Systems Biology Brings New Dimensions for Structure-Based Drug Design

Structural insights into the mechanism of human methyltransferase hPRMT4

Human PRMT4, also known as CARM1, is a type I arginine methyltransferase protein that catalyse the formation of asymmetrical dimethyl arginine product. Structural studies done to date on PRMT4 have shown that the N-terminal region, Rossmann fold and dimerization arm play an important role in PRMT4 activity. Elucidating the functions of these regions in catalysis remains to be explored. Studies have shown the existence of communication pathways in PRMT4, which need further elucidation. The molecular dynamics (MD) simulations performed in this study show differences in different monomeric and dimeric forms of hPRMT4, revealing the role of the N-terminal region, Rossmann fold and dimerization arm. The study shows the conformational changes that occur during dimerization and SAM binding. Our cross-correlation analysis showed a correlation between these regions. Further, we performed PSN and network analysis to establish the existence of communication networks and an allosteric pathway. This study shows the use of MD simulations and network analysis to explore the aspects of PRMT4 dimerization, SAM binding and demonstrates the existence of an allosteric network. These findings shed novel insights into the conformational changes associated with hPRMT4, the mechanism of its dimerization, SAM binding and clues for better inhibitor designs.Communicated by Ramaswamy H. Sarma.

In silico study of natural antioxidants

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

Hidden allosteric sites and De-Novo drug design

INTRODUCTION

Hidden allosteric sites are not visible in apo-crystal structures, but they may be visible in holo-structures when a certain ligand binds and maintains the ligand intended conformation. Several computational and experimental techniques have been used to investigate these hidden sites but identifying them remains a challenge.

AREAS COVERED

This review provides a summary of the many theoretical approaches for predicting hidden allosteric sites in disease-related proteins. Furthermore, promising cases have been thoroughly examined to reveal the hidden allosteric site and its modulator.

EXPERT OPINION

In the recent past, with the development in scientific techniques and bioinformatics tools, the number of drug targets for complex human diseases has significantly increased but unfortunately most of these targets are undruggable due to several reasons. Alternative strategies such as finding cryptic (hidden) allosteric sites are an attractive approach for exploitation of the discovery of new targets. These hidden sites are difficult to recognize compared to allosteric sites, mainly due to a lack of visibility in the crystal structure. In our opinion, after many years of development, MD simulations are finally becoming successful for obtaining a detailed molecular description of drug-target interaction.

Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions

The continuous emergence of drug-target interaction data provides an opportunity to construct a biological network for systematically discovering unknown interactions. However, this is challenging due to complex and heterogeneous correlations between drug and target. Here, we describe a heterogeneous hypergraph-based framework for drug-target interaction (HHDTI) predictions by modeling biological networks through a hypergraph, where each vertex represents a drug or a target and a hyperedge indicates existing similar interactions or associations between the connected vertices. The hypergraph is then trained to generate suitably structured embeddings for discovering unknown interactions. Comprehensive experiments performed on four public datasets demonstrate that HHDTI achieves significant and consistently improved predictions compared with state-of-the-art methods. Our analysis indicates that this superior performance is due to the ability to integrate heterogeneous high-order information from the hypergraph learning. These results suggest that HHDTI is a scalable and practical tool for uncovering novel drug-target interactions.

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A hypergraph framework to model high-order correlations in heterogenous biological network

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An embedding learning method for drugs and targets using hypergraphs

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High-order correlation between drugs and targets can contribute to DTI predictions

The prediction of drug-target interactions (DTIs) plays a crucial role in drug discovery. In this work, we discover that the high-order correlations in heterogeneous biological networks are essential for DTI predictions. The hypergraph structure is ultilized to model the high-order correlations in the biological networks, then the embeddings are generated for the drugs and targets, respectively. Finally, the interaction between them can be predicted according to the similarity of the embeddings. Our proposed method has been evaluated on multiple public datasets and the improved performance demonstrates that the high-order correlations among drugs and targets contribute significantly on DTI predictions, and other associations besides DTIs are also useful in this task.

Our method can also be used in other scenarios containing complex correlations.

Exploration of potential inhibitors for tuberculosis via structure-based drug design, molecular docking, and molecular dynamics simulation studies

Drug resistance in tuberculosis is major threat to human population. In the present investigation, we aimed to identify novel and potent benzimidazole molecules to overcome the resistance management. A series of 20 benzimidazole derivatives were examined for its activity as selective antitubercular agents. Initially, AutodockVina algorithm was performed to assess the efficacy of the molecules. The results are further enriched by redocking by means of Glide algorithm. The binding free energies of the compounds were then calculated by MM-generalized-born surface area method. Molecular docking studies elucidated that benzimidazole derivatives has revealed formation of hydrogen bond and strong binding affinity in the active site of Mycobacterium tuberculosis protein. Note that ARG308, GLY189, VAL312, LEU403, and LEU190 amino acid residues of Mycobacterium tuberculosis protein PrpR are involved in binding with ligands of benzimidazoles. Interestingly, the ligands exhibited same binding potential to the active site of protein complex PrpR in both the docking programs. In essence, the result portrays that benzimidazole derivatives such as 1p, 1q, and 1 t could be potent and selective antitubercular agents than the standard drug isoniazid. These compounds were then subjected to molecular dynamics simulation to validate the dynamics activity of the compounds against PrpR. Finally, the inhibitory behavior of compounds was predicted using a machine learning algorithm trained on a data collection of 15,000 compounds utilizing graph-based signatures. Overall, the study concludes that designed benzimidazoles can be employed as antitubercular agents. Indeed, the results are helpful for the experimental biologists to develop safe and non-toxic drugs against tuberculosis.

