Identification Of Drug Targets Research Articles

Streamlined analysis of drug targets by proteome integral solubility alteration indicates organ-specific engagement

Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions.

Identification of Novel Target DCTPP1 for Colorectal Cancer Therapy with the Natural Small‐Molecule Inhibitors Regulating Metabolic Reprogramming

AbstractColorectal cancer (CRC) is one of the most common malignant tumors. Identification of new effective drug targets for CRC and exploration of bioactive small‐molecules are clinically urgent. The human dCTP pyrophosphatase 1 (DCTPP1) is a newly identified pyrophosphatase regulating the cellular nucleotide pool but remains unexplored as potential target for CRC treatment. Here, twelve unprecedented chemical architectures terpene‐nonadride heterodimers (1–12) and their monomers (13–20) were isolated from endophyte Bipolaris victoriae S27. Compounds 1–12 represented the first example of terpene‐nonadride heterodimers, in which nonadride monomers of 1 and 2 were also first example of 5/6 bicyclic nonadrides. A series of assays showed that 2 could repress proliferation and induce cell cycle arrest, apoptotic and autophagic CRC cell death in vitro and in vivo. Clinical cancer samples data revealed that DCTPP1 was a novel target associated with poor survival in CRC. DCTPP1 was also identified as a new target protein of 2. Mechanically, compound 2 bound to DCTPP1, inhibited its enzymatic activity, intervened with amino acid metabolic reprogramming, and exerted anti‐CRC activity. Our study demonstrates that DCTPP1 was a novel potential biomarker and therapeutic target for CRC, and 2 was the first natural anti‐CRC drug candidate targeting DCTPP1.

Identification of Novel Target DCTPP1 for Colorectal Cancer Therapy with the Natural Small-Molecule Inhibitors Regulating Metabolic Reprogramming.

Colorectal cancer (CRC) is one of the most common malignant tumors. Identification of new effective drug targets for CRC and exploration of bioactive small-molecules are clinically urgent. The human dCTP pyrophosphatase 1 (DCTPP1) is a newly identified pyrophosphatase regulating the cellular nucleotide pool but remains unexplored as potential target for CRC treatment. Here, twelve unprecedented chemical architectures terpene-nonadride heterodimers (1-12) and their monomers (13-20) were isolated from endophyte Bipolaris victoriae S27. Compounds 1-12 represented the first example of terpene-nonadride heterodimers, in which nonadride monomers of 1 and 2 were also first example of 5/6 bicyclic nonadrides. A series of assays showed that 2 could repress proliferation and induce cell cycle arrest, apoptotic and autophagic CRC cell death in vitro and in vivo. Clinical cancer samples data revealed that DCTPP1 was a novel target associated with poor survival in CRC. DCTPP1 was also identified as a new target protein of 2. Mechanically, compound 2 bound to DCTPP1, inhibited its enzymatic activity, intervened with amino acid metabolic reprogramming, and exerted anti-CRC activity. Our study demonstrates that DCTPP1 was a novel potential biomarker and therapeutic target for CRC, and 2 was the first natural anti-CRC drug candidate targeting DCTPP1.

GoToCloud optimization of cloud computing environment for accelerating cryo-EM structure-based drug design

Cryogenic electron microscopy (Cryo-EM) is a widely used technique for visualizing the 3D structures of many drug design targets, including membrane proteins, at atomic resolution. However, the necessary throughput for structure-based drug design (SBDD) is not yet achieved. Currently, data analysis is a major bottleneck due to the rapid advancements in detector technology and image acquisition methods. Here we show “GoToCloud”, a cloud-computing-based platform for advanced data analysis and data management in Cryo-EM. With GoToCloud, it is possible to optimize computing resources and reduce costs by selecting the most appropriate parallel processing settings for each processing step. Our benchmark tests on GoToCloud demonstrate that parallel computing settings, including the choice of computational hardware, as well as a required target resolution have significant impacts on the processing time and cost performance. Through this optimization of a cloud computing environment, GoToCloud emerges as a promising platform for the acceleration of Cryo-EM SBDD.

Structural basis for antibiotic transport and inhibition in PepT2

The uptake and elimination of beta-lactam antibiotics in the human body are facilitated by the proton-coupled peptide transporters PepT1 (SLC15A1) and PepT2 (SLC15A2). The mechanism by which SLC15 family transporters recognize and discriminate between different drug classes and dietary peptides remains unclear, hampering efforts to improve antibiotic pharmacokinetics through targeted drug design and delivery. Here, we present cryo-EM structures of the proton-coupled peptide transporter, PepT2 from Rattus norvegicus, in complex with the widely used beta-lactam antibiotics cefadroxil, amoxicillin and cloxacillin. Our structures, combined with pharmacophore mapping, molecular dynamics simulations and biochemical assays, establish the mechanism of beta-lactam antibiotic recognition and the important role of protonation in drug binding and transport.

