Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.     

Systematic identification of proteins that elicit drug side effects

Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large‐scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug–target relations to identify overrepresented protein–side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.     

Systematic Prediction of Pharmacodynamic Drug-Drug Interactions through Protein-Protein-Interaction Network

Identifying drug-drug interactions (DDIs) is a major challenge in drug development. Previous attempts have established formal approaches for pharmacokinetic (PK) DDIs, but there is not a feasible solution for pharmacodynamic (PD) DDIs because the endpoint is often a serious adverse event rather than a measurable change in drug concentration. Here, we developed a metric “S-score” that measures the strength of network connection between drug targets to predict PD DDIs. Utilizing known PD DDIs as golden standard positives (GSPs), we observed a significant correlation between S-score and the likelihood a PD DDI occurs. Our prediction was robust and surpassed existing methods as validated by two independent GSPs. Analysis of clinical side effect data suggested that the drugs having predicted DDIs have similar side effects. We further incorporated this clinical side effects evidence with S-score to increase the prediction specificity and sensitivity through a Bayesian probabilistic model. We have predicted 9,626 potential PD DDIs at the accuracy of 82% and the recall of 62%. Importantly, our algorithm provided opportunities for better understanding the potential molecular mechanisms or physiological effects underlying DDIs, as illustrated by the case studies.     

Perturbation of the mutated EGFR interactome identifies vulnerabilities and resistance mechanisms

We hypothesized that elucidating the interactome of epidermal growth factor receptor (EGFR) forms that are mutated in lung cancer, via global analysis of protein–protein interactions, phosphorylation, and systematically perturbing the ensuing network nodes, should offer a new, more systems‐level perspective of the molecular etiology. Here, we describe an EGFR interactome of 263 proteins and offer a 14‐protein core network critical to the viability of multiple EGFR‐mutated lung cancer cells. Cells with acquired resistance to EGFR tyrosine kinase inhibitors (TKIs) had differential dependence of the core network proteins based on the underlying molecular mechanisms of resistance. Of the 14 proteins, 9 are shown to be specifically associated with survival of EGFR‐mutated lung cancer cell lines. This included EGFR, GRB2, MK12, SHC1, ARAF, CD11B, ARHG5, GLU2B, and CD11A. With the use of a drug network associated with the core network proteins, we identified two compounds, midostaurin and lestaurtinib, that could overcome drug resistance through direct EGFR inhibition when combined with erlotinib. Our results, enabled by interactome mapping, suggest new targets and combination therapies that could circumvent EGFR TKI resistance.     

New insights into cancer-related proteins provided by the yeast model

Cancer is a devastating disease with a profound impact on society. In recent years, yeast has provided a valuable contribution with respect to uncovering the molecular mechanisms underlying this disease, allowing the identification of new targets and novel therapeutic opportunities. Indeed, several attributes make yeast an ideal model system for the study of human diseases. It combines a high level of conservation between its cellular processes and those of mammalian cells, with advantages such as a short generation time, ease of genetic manipulation and a wealth of experimental tools for genome- and proteome-wide analyses. Additionally, the heterologous expression of disease-causing proteins in yeast has been successfully used to gain an understanding of the functions of these proteins and also to provide clues about the mechanisms of disease progression. Yeast research performed in recent years has demonstrated the tremendous potential of this model system, especially with the validation of findings obtained with yeast in more physiologically relevant models. The present review covers the major aspects of the most recent developments in the yeast research area with respect to cancer. It summarizes our current knowledge on yeast as a cellular model for investigating the molecular mechanisms of action of the major cancer-related proteins that, even without yeast orthologues, still recapitulate in yeast some of the key aspects of this cellular pathology. Moreover, the most recent contributions of yeast genetics and high-throughput screening technologies that aim to identify some of the potential causes underpinning this disorder, as well as discover new therapeutic agents, are discussed.     