Identification of the molecular targets and mechanisms of compound mylabris capsules for hepatocellular carcinoma treatment through network pharmacology and bioinformatics analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese herbal formulas have been proven to exert an inhibitory effect on tumor. Compound mylabris capsules (CMC) has been used for treating cancer, especially hepatocellular carcinoma (HCC), for years in China. However, its therapeutic mechanisms on HCC remain unclear.

AIM OF THE STUDY

This research aimed to elucidate the molecular targets and mechanisms of CMC for treating HCC.

MATERIALS AND METHODS

First, the bioactive ingredients and potential targets of CMC, as well as HCC-related targets were retrieved from publicly available databases. Next, the overlapped genes between potential targets of CMC and HCC-related targets were determined using bioinformatics analysis. Then, networks of ingredient-target and gene-pathway were constructed. Finally, cell experiments were carried out to examine the effects of CMC-medicated serum on HCC and validate the core molecular targets.

RESULTS

In total, 151 bioactive ingredients and 255 potential targets of CMC were selected, 982 differentially expressed genes of HCC were identified. Among them, 34 overlapped genes were finally selected. In addition, 20 pathways and 429 GO terms were significantly enriched. Protein-protein interaction and gene-pathway networks indicated that Cyclin B1(CCNB1) and Cyclin Dependent Kinase 1(CDK1) were the core gene targets for the treatment of CMC on HCC. Moreover, in vitro studies showed that CMC-medicated serum significantly inhibited the viability of HepG2 cells. Furthermore, CMC downregulated CCNB1 and CDK1 expressions and induced G2/M phase cell cycle arrest.

CONCLUSIONS

CMC plays a therapeutic role in HCC via multi-component, -target and -pathway mechanisms, in which CCNB1 and CDK1 may be the core molecular targets. This study indicates that the integration of network pharmacology and bioinformatics analysis, followed by experimental validation, can serves as an effective tool for studying the therapeutic mechanisms of traditional Chinese medicine.

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.

PASSer: Prediction of Allosteric Sites Server

Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is a prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and protein dynamics. Here, we present an ensemble learning method, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN), to predict allosteric sites. Our model can learn physical properties and topology without any prior information, and shows good performance under multiple indicators. Prediction results showed that 84.9% of allosteric pockets in the test set appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (CLI, https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism

The current global crisis caused by COVID-19 almost halted normal life in most parts of the world. Due to the long development cycle for new drugs, drug repositioning becomes an effective method of screening drugs for COVID-19. To find suitable drugs for COVID-19, we add COVID-19-related information into our medical knowledge graph and utilize a knowledge-graph-based drug repositioning method to screen potential therapeutic drugs for COVID-19. Specific steps are as follows. Firstly, the information about COVID-19 is collected from the latest published literature, and gene targets of COVID-19 are added to the knowledge graph. Then, the information of COVID-19 of the knowledge graph is extracted and a drug–disease interaction prediction model based on Graph Convolutional Network with Attention (Att-GCN) is established. Att-GCN is used to extract features from the knowledge graph and the prediction matrix reconstructed through matrix operation. We evaluate the model by predicting drugs for both ordinary diseases and COVID-19. The model can achieve area under curve (AUC) of 0.954 and area under the precise recall area curve (AUPR) of 0.851 for ordinary diseases. On the drug repositioning experiment for COVID-19, five drugs predicted by the models have proved effective in clinical treatment. The experimental results confirm that the model can predict drug–disease interaction effectively for both normal diseases and COVID-19.

Protective Effect of Se-Methylselenocysteine on Elaidic Acid-Induced Inflammation in Human Arterial Endothelial Cells

This study was designed to investigate the anti-inflammatory effect of Se-methylselenocysteine (MSC) on elaidic acid (9t18:1, EA) induced human arterial endothelial cells (HAECs). MTT and flow cytometry were used to determine cell viability and cell apoptosis respectively. Western blotting was used to assess protein expression of intercellular adhesion molecular 1 (ICAM-1), E-selectin, interleukin-8 (IL-8), endothelial nitric oxide synthase (e-NOS) and phospholipases A2 (PLA2), while enzyme-linked immunosorbent assay (ELISA) was performed to examine the secretion level of nitric oxide (NO). In the cell viability assay, EA significantly decreased cell viability when compared with negative control (NC) group, and MSC effectively reversed this adverse effect, especially at the concentration of 200 μmol/L with 24 h incubation. Also, the same concentration of MSC prevented HAECs cell apoptosis induced by EA. In addition, we found that the expression of ICAM-1, E-selectin, IL-8 and PLA2 were significantly increased and e-NOS decreased in EA group compared with NC group. Inhibition of PLA2 promoted ICAM-1, E-slectin and IL-8 expression in HAECs induced by EA. And MSC down-regulated the secretion of NO level in EA-induced HAECs. Based on these results, we concluded that MSC activated PLA2 which regulated the expression of ICAM-1, E-selectin and IL-8 to protect inflammation induced by EA in HEACs.

