Drug targets

We have used three established human glioblastoma (GBM) cell lines—U87MG, A172, and T98G—as cellular systems to examine the plasticity of the drug-induced GBM cell phenotype, focusing on two clinical drugs, the phosphodiesterase PDE10A inhibitor Mardepodect and the multi-kinase inhibitor Regorafenib, using genome-wide drug-induced gene expression (DIGEX) to examine the drug response. Both drugs upregulate genes encoding specific growth factors, transcription factors, cellular signaling molecules, and cell surface proteins, while downregulating a broad range of targetable cell cycle and apoptosis-associated genes. A few upregulated genes encode therapeutic targets already addressed by FDA approved drugs, but the majority encode targets for which there are no approved drugs. Amongst the latter, we identify many novel druggable targets that could qualify for chemistry-led drug discovery campaigns. We also observe several highly upregulated transmembrane proteins suitable for combined d...

Cancer is a process of uncontrolled cell proliferation that leads to the development of an abnormally growing tumor, determining initially a local disease that might spread, impairing other organs or important processes. One of the most fatal diseases in recent times, cancer kills many lives each year. The effective management of this condition has been impacted by the variances in the disease across the globe, the impact of the medical facilities that are available, and other socioeconomic issues. The aim of this study was to summaries previously published articles regarding recent advances in anticancer drug discoveries. In this review, previously published literature regarding recent advances in anticancer drug discovery collected from journals through PubMed Central, Google Scholar, and Science Direct from March 20 to May 12 was identified well, and points that I assumed were important and recent (2017–2023) were included.Based on my searches, numerous discoveries are identified and grouped as anticancer drug targets, plant derived advances, chemical compounds with in vivo or in vitro cytotoxic drug discovery, and repurposing advances. Based on a variety of articles published by scholars, this review summarizes some recent advancements in anticancer drug discovery. Under this broad topic, promising and clinically evidenced drug targets for anticancer drug binding, some drugs repurposed for cancer treatment, plant-derived advances in cancer therapy, and finally advances in novel chemical compounds in the area of cancer therapy are reviewed.

Keywords: Cancer, Drug discovery, Drug targets, Drug repurposing, Cytotoxic drug 1

The emergence of multi-drug resistant strains and co-occurrence of tuberculosis with HIV creates a major burden to the human health globally. Failure of primary antibacterial therapy necessitates the identification of new mycobacterial drugs. In this study, a comprehensive analysis involving bottom-up systems biology approach was applied wherein we have identified potential therapeutic targets of Mycobacterium tuberculosis infections. Our study prioritized M. tuberculosis therapeutic targets (aspartate-β-semialdeyhde dehydrogenase [ASD], dihydrodipicolinate reductase and diaminopimelate decarboxylase) based on flux and elementary mode analysis using direct mathematical modeling of the relevant metabolic pathways. Molecular docking and simulation studies of the priority target (ie, ASD) revealed the therapeutic potential of the selected natural products (Huperzine A, Rosmarinic acid, and Curcumin) based ASD inhibitors. The study highlights the crucial role of systems biology in conju...

The bacterial species Campylobacter jejuni RM1221 (CjR) is the primary cause of campylobacteriosis which poses a global threat for human health. Over the years the efficacy of antibiotic treatment is becoming more fruitless due to the development of multiple drug resistant strains. Therefore, identification of new drug targets is a valuable tool for the development of new treatments for affected patients and can be obtained by targeting essential protein(s) of CjR. We conducted this in silico study in order to identify therapeutic targets by subtractive CjR proteome analysis. The most important proteins of the CjR proteome, which includes chokepoint enzymes, plasmid, virulence and antibiotic resistant proteins were annotated and subjected to subtractive analyses to filter out the CjR essential proteins from duplicate or human homologous proteins. Through the subtractive and characterization analysis we have identified 38 eligible therapeutic targets including 1 potential vaccine tar...

