simulation to verify its efficiency against TNF-α. Total 56 known differential metabolites of RA were docked with TNF-α and NF-kB compared to their corresponding inhibitor compounds. Four metabolites such as Chenodeoxycholic acid, 2-Hydroxyestrone, 2-Hydroxyestradiol (2-OHE2), and 16-Hydroxyestradiol were identified as a common TNF-α inhibitor's having binding energies ranging from -8.3 to -8.6 kcal/mol, followed by docking with NF-kB. Further, 2-OHE2 was selected because of having binding energy -8.5 kcal/mol, found to inhibit inflammation and the effectiveness was validated by root mean square fluctuation, radius of gyration and molecular mechanics with generalized born and surface area solvation against TNF-α. Thus 2-OHE2, an estrogen metabolite was identified as the potential inhibitor, attenuated inflammatory activation and can be utilized as a therapeutic target to disseminate severity of RA.

Predicting Biomolecular Binding Kinetics: A Review

Biomolecular binding kinetics including the association (k_on) and dissociation (k_off) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

Competition in drug binding and … the race to equilibrium

Binding kinetics has become a popular topic in pharmacology due to its potential contribution to the selectivity and duration of drug action. Yet, the overall kinetic aspects of complex binding mechanisms are still merely described in terms of elaborate algebraic equations. Interestingly, it has been recommended some 10 years ago to examine such mechanisms in terms of binding fluxes instead of the conventional rate constants. Alike the velocity of product formation in enzymology, those fluxes refer to the velocity by which one target species converts into another one. Novel binding flux-based approaches are utilized to get a better visual insight into the "competition" between two drugs/ligands for a single target as well as between induced fit- and conformational selection pathways for a single ligand within a thermodynamic cycle. The present data were obtained by differential equation-based simulations. Early on, the ligand-binding steps "race" to equilibrium (i.e., when their forward and reverse fluxes are equal) at their individual pace. The overall/global equilibrium is only reached later on. For the competition association assays, this parting might produce a transient "overshoot" of one of the bound target species. A similar overshoot may also show up within a thermodynamic cycle and, at first glance, suggest that the induced fit pathway dominates. Yet, present findings show that under certain circumstances, it could rather be the other way round. Novel binding flux-based approaches offer visually attractive insights into crucial aspects of "complex" binding mechanisms under non-equilibrium conditions.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis

BACKGROUND

Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.

METHODS

We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.

RESULTS

To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.

CONCLUSIONS

All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

Complete reconstruction of dasatinib unbinding pathway from c-Src kinase by supervised molecular dynamics simulation method; assessing efficiency and trustworthiness of the method

Over the past years, rational drug design has gained lots of attention since employing it gave the world targeted therapy and more effective treatment solutions. Structure-based drug design (SBDD) is an excellent tool in rational drug design that takes advantage of accurate methods such as unbiased molecular dynamics (UMD) simulation for designing and optimizing molecular entities by understanding the binding and unbinding pathways of the binders. Supervised molecular dynamics (SuMD) simulation is a branch of UMD in which long-duration simulations are turned into short simulations, called replica, and a specific parameter is monitored throughout the simulation. In this work, we utilized this strategy to reconstruct the unbinding pathway of the anticancer drug dasatinib from its target protein, the c-Src kinase. Several unbinding events with valuable details were achieved. Then, to assess the efficiency and trustworthiness of the SuMD method, the unbinding pathway was also reconstructed by conventional UMD simulation, which uncovered some of the limitations of this method, such as limited sampling of the active site and finding the metastable states in the unbinding pathway. Furthermore, in times like these, when the world is desperate to find treatments for the Covid-19 disease, we think these methods are of exceptional value.Communicated by Ramaswamy H. Sarma.

Uncovering the molecular mechanism of Gynostemma pentaphyllum (Thunb.) Makino against breast cancer using network pharmacology and molecular docking

Because of their strong anti-cancer efficacy with fewer side effects, traditional Chinese medicines (TCM) have attracted considerable attention for their potential application in treating breast cancer (BC). However, knowledge about the underlying systematic mechanisms is scarce. Gynostemma pentaphyllum (Thunb.) Makino (GP), a creeping herb, has been regularly used as a TCM to prevent and treat tumors including BC. Again, mechanisms underlying its anti-BC properties have remained elusive. We used network pharmacology and molecular docking to explore the mechanistic details of GP against BC. The TCM systems pharmacology database and analysis platform and PharmMapper Server database were used to retrieve the chemical constituents and potential targets in GP. In addition, targets related to BC were identified using DrugBank and Therapeutic Target Database. Protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of crucial targets were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins and database for annotation, visualization, and integrated discovery databases, whereas the network visualization analysis was performed using Cytoscape 3.8.2. In addition, the molecular docking technique was used to validate network pharmacology-based predictions. A comparison of the predicted targets of GP with those of BC-related drugs revealed 26 potential key targets related to the treatment of BC, among which ALB, EGFR, ESR1, AR, PGR, and HSP90AA1 were considered the major potential targets. Finally, network pharmacology-based prediction results were preliminarily verified by molecular docking experiments. In addition, chemical constituents and potential target proteins were scored, followed by a comparison with the ligands of the protein. We provide a network of pharmacology-based molecular mechanistic insights on the therapeutic action of GP against BC. We believe that our data will serve as a basis to conduct future studies and promote the clinical applications of GP.