Imbalance in the antioxidant defence system and pro-genotoxic status induced by high glucose concentrations: In vitro testing in human liver cells

It has been hypothesized that high glucose concentrations might contribute to the overall intracellular oxidative stress either by the direct generation of reactive oxygen species (ROS) or by altering the redox balance. Moreover, it has also been suggested that high glucose concentration can increase the susceptibility of DNA to genotoxic effects of xenobiotics. The aim of this approach was to test high glucose concentrations for pro-genotoxicity in human liver cells by setting up an in vitro model for hyperglycaemia. The experimental design included performing of tests on both human HepG2 tumour cells and HepaRG immortalized cells. Increased cell susceptibility to genotoxic xenobiotics was tested by challenging cell cultures with 4-nitroquinoline-N-oxide (4NQO) and evaluating the extent of primary DNA damage by comet assay. Moreover, we evaluated the relationship between glucose concentration and intracellular ROS, as well as the effects of glucose concentration on the induction of Nrf2-dependent genes such as Glutathione S-transferases, Heme‑oxygenase-1, and Glutathione peroxidase-4. To investigate the involvement of ROS in the induced pro-genotoxic activity, parallel experimental sets were set up by considering co-treatment of cells with the model mutagen 4NQO and the antioxidant, glutathione precursor N-acetyl-L-cysteine. High glucose concentrations caused a significant increase in the levels of primary DNA damage, with a pro-genotoxic condition closely related to the concentration of glucose in the culture medium when cells were exposed to 4NQO. High glucose concentrations also stimulated the production of ROS and down-regulated genes involved in contrasting of the effects of oxidative stress. In conclusion, in the presence of high concentrations of glucose, the cells are in unfavourable conditions for the maintenance of genome integrity.

On Calculating Free Energy Differences Using Ensembles of Transition Paths

The free energy of a process is the fundamental quantity that determines its spontaneity or propensity at a given temperature. In particular, the binding free energy of a drug candidate to its biomolecular target is used as an objective quantity in drug design. Recently, binding kinetics—rates of association (k_on) and dissociation (k_off)—have also demonstrated utility for their ability to predict efficacy and in some cases have been shown to be more predictive than the binding free energy alone. Some methods exist to calculate binding kinetics from molecular simulations, although these are typically more difficult to calculate than the binding affinity as they depend on details of the transition path ensemble. Assessing these rate constants can be difficult, due to uncertainty in the definition of the bound and unbound states, large error bars and the lack of experimental data. As an additional consistency check, rate constants from simulation can be used to calculate free energies (using the log of their ratio) which can then be compared to free energies obtained experimentally or using alchemical free energy perturbation. However, in this calculation it is not straightforward to account for common, practical details such as the finite simulation volume or the particular definition of the “bound” and “unbound” states. Here we derive a set of correction terms that can be applied to calculations of binding free energies using full reactive trajectories. We apply these correction terms to revisit the calculation of binding free energies from rate constants for a host-guest system that was part of a blind prediction challenge, where significant deviations were observed between free energies calculated with rate ratios and those calculated from alchemical perturbation. The correction terms combine to significantly decrease the error with respect to computational benchmarks, from 3.4 to 0.76 kcal/mol. Although these terms were derived with weighted ensemble simulations in mind, some of the correction terms are generally applicable to free energies calculated using physical pathways via methods such as Markov state modeling, metadynamics, milestoning, or umbrella sampling.

Identifying drug-target interactions based on graph convolutional network and deep neural network

Identification of new drug-target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug-protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, 'graph convolutional network (GCN)-DTI', for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.

Prediction of the mechanisms of Xiaoai Jiedu Recipe in the treatment of breast cancer: A comprehensive approach study with experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) holds a great promise for preventing complex chronic diseases through a holistic way. Certain Chinese medicine formulae from TCM are effective for treating and preventing cancer in clinical practice. Xiaoai Jiedu Recipe (XJR) is a Chinese medicine formula that has been used to treat breast cancer (BC). However, its active ingredients and therapeutic mechanisms on tumor are unclear. Therefore, further investigation is necessary.

AIM OF THE STUDY

This study aims to elucidate the active compounds of XJR and its molecular mechanisms for the treatment of BC.

MATERIALS AND METHODS

A comprehensive approach was used to clarify the pharmacodynamic basis of XJR and its pharmacological mechanism, including the acquisition of differentially expressed genes of BC, screening of active ingredients and their targets, construction of complex internetwork between drugs and diseases, and analysis of the key subnetwork. Finally, these results were validated by in vitro experiments and comparison with literature reviews.

RESULTS

By using bioinformatics, 5211 differentially expressed genes of BC were identified, more than half of them had been reported in previous studies. By using network analysis, 113 potential bioactive compounds in the ten component herbs of XJR and 157 BC-related targets were identified, which were significantly enriched in 85 pathways and 1321 GO terms. The in vitro studies showed that quercetin and ursolic acid, the active components of XJR, could effectively inhibit the proliferation of breast cancer cells, and the combination of the two components could significantly decrease the mitochondrial membrane potential and suppress the activation of PI3K-Akt signaling pathway, thus inducing apoptosis of cancer cells.