Triptolide prevents bone loss via suppressing osteoclastogenesis through inhibiting PI3K-AKT-NFATc1 pathway

Bone loss (osteopenia) is a common complication in human solid tumour. In addition, after surgical treatment of gynaecological tumour, osteoporosis often occurs due to the withdrawal of oestrogen. The major characteristic of osteoporosis is the low bone mass with micro-architectural deteriorated bone tissue. And the main cause is the overactivation of osteoclastogenesis, which is one of the most important therapeutic targets. Inflammation could induce the interaction of RANKL/RANK, which is the promoter of osteoclastogenesis. Triptolide is derived from the traditional Chinese herb lei gong teng, presented multiple biological effects, including anti-cancer, anti-inflammation and immunosuppression. We hypothesized that triptolide could inhibits osteoclastogenesis by suppressing inflammation activation. In this study, we confirmed that triptolide could suppress RANKL-induced osteoclastogenesis in bone marrow mononuclear cells (BMMCs) and RAW264.7 cells and inhibited the osteoclast bone resorption functions. PI3K-AKT-NFATc1 pathway is one of the most important downstream pathways of RANKL-induced osteogenesis. The experiments in vitro indicated that triptolide suppresses the activation of PI3K-AKT-NFATc1 pathway and the target point located at the upstream of AKT because both NFATc1 overexpression and AKT phosphorylation could ameliorate the triptolide suppression effects. The expression of MDM2 was elevated, which demonstrated the MDM-p53-induced cell death might contribute to the osteoclastogenesis suppression. Ovariectomy-induced bone loss and inflammation activation were also found to be ameliorated in the experiments in vivo. In summary, the new effect of anti-cancer drug triptolide was demonstrated to be anti-osteoclastogenesis, and we demonstrated triptolide might be a promising therapy for bone loss caused by tumour.     

Metabolomics and Proteomics Annotate Therapeutic Properties of Geniposide: Targeting and Regulating Multiple Perturbed Pathways

Geniposide is an important constituent of Gardenia jasminoides Ellis, a famous Chinese medicinal plant, and has displayed bright prospects in prevention and therapy of hepatic injury (HI). Unfortunately, the working mechanisms of this compound are difficult to determine and thus remain unknown. To determine the mechanisms that underlie this compound, we conducted a systematic analysis of the therapeutic effects of geniposide using biochemistry, metabolomics and proteomics. Geniposide significantly intensified the therapeutic efficacy as indicated by our modern biochemical analysis. Metabolomics results indicate 9 ions in the positive mode as differentiating metabolites which were associated with perturbations in primary bile acid biosynthesis, butanoate metabolism, citrate cycle (TCA cycle), alanine, aspartate and glutamate metabolism. Of note, geniposide has potential pharmacological effect through regulating multiple perturbed pathways to normal state. In an attempt to address the benefits of geniposide based on the proteomics approaches, the protein-interacting networks were constructed to aid identifying the drug targets of geniposide. Six identified differential proteins appear to be involved in antioxidation and signal transduction, energy production, immunity, metabolism, chaperoning. These proteins were closely related in the protein-protein interaction network and the modulation of multiple vital physiological pathways. These data will help to understand the molecular therapeutic mechanisms of geniposide on hepatic damage rats. We also conclude that metabolomics and proteomics are powerful and versatile tools for both biomarker discovery and exploring the complex relationships between biological pathways and drug response, highlighting insights into drug discovery.     

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.     

Aromatic side-chain contributions to the far ultraviolet circular dichroism of peptides and proteins

The rotational strength of the L_a transition in phenylalanine and tyrosine side chains has been calculated for dipeptides with various backbone and side-chain conformations. Similar calculations have also been performed for tripeptides in the β-turn conformation with aromatic residues at the corners of the turn. The interaction of the aromatic ring with neighboring peptides generates rotational strengths in the L_a transition of the order of 0.1 Debye-Bohr magneton. When the preferred backbone and side-chain conformations are considered, it is found that the most probable conformations have positive L_a bonds. This result accounts for the observation that the N-acyl amino acid amides of L -Tyr and L Phe have positive L_a bands. It also suggests that, although other interactions may affect the numerical value and even the sign, there will be a significant positive contribution to the rotational strength of aromatic residues in globular proteins from nearest-neighbor interactions. Calculations on proteins of known conformation at the nearest-neighbor level confirm the tendency toward positive L_a contributions for Phe and Tyr residues. This contribution can be of the order of 10% of the observed CD even in proteins with rather strong amide contributions. In some proteins, such as the gene 5 protein from bacteriophage fd and many snake-venom toxins, side-chain contributions from Tyr and Trp residues manifest themselves as positive CD bands in the 225–250-nm region. The magnitude of the nearest-neighbor contributions and the trend toward positive contributions are consistent with the observation of such CD bands in globular proteins. No special stacking interaction among aromatic side chains needs to be invoked.     