Qualitative Estimation of Protein-Ligand Complex Stability through Thermal Titration Molecular Dynamics Simulations

The prediction of ligand efficacy has long been linked to thermodynamic properties such as the equilibrium dissociation constant, which considers both the association and the dissociation rates of a defined protein-ligand complex. In the last 15 years, there has been a paradigm shift, with an increased interest in the determination of kinetic properties such as the drug-target residence time since they better correlate with ligand efficacy compared to other parameters. In this article, we present thermal titration molecular dynamics (TTMD), an alternative computational method that combines a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints for the qualitative estimation of protein-ligand-binding stability. The protocol has been applied to four different pharmaceutically relevant test cases, including protein kinase CK1δ, protein kinase CK2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease, on a variety of ligands with different sizes, structures, and experimentally determined affinity values. In all four cases, TTMD was successfully able to distinguish between high-affinity compounds (low nanomolar range) and low-affinity ones (micromolar), proving to be a useful screening tool for the prioritization of compounds in a drug discovery campaign.

Characterizing (un)binding mechanism of USP7 inhibitors to unravel the cause of enhanced binding potencies at allosteric checkpoint

The ability to predict the intricate mechanistic behavior of ligands and associated structural determinants during protein-ligand (un)binding is of great practical importance in drug discovery. Ubiquitin specific protease-7 (USP7) is a newly emerging attractive cancer therapeutic target with bound allosteric inhibitors. However, none of the inhibitors have reached clinical trials, allowing opportunities to examine every aspect of allosteric modulation. The crystallographic insights reveal that these inhibitors have common properties such as chemical scaffolds, binding site and interaction fingerprinting. However, they still possess a broader range of binding potencies, ranging from 22 nM to 1,300 nM. Hence, it becomes more critical to decipher the structural determinants guiding the enhanced binding potency of the inhibitors. In this regard, we elucidated the atomic-level insights from both interacting partners, that is, protein-ligand perspective, and established the structure-activity link between USP7 inhibitors by using classical and advanced molecular dynamics simulations combined with linear interaction energy and molecular mechanics-Poisson Boltzmann surface area. We revealed the inhibitor potency differences by examining the contributions of chemical moieties and USP7 residues, the involvement of water-mediated interactions, and the thermodynamic landscape alterations. Additionally, the dissociation profiles aided in the establishment of a correlation between experimental potencies and structural determinants. Our study demonstrates the critical role of blocking loop 1 in allosteric inhibition and enhanced binding affinity. Comprehensively, our findings provide a constructive expansion of experimental outcomes and show the basis for varying binding potency using in-silico approaches. We expect this atomistic approach to be useful for effective drug design.

An ensemble-based drug-target interaction prediction approach using multiple feature information with data balancing

Background

Recently, drug repositioning has received considerable attention for its advantage to pharmaceutical industries in drug development. Artificial intelligence techniques have greatly enhanced drug reproduction by discovering therapeutic drug profiles, side effects, and new target proteins. However, as the number of drugs increases, their targets and enormous interactions produce imbalanced data that might not be preferable as an input to a prediction model immediately.

Methods

This paper proposes a novel scheme for predicting drug–target interactions (DTIs) based on drug chemical structures and protein sequences. The drug Morgan fingerprint, drug constitutional descriptors, protein amino acid composition, and protein dipeptide composition were employed to extract the drugs and protein’s characteristics. Then, the proposed approach for extracting negative samples using a support vector machine one-class classifier was developed to tackle the imbalanced data problem feature sets from the drug–target dataset. Negative and positive samplings were constructed and fed into different prediction algorithms to identify DTIs. A 10-fold CV validation test procedure was applied to assess the predictability of the proposed method, in addition to the study of the effectiveness of the chemical and physical features in the evaluation and discovery of the drug–target interactions.

Results

Our experimental model outperformed existing techniques concerning the curve for receiver operating characteristic (AUC), accuracy, precision, recall F-score, mean square error, and MCC. The results obtained by the AdaBoost classifier enhanced prediction accuracy by 2.74%, precision by 1.98%, AUC by 1.14%, F-score by 3.53%, and MCC by 4.54% over existing methods.

Molecular docking of DS-3032B, a mouse double minute 2 enzyme antagonist with potential for oncology treatment development

BACKGROUND

It is known that p53 suppression is an important marker of poor prognosis of cancers, especially in solid tumors of the breast, lung, stomach, and esophagus; liposarcomas, glioblastomas, and leukemias. Because p53 has mouse double minute 2 (MDM2) as its primary negative regulator, this molecular docking study seeks to answer the following hypotheses: Is the interaction between DS-3032B and MDM2 stable enough for this drug to be considered as a promising neoplastic inhibitor?

AIM

To analyze, in silico, the chemical bonds between the antagonist DS-3032B and its binding site in MDM2.

METHODS

For molecular docking simulations, the file containing structures of MDM2 (receptor) and the drug DS-3032B (ligand) were selected. The three-dimensional structure of MDM2 was obtained from Protein Data Bank, and the one for DS-3032B was obtained from PubChem database. The location and dimensions of the Grid box was determined using AutoDock Tools software. In this case, the dimensions of the Grid encompassed the entire receptor. The ligand DS-3032B interacts with the MDM2 receptor in a physiological environment with pH 7.4; thus, to simulate more reliably, its interaction was made with the calculation for the prediction of its protonation state using the MarvinSketch^® software. Both ligands, with and without the protonation, were prepared for molecular docking using the AutoDock Tools software. This software detects the torsion points of the drug and calculates the angle of the torsions. Molecular docking simulations were performed using the tools of the AutoDock platform connected to the Vina software. The analyses of the amino acid residues involved in the interactions between the receptor and the ligand as well as the twists of the ligand, atoms involved in the interactions, and type, strength, and length of the interactions were performed using the PyMol software (pymol.org/2) and Discovery Studio from BIOVIA^®.