CONCLUSIONS

XJR played an important role in anti-BC through multi-component, multi-target and multi-pathway mechanisms, in which quercetin and ursolic acid may be the key active components. The anticancer effect of multi-component application was better than that of a single component. This study not only deepened our understanding of the role of TCM in the prevention and treatment of diseases, but also provided a reference for the in-depth research, development and application of the ancient medicine.

Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein-ligand recognition process

The designing of drugs that can simultaneously affect different protein targets is one novel and promising way to treat complex diseases. Multitarget drugs act on multiple protein receptors each implicated in the same disease state, and may be considered to be more beneficial than conventional drug therapies. For example, these drugs can have improved therapeutic potency due to synergistic effects on multiple targets, as well as improved safety and resistance profiles due to the combined regulation of potential primary therapeutic targets and compensatory elements and lower dosage typically required. This review analyzes in-silico methods that facilitate multitarget drug design that facilitate the discovery and development of novel therapeutic agents. Here presented is a summary of the progress in structure-based drug discovery techniques that study the process of molecular recognition of targets and ligands, moving from static molecular docking to improved molecular dynamics approaches in multitarget drug design, and the advantages and limitations of each.

Selenium Donors at the Junction of Inflammatory Diseases

Selenium is an essential non-metal trace element, and the imbalance in the bioavailability of selenium is associated with many diseases ranking from acute respiratory distress syndrome, myocardial infarction and renal failure (Se overloading) to diseases associated with chronic inflammation like inflammatory bowel diseases, rheumatoid arthritis, and atherosclerosis (Se unload). The only source of selenium is the diet (animal and cereal sources) and its intestinal absorption is limiting for selenocysteine and selenomethionine synthesis and incorporation in selenoproteins. In this review, after establishing the link between selenium and inflammatory diseases, we envisaged the potential of selenium nanoparticles and organic selenocompounds to compensate the deficit of selenium intake from the diet. With high selenium loading, nanoparticles offer a low dosage to restore selenium bioavailability whereas organic selenocompounds can play a role in the modulation of their antioxidant or antiinflammatory activities.

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.

AlloDriver: a method for the identification and analysis of cancer driver targets

Identifying the variants that alter protein function is a promising strategy for deciphering the biological consequences of somatic mutations during tumorigenesis, which could provide novel targets for the development of cancer therapies. Here, based on our previously developed method, we present a strategy called AlloDriver that identifies cancer driver genes/proteins as possible targets from mutations. AlloDriver utilizes structural and dynamic features to prioritize potentially functional genes/proteins in individual cancers via mapping mutations generated from clinical cancer samples to allosteric/orthosteric sites derived from three-dimensional protein structures. This strategy exhibits desirable performance in the reemergence of known cancer driver mutations and genes/proteins from clinical samples. Significantly, the practicability of AlloDriver to discover novel cancer driver proteins in head and neck squamous cell carcinoma (HNSC) was tested in a real case of human protein tyrosine phosphatase, receptor type K (PTPRK) through a L1143F driver mutation located at the allosteric site of PTPRK, which was experimentally validated by cell proliferation assay. AlloDriver is expected to help to uncover innovative molecular mechanisms of tumorigenesis by perturbing proteins and to discover novel targets based on cancer driver mutations. The AlloDriver is freely available to all users at http://mdl.shsmu.edu.cn/ALD.

Allosteric Methods and Their Applications: Facilitating the Discovery of Allosteric Drugs and the Investigation of Allosteric Mechanisms

Allostery, or allosteric regulation, is the phenomenon in which protein functional activity is altered by the binding of an effector at an allosteric site that is topographically distinct from the orthosteric, active site. As one of the most direct and efficient ways to regulate protein function, allostery has played a fundamental role in innumerable biological processes of all living organisms, including enzyme catalysis, signal transduction, cell metabolism, and gene transcription. It is thus considered as "the second secret of life". The abnormality of allosteric communication networks between allosteric and orthosteric sites is associated with the pathogenesis of human diseases. Allosteric modulators, by attaching to structurally diverse allosteric sites, offer the potential for differential selectivity and improved safety compared with orthosteric drugs that bind to conserved orthosteric sites. Harnessing allostery has thus been regarded as a novel strategy for drug discovery. Despite much progress having been made in the repertoire of allostery since the turn of the millennium, the identification of allosteric drugs for therapeutic targets and the elucidation of allosteric mechanisms still present substantial challenges. These challenges are derived from the difficulties in the identification of allosteric sites and mutations, the assessment of allosteric protein-modulator interactions, the screening of allosteric modulators, and the elucidation of allosteric mechanisms in biological systems. To address these issues, we have developed a panel of allosteric services for specific allosteric applications over the past decade, including (i) the creation of the Allosteric Database, with the aim of providing comprehensive allosteric information such as allosteric proteins, modulators, sites, pathways, etc., (ii) the construction of the ASBench benchmark of high-quality allosteric sites for the development of computational methods for predicting allosteric sites, (iii) the development of Allosite and AllositePro for the prediction of the location of allosteric sites in proteins, (iv) the development of the Alloscore scoring function for the evaluation of allosteric protein-modulator interactions, (v) the development of Allosterome for evolutionary analysis of query allosteric sites/modulators within the human proteome, (vi) the development of AlloDriver for the prediction of allosteric mutagenesis, and (vii) the development of AlloFinder for the virtual screening of allosteric modulators and the investigation of allosteric mechanisms. Importantly, we have validated computationally predicted allosteric sites, mutations, and modulators in the real cases of sirtuin 6, casein kinase 2α, phosphodiesterase 10A, and signal transduction and activation of transcription 3. Furthermore, our developed allosteric methods have been widely exploited by other users around the world for allosteric research. Therefore, these allosteric services are expected to expedite the discovery of allosteric drugs and the investigation of allosteric mechanisms.