Typhoid fever is caused by a pathogenic, rod-shaped, flagellated, and Gram-negative bacterium known as Salmonella Typhi. It features a polysaccharide capsule that acts as a virulence factor and deceives the host immune system by protecting phagocytosis. Typhoid fever remains a major health concern in low and middle-income countries, with an estimated death rate of ~200,000 per annum. However, the situation is exacerbated by the emergence of the extensively drug-resistant (XDR) strain designated as H58 of S. Typhi. The emergence of the XDR strain is alarming, and it poses serious threats to public health due to the failure of the current therapeutic regimen. A relatively newer computational method called subtractive genomics analyses has been widely applied to discover novel and new drug targets against pathogens, particularly drug-resistant ones. The method involves the gradual reduction of the complete proteome of the pathogen, leading to few potential and novel drug targets. Thus,...

Completely sequenced organisms have some uncharacterized proteins that are gene-encoded products. These proteins can be predicted through  in-silico  approaches and their biological activities are not proved by experimental evidence and known as hypothetical proteins (HPs). These proteins are important due to their excessive involvement in different cellular and signaling pathways. Structural and functional characterization of HPs reveal crucial roles in microorganisms, especially in pathogens related to human diseases. Here, we discussed all possibilities of in-silico analysis tools and other recently reported methods for hypothetical protein characterization and biomedical applications, including drug and vaccine development. Different methodologies, including meta-proteomics have been used to study protein expression by identification of HPs and comparative genomics have also come under observation due to the emergence of evolutionary study among different organisms. Structural c...

Objective: Yeast commonly causes superficial mycoses similar to the dermatophytes. Superficial mycoses were reported with an estimated incidence of ∼140,000,000 cases/year worldwide and most frequently caused by Malassezia globosa and Malassezia furfur. Treatment available for these conditions is limited and with side effects. Moreover, termination of the treatment may result in the reoccurrence of the disease. The objective of this research was to identify the putative drug targets using computational approaches. Methods: The analysis of genome sequence improves the understanding of diseases which leads to better treatment. Comparison of the genome of the pathogen with the host at the molecular level is suitable for performing the sequence based prediction of protein-protein interaction network, which also forms the basis of drug target identification leading to the discovery of new drugs for the improved treatment. Results: Out of 100 pathways of M. globosa, 95 were common to the host and 5 were unique to the pathogen. Total common and unique targets from common pathways are 1704 and 300, respectively. A unique target from unique pathways and 147 from common pathways were nonhomologous targets. From this, 46 targets were screened out as essential and processed in the next phase to identify the clustered targets which resulted with three clusters based on their biological role and subcellular location. Conclusion: In this study, putative drug targets were identified in M. globosa using in silico approaches of subtractive genomics and cluster network which will help in the next level of drug discovery such as lead identification for the novel targets.

Neuropsychiatric disorders overlap in symptoms and share genetic risk factors, challenging their current classification into distinct diagnostic categories. Novel cross-disorder approaches are needed to improve our understanding of the heterogeneous nature of neuropsychiatric diseases and overcome existing bottlenecks in their diagnosis and treatment. Here we employ high-content multi-parameter phospho-specific flow cytometry, fluorescent cell barcoding and automated sample preparation to characterize ex vivo signaling network responses (n = 1764) measured at the single-cell level in B and T lymphocytes across patients diagnosed with four major neuropsychiatric disorders: autism spectrum condition (ASC), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ; n = 25 each), alongside matched healthy controls (n = 100). We identified 25 nodes (individual cell subtype-epitope-ligand combinations) significantly altered relative to the control group, with variable...