Use of X-ray scattering to aid the design and delivery of membrane-active drugs

Biological membranes can be targets for compounds that either disrupt their barrier function or affect protein function via membrane-mediated processes. Biophysical studies on membrane-mimetic systems composed of membrane lipids have contributed substantially to our knowledge on the pertaining membrane physics and aid the development of membrane-specific drugs. Moreover, lipid membranes and, in particular, liposomes are convenient systems for drug delivery. We review some of our recent work that demonstrates the applicability of X-ray scattering to understanding the molecular mechanisms of drug–membrane interactions. In particular we focus on effects of anesthetics, sphingomyelinase, and antimicrobial peptides. We further discuss X-ray scattering as a quality-control tool for liposomal drug-delivery systems.     

Drug-target network

The global set of relationships between protein targets of all drugs and all disease-gene products in the human protein-protein interaction or 'interactome' network remains uncharacterized. We built a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend toward more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease-gene products, we measured the shortest distance between both sets of proteins in current models of the human interactome network. Significant differences in distance were found between etiological and palliative drugs. A recent trend toward more rational drug design was observed.     

Mass spectrometry‐based protein–protein interaction networks for the study of human diseases

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein–protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)‐based approaches have allowed unbiased mapping of these disease‐mediated changes in protein–protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein–protein interactions at a system‐level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS‐based protein–protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.     

Computer search for molecular mechanisms of ulcerogenic action of non-steroidal anti-inflammatory drugs

Peptic ulcers are the most frequent side effect of therapy with non-steroidal anti-inflammatory drugs (NSAIDs). Good experimental evidence exists that pathogenesis of peptic ulcers cannot be attributed only to inhibition of cyclooxygenases. The knowledge about other molecular mechanisms of drug action associated with development of peptic ulcers could be useful for design of new safer NSAIDs. However, considerable time and material resources are needed for corresponding experimental studies. For simplification of the experimental search, we have developed an approach for in silico identification of putative molecular mechanisms of drug actions associated with their side effects. We have generated a data set of 85 NSAIDs, which includes information about their structures and side effects. Unknown molecular mechanisms of action of these NSAIDs were evaluated by the computer program PASS (Prediction of Activity Spectra for Substances) predicting more than 3000 molecular mechanisms of action based on structural formula of sub-stances. Statistically significant associations have been found between predicted molecular mechanisms of action and development of peptic ulcers. Twenty six molecular mechanisms of action probably associated with development of peptic ulcers have been found: two of them were known previously and 24 were quite new. Analyzing Gene Ontology data, data on signal and metabolic pathways, and available MEDLINE publication data, we proposed hypotheses on the role of 10 molecular mechanisms of action in the pathogenesis of peptic ulcer.     

The Ubiquitination-Dependent and -Independent Functions of Cereblon in Cancer and Neurological Diseases