Correlation Between Allosteric and Orthosteric Sites

Correlation between an allosteric site and its orthosteric site refers to the phenomenon that perturbations like ligand binding, mutation, or posttranslational modifications at the allosteric site leverage variation in the orthosteric site. Understanding this kind of correlation not only helps to disclose how information is transmitted in allosteric regulation but also provides clues for allosteric drug discovery. This chapter starts with an overview of correlation studies on allosteric and orthosteric sites and then introduces recent progress in evolutionary and simulation-based dynamic studies. Discussions and perspectives on future directions are also given.

Are the Apo Proteins Suitable for the Rational Discovery of Allosteric Drugs?

Allosteric modulators, by targeting the less-conserved allosteric sites, represent an innovative strategy in drug discovery. These modulators have a distinctive advantage over orthosteric ligands that attach to the conserved, functional orthosteric sites. However, in structure-based drug design, it remains unclear whether allosteric protein structures determined without orthosteric ligand binding are suitable for allosteric drug screening. In this study, we performed large-scale conformational samplings of six representative allosteric proteins uncomplexed ( apo) and complexed ( holo) with orthosteric ligands to explore the effect of orthosteric site binding on the conformational dynamics of allosteric sites. The results, coupled with the redocking evaluation of allosteric modulators to their apo and holo proteins using their MD trajectories, indicated that orthosteric site binding had an effect on the dynamics of the allosteric sites and allosteric modulators preferentially bound to their holo proteins. According to the analysis data, we constructed a new correlation model for quantifying the allosteric site change driven by substrate binding to the orthosteric site. These results highlight the strong demand to select holo allosteric proteins as initial inputs in structure-based allosteric drug screening when the distance between orthosteric and allosteric sites in the protein is below 5 Å, which is expected to contribute to allosteric drug discovery.

Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening: A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors

Accurate protein structure in the ligand-bound state is a prerequisite for successful structure-based virtual screening (SBVS). Therefore, applications of SBVS against targets for which only an apo structure is available may be severely limited. To address this constraint, we developed a computational strategy to explore the ligand-bound state of a target protein, by combined use of molecular dynamics simulation, MM/GBSA binding energy calculation, and fragment-centric topographical mapping. Our computational strategy is validated against low-molecular weight protein tyrosine phosphatase (LMW-PTP) and then successfully employed in the SBVS against protein tyrosine phosphatase receptor type O (PTPRO), a potential therapeutic target for various diseases. The most potent hit compound GP03 showed an IC₅₀ value of 2.89 μM for PTPRO and possessed a certain degree of selectivity toward other protein phosphatases. Importantly, we also found that neglecting the ligand energy penalty upon binding partially accounts for the false positive SBVS hits. The preliminary structure-activity relationships of GP03 analogs are also reported.

Activation of Glutathione Peroxidase 4 as a Novel Anti-inflammatory Strategy

The anti-oxidative enzyme, glutathione peroxidase 4 (GPX4), helps to promote inflammation resolution by eliminating oxidative species produced by the arachidonic acid (AA) metabolic network. Up-regulating its activity has been proposed as a promising strategy for inflammation intervention. In the present study, we aimed to study the effect of GPX4 activator on the AA metabolic network and inflammation related pathways. Using combined computational and experimental screen, we identified a novel compound that can activate the enzyme activity of GPX4 by more than two folds. We further assessed its potential in a series of cellular assays where GPX4 was demonstrated to play a regulatory role. We are able to show that GPX4 activation suppressed inflammatory conditions such as oxidation of AA and NF-κB pathway activation. We further demonstrated that this GPX4 activator can decrease the intracellular ROS level and suppress ferroptosis. Our study suggests that GPX4 activators can be developed as anti-inflammatory or cyto-protective agent in lipid-peroxidation-mediated diseases.