RESULTS

The global alignment indicated crystal structure 5SWK was more suitable for docking simulations by presenting the p53 binding site. The three-dimensional structure 5SWK for MDM2 was selected from Protein Data Bank and the three-dimensional structure of DS-3032B was selected from PubChem (Compound CID: 73297272; Milademetan). After molecular docking simulations, the most stable conformer was selected for both protonated and non-protonated DS-3032B. The interaction between MDM2 and DS-3032B occurs with high affinity; no significant difference was observed in the affinity energies between the MDM2/pronated DS-3032B (-9.9 kcal/mol) and MDM2/non-protonated DS-3032B conformers (-10.0 kcal/mol). Sixteen amino acid residues of MDM2 are involved in chemical bonds with the protonated DS-3032B; these 16 residues of MDM2 belong to the p53 biding site region and provide high affinity to interaction and stability to drug-protein complex.

CONCLUSION

Molecular docking indicated that DS-3032B antagonist binds to the same region of the p53 binding site in the MDM2 with high affinity and stability, and this suggests therapeutic efficiency.

Exploring Aspartic Protease Inhibitor Binding to Design-Selective Antimalarials

Selectivity is a major issue in the development of drugs targeting pathogen aspartic proteases. Here, we explore the selectivity-determining factors by studying specifically designed malaria aspartic protease (plasmepsin) open-flap inhibitors. Metadynamics simulations are used to uncover the complex binding/unbinding pathways of these inhibitors and describe the critical transition states in atomistic resolution. The simulation results are compared with experimentally determined enzymatic activities. Our findings demonstrate that plasmepsin inhibitor selectivity can be achieved by targeting the flap loop with hydrophobic substituents that enable ligand binding under the flap loop, as such a behavior is not observed for several other aspartic proteases. The ability to estimate the selectivity of compounds before they are synthesized is of considerable importance in drug design; therefore, we expect that our approach will be useful in selective inhibitor designs against not only aspartic proteases but also other enzyme classes.

DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph

Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and targets, experimental identification of DTIs is costly and time-consuming. In recent years, the emerging graph neural network (GNN) has been applied to DTI prediction because DTIs can be represented effectively using graphs. However, some of these methods are only based on homogeneous graphs, and some consist of two decoupled steps that cannot be trained jointly. To further explore GNN-based DTI prediction by integrating heterogeneous graph information, this study regards DTI prediction as a link prediction problem and proposes an end-to-end model based on HETerogeneous graph with Attention mechanism (DTI-HETA). In this model, a heterogeneous graph is first constructed based on the drug-drug and target-target similarity matrices and the DTI matrix. Then, the graph convolutional neural network is utilized to obtain the embedded representation of the drugs and targets. To highlight the contribution of different neighborhood nodes to the central node in aggregating the graph convolution information, a graph attention mechanism is introduced into the node embedding process. Afterward, an inner product decoder is applied to predict DTIs. To evaluate the performance of DTI-HETA, experiments are conducted on two datasets. The experimental results show that our model is superior to the state-of-the-art methods. Also, the identification of novel DTIs indicates that DTI-HETA can serve as a powerful tool for integrating heterogeneous graph information to predict DTIs.

In Vitro and In Silico Analysis of the Residence Time of Serotonin 5-HT7 Receptor Ligands with Arylpiperazine Structure: A Structure-Kinetics Relationship Study

During the last decade, the kinetics of drug-target interaction has received increasing attention as an important pharmacological parameter in the drug development process. Several studies have suggested that the lipophilicity of a molecule can play an important role. To date, this aspect has been studied for several G protein-coupled receptors (GPCRs) ligands but not for the 5-HT₇ receptor (5-HT₇R), a GPCR proposed as a valid therapeutic target in neurodevelopmental and neuropsychiatric disorders associated with abnormal neuronal connectivity. In this study, we report on structure-kinetics relationships of a set of arylpiperazine-based 5-HT₇R ligands. We found that it is not the overall lipophilicity of the molecule that influences drug-target interaction kinetics but rather the position of polar groups within the molecule. Next, we performed a combination of molecular docking studies and molecular dynamics simulations to gain insights into structure-kinetics relationships. These studies did not suggest specific contact patterns between the ligands and the receptor-binding site as determinants for compounds kinetics. Finally, we compared the abilities of two 5-HT₇R agonists with similar receptor-binding affinities and different residence times to stimulate the 5-HT₇R-mediated neurite outgrowth in mouse neuronal primary cultures and found that the compounds induced the effect with different timing. This study provides the first insights into the binding kinetics of arylpiperazine-based 5-HT₇R ligands that can be helpful to design new 5-HT₇R ligands with fine-tuning of the kinetic profile.

Discovery of the signal pathways and major bioactive compounds responsible for the anti-hypoxia effect of Chinese cordyceps

ETHNOPHARMACOLOGICAL RELEVANCE

Hypoxia will cause an increase in the rate of fatigue and aging. Chinese cordyceps, a parasitic Thitarodes insect-Ophiocordyceps sinensis fungus complex in the Qinghai-Tibet Plateau, has long been used to ameliorate human conditions associated with aging and senescence, it is principally applied to treat fatigue, night sweating and other symptoms related to aging, and it may play the anti-aging and anti-fatigue effect by improving the body's hypoxia tolerance.