A metabolism is a complex chemical reaction system, whose metabolic genotype – the DNA encoding the enzymes catalyzing these reactions – can be compactly represented by its complement of metabolic reactions. Here, we analyze a space of such metabolic genotypes. Specifically, we study nitrogen metabolism and focus on nitrogen utilization phenotypes that are defined through the viability of a metabolism – its ability to synthesize all essential small biomass precursors – on a given combination of sole nitrogen sources. We randomly sample metabolisms with known phenotypes from metabolic genotype space with the aid of a method based on Markov Chain Monte Carlo sampling. We find that metabolisms viable on a given nitrogen source or a combination of nitrogen sources can differ in as much as 80 percent of their reactions, but can form networks of genotypes that are connected to one another through sequences of single reaction changes. The reactions that cannot vary in any one metabolism di...

The metabolic genotype of an organism can change through loss and acquisition of enzyme-coding genes, while preserving its ability to survive and synthesize biomass in specific environments. This evolutionary plasticity allows pathogens to evolve resistance to antimetabolic drugs by acquiring new metabolic pathways that bypass an enzyme blocked by a drug. We here study quantitatively the extent to which individual metabolic reactions and enzymes can be bypassed. To this end, we use a recently developed computational approach to create large metabolic network ensembles that can synthesize all biomass components in a given environment but contain an otherwise random set of known biochemical reactions. Using this approach, we identify a small connected core of 124 reactions that are absolutely superessential (that is, required in all metabolic networks). Many of these reactions have been experimentally confirmed as essential in different organisms. We also report a superessentiality in...

Completely sequenced organisms have some uncharacterized proteins that are gene-encoded products. These proteins can be predicted through  in-silico  approaches and their biological activities are not proved by experimental evidence and known as hypothetical proteins (HPs). These proteins are important due to their excessive involvement in different cellular and signaling pathways. Structural and functional characterization of HPs reveal crucial roles in microorganisms, especially in pathogens related to human diseases. Here, we discussed all possibilities of in-silico analysis tools and other recently reported methods for hypothetical protein characterization and biomedical applications, including drug and vaccine development. Different methodologies, including meta-proteomics have been used to study protein expression by identification of HPs and comparative genomics have also come under observation due to the emergence of evolutionary study among different organisms. Structural c...

Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metaboli...

Typhoid fever is caused by a pathogenic, rod-shaped, flagellated, and Gram-negative bacterium known as Salmonella Typhi. It features a polysaccharide capsule that acts as a virulence factor and deceives the host immune system by protecting phagocytosis. Typhoid fever remains a major health concern in low and middle-income countries, with an estimated death rate of ~200,000 per annum. However, the situation is exacerbated by the emergence of the extensively drug-resistant (XDR) strain designated as H58 of S. Typhi. The emergence of the XDR strain is alarming, and it poses serious threats to public health due to the failure of the current therapeutic regimen. A relatively newer computational method called subtractive genomics analyses has been widely applied to discover novel and new drug targets against pathogens, particularly drug-resistant ones. The method involves the gradual reduction of the complete proteome of the pathogen, leading to few potential and novel drug targets. Thus,...

Background Understanding complex systems through decomposition into simple interacting components is a pervasive paradigm throughout modern science and engineering. For cellular metabolism, complexity can be reduced by decomposition into pathways with particular biochemical functions, and the concept of elementary flux modes provides a systematic way for organizing metabolic networks into such pathways. While decomposition using elementary flux modes has proven to be a powerful tool for understanding and manipulating cellular metabolism, its utility, however, is severely limited since the number of modes in a network increases exponentially with its size. Results Here, we present a new method for decomposition of metabolic flux distributions into elementary flux modes. Our method can easily operate on large, genome-scale networks since it does not require all relevant modes of the metabolic network to be generated. We illustrate the utility of our method for metabolic engineering of...

Bromodomains (BRDs) are epigenetic interaction domains currently recognized as emerging drug targets for development of anticancer or anti-inflammatory agents. In this study, development of a selective ligand of the fifth BRD of Polybromo protein-1 (PB1(5)) related to Switch/Sucrose Non-Fermenting (SWI/SNF) chromatin remodeling complexes is presented. A compound collection was evaluated by consensus virtual screening and a hit was identified. The biophysical study of protein-ligand interactions was performed using X-ray crystallography and isothermal titration calorimetry. Collective data supported the hypothesis that affinity improvement could be achieved by enhancing interactions of the complex with the solvent. The derived SAR along with free energy calculations and a consensus hydration analysis using WaterMap and SZmap algorithms guided rational design of a set of novel analogues. The most potent analogue demonstrated high affinity of 3.3 μM and an excellent selectivity profile...