Molecular mechanism of 15-lipoxygenase allosteric activation and inhibition

Human reticulocyte 15-lipoxygenase (15-LOX) plays an important role in inflammation resolution and is also involved in many cancer-related processes. Both an activator and an inhibitor will serve as research tools for understanding the biological functions of 15-LOX and provide opportunities for drug discovery. In a previous study, both allosteric activators and inhibitors of 15-LOX were discovered through a virtual screening based computational approach. However, why molecules binding to the same site causes different effects remains to be disclosed. In the present study, we used previously reported activator and inhibitor molecules as probes to elucidate the mechanism of allosteric regulation of 15-LOX. We measured the influences of the allosteric activator and inhibitor on the enzymatic reaction rate and found that the activator increases 15-LOX activity by preventing substrate inhibition instead of increasing the turnover number. The inhibitor can also prevent substrate inhibition but decreases the turnover number at the same time, resulting in inhibition. Molecular dynamics simulations were conducted to help explain the underlying mechanism of allostery. Both the activator and inhibitor were demonstrated to be able to prevent 15-LOX from transforming into potentially inactive conformations. Compared to the activator, the inhibitor molecule restrains the motions of residues around the substrate binding site and reduces the flexibility of 15-LOX. These results explained the different effects between the activator and the inhibitor and shed light on how to effectively design novel activator molecules.

Computational Approaches in Multitarget Drug Discovery

Current therapeutic strategies entail identifying and characterizing a single protein receptor whose inhibition is likely to result in the successful treatment of a disease of interest, and testing experimentally large libraries of small molecule compounds "in vitro" and "in vivo" to identify promising inhibitors in model systems and determine if the findings are extensible to humans. This highly complex process is largely based on tests, errors, risk, time, and intensive costs. The virtual computational study of compounds simulates situations predicting possible drug linkages with multiple protein target atomic structures, taking into account the dynamic protein inhibitor, and can help identify inhibitors efficiently, particularly for complex drug-resistant diseases. Some discussions will relate to the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. Some authors have proposed a virtual drug discovery that not only identifies efficient inhibitors but also helps to minimize side effects and toxicity, thus increasing the likelihood of successful therapies. This chapter discusses concepts and research of bioactive multitargets related to toxicology.

Rare Diseases: Drug Discovery and Informatics Resource

A rare disease refers to any disease with very low prevalence individually. Although the impacted population is small for a single disease, more than 6000 rare diseases affect millions of people across the world. Due to the small market size, high cost and possibly low return on investment, only in recent years, the research and development of rare disease drugs have gradually risen globally, in several domains including gene therapy, enzyme replacement therapy, and drug repositioning. Due to the complex etiology and heterogeneous symptoms, there is a large gap between basic research and patient unmet needs for rare disease drug discovery. As computational biology increasingly arises researchers' awareness, the informatics database on rare disease have grown rapidly in the recent years, including drug targets, genetic variant and mutation, phenotype and ontology and patient registries. Along with the advances of informatics database and networks, new computational models will help accelerate the target identification and lead optimization process for rare disease pre-clinical drug development.

Structure-based drug design of novel peptidomimetic cellulose derivatives as HCV-NS3 protease inhibitors

Hepatitis C Virus (HCV) represents a global health threat not only due to the large number of reported worldwide HCV infections, but also due to the absence of a reliable vaccine for its prevention. HCV NS3 protease is one of the most important targets for drug design aiming at the deactivation of HCV. In the present work, molecular docking simulations are carried out for suggested novel NS3 protease inhibitors applied to the Egyptian genotype 4. These inhibitors are modifications of dimer cellulose by adding a hexa-peptide to the cellulose at one of the positions 2, 3, 6, 2', 3' or 6'. Results show that the inhibitor compound with the hexa-peptide at position 6 shows significantly higher simulation docking score with HCV NS3 protease active site. This is supported by low total energy value of docking system, formation of two H-bonds with HCV NS3 protease active site residues, high binding affinity and increased stability in the interaction system.

The development of small-molecule modulators for ClpP protease activity

The global spread of antibiotic resistance among important human pathogens emphasizes the need to find new antibacterial drugs with a novel mode of action. The ClpP protease has been shown to demonstrate its pivotal importance to both the survival and the virulence of pathogenic bacteria during host infection. Deregulating ClpP activity either through overactivation or inhibition could lead to antibacterial activity, declaiming the dual molecular mechanism for small-molecule modulation. Recently, natural products acyldepsipeptides (ADEPs) have been identified as a new class of antibiotics that activate ClpP to a dysfunctional state in the absence of cognate ATPases. ADEPs in combination with rifampicin eradicate deep-seated mouse biofilm infections. In addition, several non-ADEP compounds have been identified as activators of the ClpP proteolytic core without the involvement of ATPases. These findings indicate a general principle for killing dormant cells, the activation and corruption of the ClpP protease, rather than through conventional inhibition. Deletion of the clpP gene reduced the virulence of Staphylococcus aureus, thus making it an ideal antivirulence target. Multiple inhibitors have been developed in order to attenuate the production of extracellular virulence factors of bacteria through covalent modifications on serine in the active site or disruption of oligomerization of ClpP. Interestingly, due to the unusual composition and activation mechanism of ClpP in Mycobacterium tuberculosis, mycobacteria are killed by ADEPs through inhibition of ClpP activity rather than overactivation. In this short review, we will summarize recent progress in the development of small molecules modulating ClpP protease activity for both antibiotics and antivirulence.