AIMS OF THE STUDY

The present study investigated the anti-hypoxia activity of Chinese cordyceps and explore the main corresponding signal pathways and bioactive compounds.

MATERIALS AND METHODS

In this study, network pharmacology analysis, molecular docking, cell and whole pharmacodynamic experiments were hired to study the major signal pathways and the bioactive compounds of Chinese cordyceps for anti-hypoxia activity.

RESULTS

17 pathways which Chinese cordyceps acted on seemed to be related to the anti-hypoxia effect, and "VEGF signal pathway" was one of the most important pathway. Chinese cordyceps improved the survival rate and regulated the targets related VEGF signal pathway of H9C2 cells under hypoxia, and also had significant anti-hypoxia effects to mice. Chorioallantoic membrane model experiment showed that Chinese cordyceps and the main constituents of (9Z,12Z)-octadeca-9,12-dienoic acid and cerevisterol had significant angiogenic activity in hypoxia condition.

CONCLUSION

Based on the results of network pharmacology and molecular docking analysis, cell and whole pharmacodynamic experiments, promoting angiogenesis by regulating VEGF signal pathway might be one of the mechanisms of anti-hypoxia effect of Chinese cordyceps, (9Z, 12Z)-octadeca-9,12-dienoic acid and cerevisterol were considered as the major anti-hypoxia bioactive compounds in Chinese cordyceps.

Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning

Identifying drug-target interactions is crucial for drug discovery. Despite modern technologies used in drug screening, experimental identification of drug-target interactions is an extremely demanding task. Predicting drug-target interactions in silico can thereby facilitate drug discovery as well as drug repositioning. Various machine learning models have been developed over the years to predict such interactions. Multi-output learning models in particular have drawn the attention of the scientific community due to their high predictive performance and computational efficiency. These models are based on the assumption that all the labels are correlated with each other. However, this assumption is too optimistic. Here, we address drug-target interaction prediction as a multi-label classification task that is combined with label partitioning. We show that building multi-output learning models over groups (clusters) of labels often leads to superior results. The performed experiments confirm the efficiency of the proposed framework.

Nanophotonic biosensors harnessing van der Waals materials

Low-dimensional van der Waals (vdW) materials can harness tightly confined polaritonic waves to deliver unique advantages for nanophotonic biosensing. The reduced dimensionality of vdW materials, as in the case of two-dimensional graphene, can greatly enhance plasmonic field confinement, boosting sensitivity and efficiency compared to conventional nanophotonic devices that rely on surface plasmon resonance in metallic films. Furthermore, the reduction of dielectric screening in vdW materials enables electrostatic tunability of different polariton modes, including plasmons, excitons, and phonons. One-dimensional vdW materials, particularly single-walled carbon nanotubes, possess unique form factors with confined excitons to enable single-molecule detection as well as in vivo biosensing. We discuss basic sensing principles based on vdW materials, followed by technological challenges such as surface chemistry, integration, and toxicity. Finally, we highlight progress in harnessing vdW materials to demonstrate new sensing functionalities that are difficult to perform with conventional metal/dielectric sensors.

This review presents an overview of scenarios where van der Waals (vdW) materials provide unique advantages for nanophotonic biosensing applications. The authors discuss basic sensing principles based on vdW materials, advantages of the reduced dimensionality as well as technological challenges.

Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2

Understanding mechanisms of protein folding and binding is crucial to designing their molecular function. Molecular dynamics (MD) simulations and Markov state model (MSM) approaches provide a powerful way to understand complex conformational change that occurs over long time scales. Such dynamics are important for the design of therapeutic peptidomimetic ligands, whose affinity and binding mechanism are dictated by a combination of folding and binding. To examine the role of preorganization in peptide binding to protein targets, we performed massively parallel explicit-solvent MD simulations of cyclic β-hairpin ligands designed to mimic the p53 transactivation domain and competitively bind mouse double minute 2 homologue (MDM2). Disrupting the MDM2-p53 interaction is a therapeutic strategy to prevent degradation of the p53 tumor suppressor in cancer cells. MSM analysis of over 3 ms of aggregate trajectory data enabled us to build a detailed mechanistic model of coupled folding and binding of four cyclic peptides which we compare to experimental binding affinities and rates. The results show a striking relationship between the relative preorganization of each ligand in solution and its affinity for MDM2. Specifically, changes in peptide conformational populations predicted by the MSMs suggest that entropy loss upon binding is the main factor influencing affinity. The MSMs also enable detailed examination of non-native interactions which lead to misfolded states and comparison of structural ensembles with experimental NMR measurements. In contrast to an MSM study of p53 transactivation domain (TAD) binding to MDM2, MSMs of cyclic β-hairpin binding show a conformational selection mechanism. Finally, we make progress toward predicting accurate off rates of cyclic peptides using multiensemble Markov models (MEMMs) constructed from unbiased and biased simulated trajectories.

De Novo Prediction of Drug-Target Interactions Using Laplacian Regularized Schatten -Norm Minimization

In pharmaceutical sciences, a crucial step of the drug discovery is the identification of drug-target interactions (DTIs). However, only a small portion of the DTIs have been experimentally validated. Moreover, it is an extremely laborious, expensive, and time-consuming procedure to capture new interactions between drugs and targets through traditional biochemical experiments. Therefore, designing computational methods for predicting potential interactions to guide the experimental verification is of practical significance, especially for de novo situation. In this article, we propose a new algorithm, namely Laplacian regularized Schatten p-norm minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets where there are no known interactions. Specifically, we first take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten p-norm minimization model combined with Laplacian regularization terms to improve prediction performance in the new drug/target cases. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers algorithm. We evaluate LRSpNM on five data sets and an extensive set of numerical experiments show that LRSpNM achieves better and more robust performance than five state-of-the-art DTIs prediction algorithms. In addition, we conduct two case studies for new drug and new target prediction, which illustrates that LRSpNM can successfully predict most of the experimental validated DTIs.