Cereblon (CRBN) mediates the teratogenic effect of thalidomide in zebrafish, chickens, and humans. It additionally modulates the anti-myeloma effect of the immunomodulatory drugs (IMiDs) thalidomide, lenalidomide, and pomalidomide. IMiDs bind to CRBN and recruit neo-substrates for their ubiquitination and proteasome-mediated degradation, which significantly expands the application of proteolysis-targeting chimeras (PROTACs) for targeted drug discovery. However, the underlying molecular mechanisms by which CRBN mediates the teratogenicity and anti-myeloma effect of IMiDs have not been fully elucidated. Furthermore, the normal physiological functions of endogenous CRBN have not been extensively studied, which prevents the thorough assessment of side effects of the CRBN ligand-based PROTACs in the treatment of cancer and neurological diseases. To advance our understanding of the diverse functions of CRBN, in this review, we will survey the ubiquitination-dependent and -independent functions of CRBN, summarize recent advances in the discovery of constitutive substrates and neo-substrates of CRBN, and explore the molecular functions of CRBN in cancer treatment and in the development of neurological diseases. We will also discuss the potential future directions toward the identification of CRBN substrates/interacting proteins and CRBN ligand-based drug discovery in the treatment of cancer and neurological diseases.     

Effects of extremely high-frequency electromagnetic radiation on the immune system and systemic regulation of homeostasis

Low-intensity of electromagnetic radiation of extremely high frequencies (EHF EMR) is effectively used in medical practice for diagnostics, prevention and treatment of a broad spectrum of diseases of different etiology. However, in spite of existence of many hypotheses about mechanisms of EHF EMR effects on the molecular and cellular levels of organization of living systems, there is not conception that could explain all diversity of the EHF-therapy effects from unified approach. In our opinion, the problem of determination of mechanisms of EHF EMR effects on living organism is divided into two basic tasks: first, determining subcellular structures which can receive radiation, and, second, studying physiological reactions of the organism which are caused by radiation. It is obviously, that investigation of functions of single cells and subcellular elements can not entirely explain therapeutic effects and mechanisms of EHF EMR influence on multicellular organism on the whole. Plenty of functional relationships between organs and systems of organs should be taken into account. In the present review, a realization of the EHF-therapy effects due to the influence on immune system functions and start of system mechanisms of maintenance of the homeostasis on the organism level is hypothesized. Potential targets for EHF EMR acception on the level of different systems of the organism are analysed. The material is formed so that functional relations between immune system and other regulatory systems (nervous and endocrine systems) are traced.     

Evaluating the Pharmacological Mechanism of Chinese Medicine Si-Wu-Tang through Multi-Level Data Integration

Si-Wu-Tang (SWT) is a Traditional Chinese Medicine (TCM) formula widely used for the treatments of gynecological diseases. To explore the pharmacological mechanism of SWT, we incorporated microarray data of SWT with our herbal target database TCMID to analyze the potential activity mechanism of SWT''s herbal ingredients and targets. We detected 2,405 differentially expressed genes in the microarray data, 20 of 102 proteins targeted by SWT were encoded by these DEGs and can be targeted by 2 FDA-approved drugs and 39 experimental drugs. The results of pathway enrichment analysis of the 20 predicted targets were consistent with that of 2,405 differentially expressed genes, elaborating the potential pharmacological mechanisms of SWT. Further study from a perspective of protein-protein interaction (PPI) network showed that the predicted targets of SWT function cooperatively to perform their multi-target effects. We also constructed a network to combine herbs, ingredients, targets and drugs together which bridges the gap between SWT and conventional medicine, and used it to infer the potential mechanisms of herbal ingredients. Moreover, based on the hypothesis that the same or similar effects between different TCM formulae may result from targeting the same proteins, we analyzed 27 other TCM formulae which can also treat the gynecological diseases, the subsequent result provides additional insight to understand the potential mechanisms of SWT in treating amenorrhea. Our bioinformatics approach to detect the pharmacology of SWT may shed light on drug discovery for gynecological diseases and could be utilized to investigate other TCM formulae as well.     

Target Essentiality and Centrality Characterize Drug Side Effects

To investigate factors contributing to drug side effects, we systematically examine relationships between 4,199 side effects associated with 996 drugs and their 647 human protein targets. We find that it is the number of essential targets, not the number of total targets, that determines the side effects of corresponding drugs. Furthermore, within the context of a three-dimensional interaction network with atomic-resolution interaction interfaces, we find that drugs causing more side effects are also characterized by high degree and betweenness of their targets and highly shared interaction interfaces on these targets. Our findings suggest that both essentiality and centrality of a drug target are key factors contributing to side effects and should be taken into consideration in rational drug design.