Computational Multitarget Drug Design

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

Molecular Docking Challenges and Limitations

Today, the development of new drugs is a challenging task of science. Researchers already applied molecular docking in the drug design field to simulate ligand- receptor interactions. Docking is a term used for computational schemes that attempt to find the “best” matching between two molecules in a complex formed from constituent molecules. It has a wide range of uses and applications in drug discovery. However, some defects still exist; the accuracy and speed of docking calculation is a challenge to explore and these methods can be enhanced as a solution to docking problem. The molecular docking problem can be defined as follows: Given the atomic coordinates of two molecules, predict their “correct” bound association. The chapter discusses common challenges critical aspects of docking method such as ligand- and receptor- conformation, flexibility and cavity detection, etc. It emphasis to the challenges and inadequacies with the theories behind as well as the examples.

Rational Design of Selective Allosteric Inhibitors of PHGDH and Serine Synthesis with Anti-tumor Activity

Metabolic reprogramming in cancer cells facilitates growth and proliferation. Increased activity of the serine biosynthetic pathway through the enzyme phosphoglycerate dehydrogenase (PHGDH) contributes to tumorigenesis. With a small substrate and a weak binding cofactor, (NAD⁺), inhibitor development for PHGDH remains challenging. Instead of targeting the PHGDH active site, we computationally identified two potential allosteric sites and virtually screened compounds that can bind to these sites. With subsequent characterization, we successfully identified PHGDH non-NAD⁺-competing allosteric inhibitors that attenuate its enzyme activity, selectively inhibit de novo serine synthesis in cancer cells, and reduce tumor growth in vivo. Our study not only identifies novel allosteric inhibitors for PHGDH to probe its function and potential as a therapeutic target, but also provides a general strategy for the rational design of small-molecule modulators of metabolic enzyme function.

Exploring Molecular Mechanisms of Paradoxical Activation in the BRAF Kinase Dimers: Atomistic Simulations of Conformational Dynamics and Modeling of Allosteric Communication Networks and Signaling Pathways

The recent studies have revealed that most BRAF inhibitors can paradoxically induce kinase activation by promoting dimerization and enzyme transactivation. Despite rapidly growing number of structural and functional studies about the BRAF dimer complexes, the molecular basis of paradoxical activation phenomenon is poorly understood and remains largely hypothetical. In this work, we have explored the relationships between inhibitor binding, protein dynamics and allosteric signaling in the BRAF dimers using a network-centric approach. Using this theoretical framework, we have combined molecular dynamics simulations with coevolutionary analysis and modeling of the residue interaction networks to determine molecular determinants of paradoxical activation. We have investigated functional effects produced by paradox inducer inhibitors PLX4720, Dabrafenib, Vemurafenib and a paradox breaker inhibitor PLX7904. Functional dynamics and binding free energy analyses of the BRAF dimer complexes have suggested that negative cooperativity effect and dimer-promoting potential of the inhibitors could be important drivers of paradoxical activation. We have introduced a protein structure network model in which coevolutionary residue dependencies and dynamic maps of residue correlations are integrated in the construction and analysis of the residue interaction networks. The results have shown that coevolutionary residues in the BRAF structures could assemble into independent structural modules and form a global interaction network that may promote dimerization. We have also found that BRAF inhibitors could modulate centrality and communication propensities of global mediating centers in the residue interaction networks. By simulating allosteric communication pathways in the BRAF structures, we have determined that paradox inducer and breaker inhibitors may activate specific signaling routes that correlate with the extent of paradoxical activation. While paradox inducer inhibitors may facilitate a rapid and efficient communication via an optimal single pathway, the paradox breaker may induce a broader ensemble of suboptimal and less efficient communication routes. The central finding of our study is that paradox breaker PLX7904 could mimic structural, dynamic and network features of the inactive BRAF-WT monomer that may be required for evading paradoxical activation. The results of this study rationalize the existing structure-functional experiments by offering a network-centric rationale of the paradoxical activation phenomenon. We argue that BRAF inhibitors that amplify dynamic features of the inactive BRAF-WT monomer and intervene with the allosteric interaction networks may serve as effective paradox breakers in cellular environment.

Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine

Though many studies have been performed to elucidate molecular mechanism of traditional Chinese medicines (TCMs) by identifying protein-compound interactions, no systematic analysis at herb level was reported. TCMs are prescribed by herbs and all compounds from a certain herb should be considered as a whole, thus studies at herb level may provide comprehensive understanding of TCMs. Here, we proposed a computational strategy to study molecular mechanism of TCM at herb level and used it to analyze a TCM anti-HIV formula. Herb-target network analysis was carried out between 17 HIV-related proteins and SH formula as well as three control groups based on systematic docking. Inhibitory herbs were identified and active compounds enrichment was found to contribute to the therapeutic effectiveness of herbs. Our study demonstrates that computational analysis of TCMs at herb level can catch the rationale of TCM formulation and serve as guidance for novel TCM formula design.

Nonlinear backbone torsional pair correlations in proteins

Protein allostery requires dynamical structural correlations. Physical origin of which, however, remain elusive despite intensive studies during last two and half decades. Based on analysis of molecular dynamics (MD) simulation trajectories for ten proteins with different sizes and folds, we found that nonlinear backbone torsional pair (BTP) correlations, which are mainly spatially long-ranged and are dominantly executed by loop residues, exist extensively in most analyzed proteins. Examination of torsional motion for correlated BTPs suggested that such nonlinear correlations are mainly associated aharmonic torsional state transitions and in some cases strongly anisotropic local torsional motion of participating torsions, and occur on widely different and relatively longer time scales. In contrast, correlations between backbone torsions in stable α helices and β strands are mainly linear and spatially short-ranged, and are more likely to associate with harmonic local torsional motion. Further analysis revealed that the direct cause of nonlinear contributions are heterogeneous linear correlations. These findings implicate a general search strategy for novel allosteric modulation sites of protein activities.