Virtual Screening Technique Used to Estimate the Mechanism of Adhatoda vasica Nees for the Treatment of Rheumatoid Arthritis Based on Network Pharmacology and Molecular Docking

Adhatoda vasica Nees (AVN) is commonly used to treat joint diseases such as rheumatoid arthritis (RA) in ethnic minority areas of China, especially in Tibetan and Dai areas, and its molecular mechanisms on RA still remain unclear. Network pharmacology, a novel strategy, utilizes bioinformatics to predict and evaluate drug targets and interactions in disease. Here, network pharmacology was used to investigate the mechanism by which AVN acts in RA. The chemical compositions and functional targets of AVN were retrieved using the systematic pharmacological analysis platform PharmMapper. The targets of RA were queried through the DrugBank database. The protein-protein interaction network (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of key targets were constructed in the STRING database, and the network visualization analysis was performed in Cytoscape. Maestro 11.1, a type of professional software, was used for verifying prediction and analysis based on network pharmacology. By comparing the predicted target information with the targets of RA-related drugs, 25 potential targets may be related to the treatment of RA, among which MAPK1, TNF, DHODH, IL2, PTGS2, and JAK2 may be the main potential targets for the treatment of RA. Finally, the chemical components and potential target proteins were scored by molecular docking, and compared with the ligands of the protein, the prediction results of network pharmacology were preliminarily verified. The active ingredients and mechanism of AVN against RA were firstly investigated using network pharmacology. Additionally, this research provided a solid foundation for further experimental studies.

ɑvβ3-targeted liposomal drug delivery system with attenuated immunogenicity enabled by linear pentapeptide for glioma therapy

Owing to the binding capacity to ɑ_vβ₃ integrin overexpressed on glioma, vasculogenic mimicry and neovasculature, the peptide c(RGDyK) has been exploited pervasively to functionalize nanocarriers for targeted delivery of bioactives. The former study in our group substantiated the immunotoxicity of c(RGDyK)-modified liposome, and this unfavorable immunogenicity is known to compromise blood circulation, targeting efficacy and therapeutic outcome. Therefore, we need to find a superior alternative ligand in order to evade the exquisite immuno-sensitization. We developed mn by structure-guided peptide design and retro-inverso isomerization technique, which was experimentally substantiated to have exceptional binding affinity to ɑ_vβ₃ integrin. Besides mn does not have affinity toward normal liver cells and kidney cells, which c(RGDyK) possesses in a certain degree. Warranting that mn and c(RGDyK) anchored ɑ_vβ₃, we formulated peptide-tethered liposomes and investigated in vivo bio-fate. Compared with c(RGDyK)-modified liposome, mn-modified liposome presented longer blood circulation and reduced ingestion by Kupffer cells with decreased retention in liver accordingly, benefitting from attenuated anti-liposome IgG and IgM response elicited by multiple sequential doses. Those merits strengthened the anti-glioma efficacy of ɑ_vβ₃-targeted doxorubicin-loaded liposomes, proving the importance of immunocompatibility in process of targeted drug delivery.

Mapping the S1 and S1' subsites of cysteine proteases with new dipeptidyl nitrile inhibitors as trypanocidal agents

The cysteine protease cruzipain is considered to be a validated target for therapeutic intervention in the treatment of Chagas disease. A series of 26 new compounds were designed, synthesized, and tested against the recombinant cruzain (Cz) to map its S1/S1´ subsites. The same series was evaluated on a panel of four human cysteine proteases (CatB, CatK, CatL, CatS) and Leishmania mexicana CPB, which is a potential target for the treatment of cutaneous leishmaniasis. The synthesized compounds are dipeptidyl nitriles designed based on the most promising combinations of different moieties in P1 (ten), P2 (six), and P3 (four different building blocks). Eight compounds exhibited a Ki smaller than 20.0 nM for Cz, whereas three compounds met these criteria for LmCPB. Three inhibitors had an EC50 value of ca. 4.0 μM, thus being equipotent to benznidazole according to the antitrypanosomal effects. Our mapping approach and the respective structure-activity relationships provide insights into the specific ligand-target interactions for therapeutically relevant cysteine proteases.

Drug-target interaction prediction with tree-ensemble learning and output space reconstruction

Background

Computational prediction of drug-target interactions (DTI) is vital for drug discovery. The experimental identification of interactions between drugs and target proteins is very onerous. Modern technologies have mitigated the problem, leveraging the development of new drugs. However, drug development remains extremely expensive and time consuming. Therefore, in silico DTI predictions based on machine learning can alleviate the burdensome task of drug development. Many machine learning approaches have been proposed over the years for DTI prediction. Nevertheless, prediction accuracy and efficiency are persisting problems that still need to be tackled. Here, we propose a new learning method which addresses DTI prediction as a multi-output prediction task by learning ensembles of multi-output bi-clustering trees (eBICT) on reconstructed networks. In our setting, the nodes of a DTI network (drugs and proteins) are represented by features (background information). The interactions between the nodes of a DTI network are modeled as an interaction matrix and compose the output space in our problem. The proposed approach integrates background information from both drug and target protein spaces into the same global network framework.