The immune system protects a host from foreign pathogens. In rare cases, the immune system can attack the cells of the host organism causing autoimmune diseases. We outline a computational framework that combines bioinformatics and network analysis with an emerging targets platform. The computational framework presented here can be used to find drug targets for autoimmune diseases. It can also be used to find existing drugs that can be repurposed to treat autoimmune diseases based on networks of interactions or similarities between different diseases. Information on which gene regions are associated with the disease (single nucleotide polymorphisms) can be used in gene therapy when that technique becomes viable. Our analysis also revealed immune cell subtypes that are implicated in these diseases. These immune cell subtypes can be selected for immunotherapy experiments. Finally, our analysis also reveals intra-cellular and protein-protein interaction networks and pathways that can b...

HIV/AIDS is still one of the leading causes of death worldwide. Current drugs that target the canonical steps of HIV-1 life cycle are efficient in blocking viral replication, but are unable to eradicate HIV-1 from infected patients. Moreover, drug resistance (DR) is often associated with the clinical use of these molecules, thus raising the need for novel drug candidates as well as novel putative drug targets. In this respect, pharmacological inhibition of the highly conserved and multifunctional nucleocapsid protein (NC) of HIV-1 is considered a promising alternative to current drugs, particularly to overcome DR. Here, using a multidisciplinary approach combining in silico screening, fluorescence-based molecular assays and cellular antiviral assays, we identified nordihydroguaiaretic acid (6), as a novel natural product inhibitor of NC. By using NMR, mass spectrometry, fluorescence spectroscopy and molecular modelling, 6 was found to act through a dual mechanism of action never hig...

Dear Colleagues,

Membrane proteins are coded by ~30% of the human genome and they have diverse roles in health and disease. Some membrane proteins have an enzyme function and some have structural roles, as ion channels and transporters they control the movement of ions and molecules across cell membranes for energy generation and for maintaining cell homeostasis, including uptake of nutrients and removal of xenobiotics. e.g. The efflux protein P-gp pumps drugs out of cancer cells and confers resistance to chemotherapy, whilst efflux proteins in bacteria provide a major mechanism of antimicrobial resistance. As receptors they provide the trigger for signal transduction and a means of cell communication. Membrane proteins have key roles in virus-host interactions, e.g. the membrane-embedded spike protein of the SARS-CoV-2 virus binds to the human ACE2 receptor. Mutations and misfolding of membrane proteins are linked to many human diseases including cystic fibrosis, Alzheimer's, Parkinson's and atherosclerosis. In addition to their inherent roles in cell function, membrane proteins are the molecular targets for over a half of therapeutic drugs, whilst transporters provide a means of drug delivery. Membrane proteins are therefore major targets for drug discovery. Despite all this, the hydrophobic nature of membrane proteins makes it difficult to study their high-resolution structure, function and molecular mechanism using experimental techniques.

In this Special Issue, we will publish original research articles or reviews on elucidation of membrane protein structure using X-ray crystallography, cryo-electron microscopy or NMR spectroscopy, and the study of membrane protein structure, function, molecular mechanism and dynamics using any chemical, biochemical, biophysical, computational or imaging techniques.

Keywords:
Membrane proteins
Ion channels
Transporters
Efflux pumps
Receptors
GPCRs
Protein structure and function
Molecular mechanism
Protein misfolding
Drug targets
Drug discovery
Drug delivery
X-ray crystallography
Cryo-electron microscopy
NMR spectroscopy
Chemistry
Biochemistry
Biophysics
Computational methods
Molecular imaging

Professor Simon G. Patching
University of Leeds, UK