Structure-based Inhibitor Design for the Intrinsically Disordered Protein c-Myc

Intrinsically disordered proteins (IDPs) are associated with various diseases and have been proposed as promising drug targets. However, conventional structure-based approaches cannot be applied directly to IDPs, due to their lack of ordered structures. Here, we describe a novel computational approach to virtually screen for compounds that can simultaneously bind to different IDP conformations. The test system used c-Myc, an oncoprotein containing a disordered basic helix-loop-helix-leucine zipper (bHLH-LZ) domain that adopts a helical conformation upon binding to Myc-associated factor X (Max). For the virtual screen, we used three binding pockets in representative conformations of c-Myc_370–409, which is part of the disordered bHLH-LZ domain. Seven compounds were found to directly bind c-Myc_370–409in vitro, and four inhibited the growth of the c-Myc-overexpressing cells by affecting cell cycle progression. Our approach of IDP conformation sampling, binding site identification, and virtual screening for compounds that can bind to multiple conformations provides a useful strategy for structure-based drug discovery targeting IDPs.

Molecular Docking Challenges and Limitations

Today, the development of new drugs is a challenging task of science. Researchers already applied molecular docking in the drug design field to simulate ligand- receptor interactions. Docking is a term used for computational schemes that attempt to find the “best” matching between two molecules in a complex formed from constituent molecules. It has a wide range of uses and applications in drug discovery. However, some defects still exist; the accuracy and speed of docking calculation is a challenge to explore and these methods can be enhanced as a solution to docking problem. The molecular docking problem can be defined as follows: Given the atomic coordinates of two molecules, predict their “correct” bound association. The chapter discusses common challenges critical aspects of docking method such as ligand- and receptor- conformation, flexibility and cavity detection, etc. It emphasis to the challenges and inadequacies with the theories behind as well as the examples.

Beyond Membrane Protein Structure: Drug Discovery, Dynamics and Difficulties

Most of the previous content of this book has focused on obtaining the structures of membrane proteins. In this chapter we explore how those structures can be further used in two key ways. The first is their use in structure based drug design (SBDD) and the second is how they can be used to extend our understanding of their functional activity via the use of molecular dynamics. Both aspects now heavily rely on computations. This area is vast, and alas, too large to consider in depth in a single book chapter. Thus where appropriate we have referred the reader to recent reviews for deeper assessment of the field. We discuss progress via the use of examples from two main drug target areas; G-protein coupled receptors (GPCRs) and ion channels. We end with a discussion of some of the main challenges in the area.

Molecular Mechanism underlying PRMT1 Dimerization for SAM Binding and Methylase Activity

Protein arginine methyltransferases (PRMTs) catalyze the posttranslational methylation of arginine, which is important in a range of biological processes, including epigenetic regulation, signal transduction, and cancer progression. Although previous studies of PRMT1 mutants suggest that the dimerization arm and the N-terminal region of PRMT1 are important for activity, the contributions of these regions to the structural architecture of the protein and its catalytic methylation activity remain elusive. Molecular dynamics (MD) simulations performed in this study showed that both the dimerization arm and the N-terminal region undergo conformational changes upon dimerization. Because a correlation was found between the two regions despite their physical distance, an allosteric pathway mechanism was proposed based on a network topological analysis. The mutation of residues along the allosteric pathways markedly reduced the methylation activity of PRMT1, which may be attributable to the destruction of dimer formation and accordingly reduced S-adenosyl-L-methionine (SAM) binding. This study provides the first demonstration of the use of a combination of MD simulations, network topological analysis, and biochemical assays for the exploration of allosteric regulation upon PRMT1 dimerization. These findings illuminate the results of mechanistic studies of PRMT1, which have revealed that dimer formation facilitates SAM binding and catalytic methylation, and provided direction for further allosteric studies of the PRMT family.

Anti-HIV Drug Discovery and Development: Current Innovations and Future Trends

The early effectiveness of combinatorial antiretroviral therapy (cART) in the treatment of HIV infection has been compromised to some extent by rapid development of multidrug-resistant HIV strains, poor bioavailability, and cumulative toxicities, and so there is a need for alternative strategies of antiretroviral drug discovery and additional therapeutic agents with novel action modes or targets. From this perspective, we first review current strategies of antiretroviral drug discovery and optimization, with the aid of selected examples from the recent literature. We highlight the development of phosphate ester-based prodrugs as a means to improve the aqueous solubility of HIV inhibitors, and the introduction of the substrate envelope hypothesis as a new approach for overcoming HIV drug resistance. Finally, we discuss future directions for research, including opportunities for exploitation of novel antiretroviral targets, and the strategy of activation of latent HIV reservoirs as a means to eradicate the virus.

AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis

BACKGROUND

Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery.

RESULTS

AlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site.

CONCLUSIONS

Perturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study.

In silico ADME/T modelling for rational drug design

In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.