Results

We performed an empirical evaluation, comparing the proposed approach to state of the art DTI prediction methods and demonstrated the effectiveness of the proposed approach in different prediction settings. For evaluation purposes, we used several benchmark datasets that represent drug-protein networks. We show that output space reconstruction can boost the predictive performance of tree-ensemble learning methods, yielding more accurate DTI predictions.

Conclusions

We proposed a new DTI prediction method where bi-clustering trees are built on reconstructed networks. Building tree-ensemble learning models with output space reconstruction leads to superior prediction results, while preserving the advantages of tree-ensembles, such as scalability, interpretability and inductive setting.

A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder

Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.

Two novel SIRT1 activators, SCIC2 and SCIC2.1, enhance SIRT1-mediated effects in stress response and senescence

SIRT1, a NAD⁺-dependent deacetylase, is the most well-studied member of class III histone deacetylases. Due to its wide range of activities and substrate targets, this enzyme has emerged as a major regulator of different physiological processes. However, SIRT1-mediated alterations are also implicated in the pathogenesis of several conditions, including metabolic and neurodegenerative disorders, and cancer. Current evidence highlights the potential role of SIRT1 as an attractive therapeutic target for disease prevention and treatment strategies, thus propelling the development of new pharmacological agents. By high-throughput screening of a large library of compounds, we identified SCIC2 as an effective SIRT1 activator. This small molecule showed enzymatic activity of 135.8% at 10 μM, an AC₅₀ value of 50 ± 1.8 µM, and bound SIRT1 with a K_D of 26.4 ± 0.6 μM. In order to potentiate its SIRT1-activating ability, SCIC2 was subjected to modelling studies, leading to the identification of a more potent derivative, SCIC2.1. SCIC2.1 displayed higher SIRT1 activity (175%; AC₅₀ = 36.83 ± 2.23 µM), stronger binding to SIRT1, and greater cell permeability than SCIC2. At cellular level, both molecules did not alter the cell cycle progression of cancer cells and normal cells, and were able to strengthen SIRT1-mediated effects in stress response. Finally, SCIC2 and SCIC2.1 attenuated induction of senescence by reducing senescence-associated β-galactosidase activity. Our findings warrant further investigation of these two novel SIRT1 activators in in vivo and human studies.

Design and SAR Analysis of Covalent Inhibitors Driven by Hybrid QM/MM Simulations

Quantum mechanics/molecular mechanics (QM/MM) hybrid technique is emerging as a reliable computational method to investigate and characterize chemical reactions occurring in enzymes. From a drug discovery perspective, a thorough understanding of enzyme catalysis appears pivotal to assist the design of inhibitors able to covalently bind one of the residues belonging to the enzyme catalytic machinery. Thanks to the current advances in computer power, and the availability of more efficient algorithms for QM-based simulations, the use of QM/MM methodology is becoming a viable option in the field of covalent inhibitor design. In the present review, we summarized our experience in the field of QM/MM simulations applied to drug design problems which involved the optimization of agents working on two well-known drug targets, namely fatty acid amide hydrolase (FAAH) and epidermal growth factor receptor (EGFR). In this context, QM/MM simulations gave valuable information in terms of geometry (i.e., of transition states and metastable intermediates) and reaction energetics that allowed to correctly predict inhibitor binding orientation and substituent effect on enzyme inhibition. What is more, enzyme reaction modelling with QM/MM provided insights that were translated into the synthesis of new covalent inhibitor featured by a unique combination of intrinsic reactivity, on-target activity, and selectivity.

[Ligand-receptor binding kinetics in drug design]

Traditionally, the thermodynamic values of affinity are considered as the main criterion for the development of new drugs. Usually, these values for drugs are measured <i>in vitro</i> at steady concentrations of the receptor and ligand, which are differed from <i>in vivo</i> environment. Recent studies have shown that the kinetics of the process of drug binding to its receptor make significant contribution in the drug effectiveness. This has increased attention in characterizing and predicting the rate constants of association and dissociation of the receptor ligand at the stage of preclinical studies of drug candidates. A drug with a long residence time can determine ligand-receptor selectivity (kinetic selectivity), maintain pharmacological activity of the drug at its low concentration in vivo. The paper discusses the theoretical basis of protein-ligand binding, molecular determinants that control the kinetics of the drug-receptor binding. Understanding the molecular features underlying the kinetics of receptor-ligand binding will contribute to the rational design of drugs with desired properties.

Surprising Non-Additivity of Methyl Groups in Drug-Kinase Interaction

Drug optimization is guided by biophysical methods with increasing popularity. In the context of lead structure modifications, the introduction of methyl groups is a simple but potentially powerful approach. Hence, it is crucial to systematically investigate the influence of ligand methylation on biophysical characteristics such as thermodynamics. Here, we investigate the influence of ligand methylation in different positions and combinations on the drug-kinase interaction. Binding modes and complex structures were analyzed using protein crystallography. Thermodynamic signatures were measured via isothermal titration calorimetry (ITC). An extensive computational analysis supported the understanding of the underlying mechanisms. We found that not only position but also stereochemistry of the methyl group has an influence on binding potency as well as the thermodynamic signature of ligand binding to the protein. Strikingly, the combination of single methyl groups does not lead to additive effects. In our case, the merger of two methyl groups in one ligand leads to an entirely new alternative ligand binding mode in the protein ligand complex. Moreover, the combination of the two methyl groups also resulted in a nonadditive thermodynamic profile of ligand binding. Molecular dynamics (MD) simulations revealed distinguished characteristic motions of the ligands in solution explaining the pronounced thermodynamic changes. The unexpected drastic change in protein ligand interaction highlights the importance of crystallographic control even for minor modifications such as the introduction of a methyl group. For an in-depth understanding of ligand binding behavior, MD simulations have shown to be a powerful tool.

Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.

Network inference with ensembles of bi-clustering trees

BACKGROUND

Network inference is crucial for biomedicine and systems biology. Biological entities and their associations are often modeled as interaction networks. Examples include drug protein interaction or gene regulatory networks. Studying and elucidating such networks can lead to the comprehension of complex biological processes. However, usually we have only partial knowledge of those networks and the experimental identification of all the existing associations between biological entities is very time consuming and particularly expensive. Many computational approaches have been proposed over the years for network inference, nonetheless, efficiency and accuracy are still persisting open problems. Here, we propose bi-clustering tree ensembles as a new machine learning method for network inference, extending the traditional tree-ensemble models to the global network setting. The proposed approach addresses the network inference problem as a multi-label classification task. More specifically, the nodes of a network (e.g., drugs or proteins in a drug-protein interaction network) are modelled as samples described by features (e.g., chemical structure similarities or protein sequence similarities). The labels in our setting represent the presence or absence of links connecting the nodes of the interaction network (e.g., drug-protein interactions in a drug-protein interaction network).

RESULTS

We extended traditional tree-ensemble methods, such as extremely randomized trees (ERT) and random forests (RF) to ensembles of bi-clustering trees, integrating background information from both node sets of a heterogeneous network into the same learning framework. We performed an empirical evaluation, comparing the proposed approach to currently used tree-ensemble based approaches as well as other approaches from the literature. We demonstrated the effectiveness of our approach in different interaction prediction (network inference) settings. For evaluation purposes, we used several benchmark datasets that represent drug-protein and gene regulatory networks. We also applied our proposed method to two versions of a chemical-protein association network extracted from the STITCH database, demonstrating the potential of our model in predicting non-reported interactions.

CONCLUSIONS

Bi-clustering trees outperform existing tree-based strategies as well as machine learning methods based on other algorithms. Since our approach is based on tree-ensembles it inherits the advantages of tree-ensemble learning, such as handling of missing values, scalability and interpretability.

Reversible silencing of endogenous receptors in intact brain tissue using 2-photon pharmacology

The physiological activity of proteins is often studied with loss-of-function genetic approaches, but the corresponding phenotypes develop slowly and can be confounding. Photopharmacology allows direct, fast, and reversible control of endogenous protein activity, with spatiotemporal resolution set by the illumination method. Here, we combine a photoswitchable allosteric modulator (alloswitch) and 2-photon excitation using pulsed near-infrared lasers to reversibly silence metabotropic glutamate 5 (mGlu5) receptor activity in intact brain tissue. Endogenous receptors can be photoactivated in neurons and astrocytes with pharmacological selectivity and with an axial resolution between 5 and 10 µm. Thus, 2-photon pharmacology using alloswitch allows investigating mGlu5-dependent processes in wild-type animals, including synaptic formation and plasticity, and signaling pathways from intracellular organelles.

Kinetics of Drug Binding and Residence Time

The kinetics of drug binding and unbinding is assuming an increasingly crucial role in the long, costly process of bringing a new medicine to patients. For example, the time a drug spends in contact with its biological target is known as residence time (the inverse of the kinetic constant of the drug-target unbinding, 1/ koff). Recent reports suggest that residence time could predict drug efficacy in vivo, perhaps even more effectively than conventional thermodynamic parameters (free energy, enthalpy, entropy). There are many experimental and computational methods for predicting drug-target residence time at an early stage of drug discovery programs. Here, we review and discuss the methodological approaches to estimating drug binding kinetics and residence time. We first introduce the theoretical background of drug binding kinetics from a physicochemical standpoint. We then analyze the recent literature in the field, starting from the experimental methodologies and applications thereof and moving to theoretical and computational approaches to the kinetics of drug binding and unbinding. We acknowledge the central role of molecular dynamics and related methods, which comprise a great number of the computational methods and applications reviewed here. However, we also consider kinetic Monte Carlo. We conclude with the outlook that drug (un)binding kinetics may soon become a go/no go step in the discovery and development of new medicines.

A network pharmacology approach to investigate the blood enriching mechanism of Danggui buxue Decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Danggui buxue Decoction (DBD) has been frequently used to treat with blood deficiency, which consisted of Danggui (DG) and Huangqi (HQ) at a ratio of 1:5. Accumulating evidence showed that blood deficiency in traditional Chinese medicine (TCM) was similar to anemia in modern medicine.

AIM OF THE STUDY

The purpose of this study was to explore its therapeutic mechanism of with network pharmacology approach.

MATERIALS AND METHODS

We explored the chemical compounds of DBD and used compound ADME screening to identify the potential compounds. Targets for the therapeutic actions of DBD were obtained from the PharmMapper, Swiss, SEA and STITCH. GO analysis and pathway enrichment analysis was performed using the DAVID webserver. Cytoscape was used to visualize the compound-target-pathway network for DBD. The pharmacodynamics and crucial targets were also validated.

RESULTS

Thirty-six potential active components in DBD and 49 targets which the active components acted on were identified. 47 KEGG pathways which DBD acted on were also come to light. And then, according to KEGG pathway annotation analysis, only 16 pathways seemed to be related to the blood nourishing effect of DBD, such as PI3K-AKT pathway, and so on. Only 32 targets participated in these 16 pathways and they were acted on by 29 of the 36 active compounds. Whole pharmacodynamic experiments showed that DBD had significant effects to blood loss rats. Furthermore, DBD could promote the up-regulation of hematopoietic and immune related targets and the down-regulation of inflammatory related targets. Significantly, with the results of effective rate, molecular docking and experimental validation, we predicted astragaloside IV in HQ, senkyunolide A and senkyunolide K in DG might be the major contributing compounds to DBD's blood enriching effect.

CONCLUSION

In this study, a systematical network pharmacology approach was built. Our results provided a basis for the future study of senkyunolide A and senkyunolide K as the blood enriching compounds in DBD. Furthermore, combined network pharmacology with validation experimental results, the nourishing blood effect of DBD might be manifested by the dual mechanism of enhancing immunity and promoting hematopoiesis.

Mobility of Histidine Side Chains Analyzed with 15N NMR Relaxation and Cross-Correlation Data: Insight into Zinc-Finger-DNA Interactions

Due to chemical exchange, the mobility of histidine (His) side chains of proteins is typically difficult to analyze by NMR spectroscopy. Using an NMR approach that is uninfluenced by chemical exchange, we investigated internal motions of the His imidazole NH groups that directly interact with DNA phosphates in the Egr-1 zinc-finger-DNA complex. In this approach, the transverse and longitudinal cross-correlation rates for ¹⁵N chemical shift anisotropy and ¹⁵N-¹H dipole-dipole relaxation interference were analyzed together with ¹⁵N longitudinal relaxation rates and heteronuclear Overhauser effect data at two magnetic field strengths. We found that the zinc-coordinating His side chains directly interacting with DNA phosphates are strongly restricted in mobility. This makes a contrast to the arginine and lysine side chains that retain high mobility despite their interactions with DNA phosphates in the same complex. The entropic effects of side-chain mobility on the molecular association are discussed.

Binding kinetics of ligands acting at GPCRs

The influence of drug-receptor binding kinetics has often been overlooked during the development of new therapeutics that target G protein-coupled receptors (GPCRs). Over the last decade there has been a growing understanding that an in-depth knowledge of binding kinetics at GPCRs is required to successfully target this class of proteins. Ligand binding to a GPCR is often not a simple single step process with ligand freely diffusing in solution. This review will discuss the experiments and equations that are commonly used to measure binding kinetics and how factors such as allosteric regulation, rebinding and ligand interaction with the plasma membrane may influence these measurements. We will then consider the molecular characteristics of a ligand and if these can be linked to association and dissociation rates.

Insights into the ubiquinol/dioxygen binding and proton relay pathways of the alternative oxidase

The alternative oxidase (AOX) is a monotopic diiron carboxylate protein which catalyzes the four-electron reduction of dioxygen to water by ubiquinol. Although we have recently determined the crystal structure of Trypanosoma brucei AOX (TAO) in the presence and absence of ascofuranone (AF) derivatives (which are potent mixed type inhibitors) the mechanism by which ubiquinol and dioxygen binds to TAO remain inconclusive. In this article, ferulenol was identified as the first competitive inhibitor of AOX which has been used to probe the binding of ubiquinol. Surface plasmon resonance reveals that AF is a quasi-irreversible inhibitor of TAO whilst ferulenol binding is completely reversible. The structure of the TAO-ferulenol complex, determined at 2.7 Å, provided insights into ubiquinol binding and has also identified a potential dioxygen molecule bound in a side-on conformation to the diiron center for the first time.

Kinetic Analyses of the Siderophore Biosynthesis Inhibitor Salicyl-AMS and Analogues as MbtA Inhibitors and Antimycobacterial Agents

There is a paramount need for expanding the drug armamentarium to counter the growing problem of drug-resistant tuberculosis. Salicyl-AMS, an inhibitor of salicylic acid adenylation enzymes, is a first-in-class antibacterial lead compound for the development of tuberculosis drugs targeting the biosynthesis of salicylic-acid-derived siderophores. In this study, we determined the K_i of salicyl-AMS for inhibition of the salicylic acid adenylation enzyme MbtA from Mycobacterium tuberculosis (MbtA_tb), designed and synthesized two new salicyl-AMS analogues to probe structure-activity relationships (SAR), and characterized these two analogues alongside salicyl-AMS and six previously reported analogues in biochemical and cell-based studies. The biochemical studies included determination of kinetic parameters ( K_i^app, k_on^app, k_off, and t_R) and analysis of the mechanism of inhibition. For these studies, we optimized production and purification of recombinant MbtA_tb, for which K_m and k_cat values were determined, and used the enzyme in conjunction with an MbtA_tb-optimized, continuous, spectrophotometric assay for MbtA activity and inhibition. The cell-based studies provided an assessment of the antimycobacterial activity and postantibiotic effect of the nine MbtA_tb inhibitors. The antimycobacterial properties were evaluated using a strain of nonpathogenic, fast-growing Mycobacterium smegmatis that was genetically engineered for MbtA_tb-dependent susceptibility to MbtA inhibitors. This convenient model system greatly facilitated the cell-based studies by bypassing the methodological complexities associated with the use of pathogenic, slow-growing M. tuberculosis. Collectively, these studies provide new information on the mechanism of inhibition of MbtA_tb by salicyl-AMS and eight analogues, afford new SAR insights for these inhibitors, and highlight several suitable candidates for future preclinical evaluation.