The function of serine/threonine-specific protein kinases in B cells.

The serine/threonine-specific protein kinases (STKs) are important for cell survival, proliferation, differentiation, and apoptosis. In B cells, these kinases play indispensable roles in regulating important cellular functions. Multiple studies on human and other animal cells have shown that multiple STKs are involved in different stages of B cell development and antibody production. However, how STKs affect B cell development and function is still not completely understood. Considering that B cells are clinically important in immunity and diseases, our understanding of STKs' roles in B cells is in great need of investigation with current technologies. Investigating serine/threonine kinases will not only deepen our insight into B cell-related disorders but also facilitate the identification of more effective drug targets for conditions like lymphoma and systemic lupus erythematosus.

Disease coverage of human genome-wide association studies and pharmaceutical research and development

BackgroundDespite the growing interest in the use of human genomic data for drug target identification and validation, the extent to which the spectrum of human disease has been addressed by genome-wide association studies (GWAS), or by drug development, and the degree to which these efforts overlap remain unclear.MethodsIn this study we harmonize and integrate different data sources to create a sample space of all the human drug targets and diseases and identify points of convergence or divergence of GWAS and drug development efforts.ResultsWe show that only 612 of 11,158 diseases listed in Human Disease Ontology have an approved drug treatment in at least one region of the world. Of the 1414 diseases that are the subject of preclinical or clinical phase drug development, only 666 have been investigated in GWAS. Conversely, of the 1914 human diseases that have been the subject of GWAS, 1121 have yet to be investigated in drug development.ConclusionsWe produce target-disease indication lists to help the pharmaceutical industry to prioritize future drug development efforts based on genetic evidence, academia to prioritize future GWAS for diseases without effective treatments, and both sectors to harness genetic evidence to expand the indications for licensed drugs or to identify repurposing opportunities for clinical candidates that failed in their originally intended indication.

An engineered model of metastatic colonization of human bone marrow reveals breast cancer cell remodeling of the hematopoietic niche

Incomplete understanding of metastatic disease mechanisms continues to hinder effective treatment of cancer. Despite remarkable advancements toward the identification of druggable targets, treatment options for patients in remission following primary tumor resection remain limited. Bioengineered human tissue models of metastatic sites capable of recreating the physiologically relevant milieu of metastatic colonization may strengthen our grasp of cancer progression and contribute to the development of effective therapeutic strategies. We report the use of an engineered tissue model of human bone marrow (eBM) to identify microenvironmental cues regulating cancer cell proliferation and to investigate how triple-negative breast cancer (TNBC) cell lines influence hematopoiesis. Notably, individual stromal components of the bone marrow niche (osteoblasts, endothelial cells, and mesenchymal stem/stromal cells) were each critical for regulating tumor cell quiescence and proliferation in the three-dimensional eBM niche. We found that hematopoietic stem and progenitor cells (HSPCs) impacted TNBC cell growth and responded to cancer cell presence with a shift of HSPCs (CD34+CD38-) to downstream myeloid lineages (CD11b+CD14+). To account for tumor heterogeneity and show proof-of-concept ability for patient-specific studies, we demonstrate that patient-derived tumor organoids survive and proliferate in the eBM, resulting in distinct shifts in myelopoiesis that are similar to those observed for aggressively metastatic cell lines. We envision that this human tissue model will facilitate studies of niche-specific metastatic progression and individualized responses to treatment.

Genome-wide association testing beyond SNPs.

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Identifying prothrombin and bone sialoprotein as potential drug targets for idiopathic pulmonary fibrosis

BackgroundIdiopathic Pulmonary Fibrosis (IPF) is a fatal disease with scarce therapeutic alternatives, which imposes a significant economic burden on society. The identification of novel drug targets is thus critically essential. Plasma proteins with discernible causal evidence hold promise as viable drug targets for this condition.MethodsWe performed a proteome-wide Mendelian randomization (MR) analysis to assess the causal effects of 4,907 circulating proteins from the deCODE study on the risk of IPF from the Finngen Database (2,018 cases vs. 373,064 controls). We further replicated the MR analysis in 1426 proteins from the ARIC study and IPF from the UK Biobank (1,369 cases vs. 435,866 controls). Then a series of analyses including Bayesian colocalization, Steiger filtering, and phenotype scanning were conducted to validate the credibility of the MR results. Subsequently, protein-protein interaction (PPI) analysis, pathway enrichment analysis, and druggability assessment were executed to elucidate the underlying mechanisms. Finally, the findings were corroborated using a bleomycin-induced pulmonary fibrosis mouse model.ResultsThe MR analysis bolstered by robust evidence of colocalization, indicated a significant positive association between Prothrombin and increased IPF risk (OR = 3.26,95%CI 1.75–6.07). Conversely, Bone Sialoprotein (IBSP) demonstrated an inverse association with IPF susceptibility (OR = 0.27,95%CI 0.14–0.55).ConclusionsThe integrative analysis suggests that Prothrombin and IBSP are promising candidates as potential drug targets for IPF. Additional clinical investigations are warranted to substantiate these findings.

Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning

ABSTRACT BACE1 has been regarded as an essential drug design target for treating Alzheimer’s disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (MM-GBSA) method are integrated to elucidate the molecular mechanism underlying the effect of D93 and D289 protonation on binding of inhibitors OV6 and 4B2 to BACE1. The GaMD trajectory-based DL successfully identifies significant function domains. Dynamic analysis shows that the protonation of D93 and D289 strongly affects the structural flexibility and dynamic behaviour of BACE1. Free energy landscapes indicate that inhibitor-bound BACE1s have more conformational states in the protonated states than the wild-type (WT) BACE1, and show more binding poses of inhibitors. Binding affinities calculated using the MM-GBSA method indicate that the protonation of D93 and D289 highly disturbs the binding ability of inhibitors to BACE1. In addition, the protonation of two residues significantly affects the hydrogen bonding interactions (HBIs) of OV6 and 4B2 with BACE1, altering their binding activity to BACE1. The binding hot spots of BACE1 recognized by residue-based free energy estimations provide rational targeting sites for drug design towards BACE1. This study is anticipated to provide theoretical aids for drug development towards treatment of AD.

Plasma Proteomic Insights for Identification of Novel Predictors and Potential Drug Targets in Atrial Fibrillation: A Prospective Cohort Study and Mendelian Randomization Analysis.

Currently, there are no reliable methods for predicting and preventing atrial fibrillation (AF) in its early stages. This study aimed to identify plasma proteins associated with AF to discover biomarkers and potential drug targets. The UK Biobank Pharma Proteomics Project examined 2923 circulating proteins using the Olink platform, forming the basis of this prospective cohort study. The UK Biobank Pharma Proteomics Project included a randomly selected discovery cohort and the consortium-selected replication cohort. The study's end point was incident AF, identified using International Classification of Diseases, Tenth Revision codes. The association between plasma proteins and incident AF was evaluated using Cox proportional hazard models in both cohorts. Proteins present in both cohorts underwent Mendelian randomization analysis to delineate causal connections, utilizing cis-protein quantitative trait loci as genetic tools. The predictive efficacy of the identified proteins for AF was assessed using the area under the receiver operating characteristic curve, and their druggability was explored. Data from 53 032 participants were included in this study. Incident AF cases were identified in the discovery cohort (1894; 5.5%) within a median follow-up of 14.5 years and in the replication cohort (451; 10.6%) within a median follow-up of 14.4 years. Twenty-one proteins linked to AF were identified in both cohorts. Specifically, COL4A1 (collagen IV α-1; odds ratio, 1.11 [95% CI, 1.04-1.19]; false discovery rate, 0.016) and RET (proto-oncogene tyrosine-protein kinase receptor Ret; odds ratio, 0.96 [95% CI, 0.94-0.98]; false discovery rate, 0.013) demonstrated a causal link with AF, and RET is druggable. COL4A1 improved the short- and long-term predictive performance of established AF models, as evidenced by significant enhancements in the area under the receiver operating characteristic, integrated discrimination improvement, and net reclassification index, all with P values below 0.05. COL4A1 and RET are associated with the development of AF. RET is identified as a potential drug target for AF prevention, while COL4A1 serves as a biomarker for AF prediction. Future studies are needed to evaluate the effectiveness of targeting these proteins to reduce AF risk.

Scalable Micro-Kidneys for Unbiased Phenotypic Drug Discovery and Target Identification in Diabetic Nephropathy and Inherited Kidney Diseases

Scalable Micro-Kidneys for Unbiased Phenotypic Drug Discovery and Target Identification in Diabetic Nephropathy and Inherited Kidney Diseases

Codes between Poles: Linking Transcriptomic Insights into the Neurobiology of Bipolar Disorder.

Bipolar disorder (BPD) is a serious psychiatric condition that is characterized by the frequent shifting of mood patterns, ranging from manic to depressive episodes. Although there are already treatment strategies that aim at regulating the manifestations of this disorder, its etiology remains unclear and continues to be a question of interest within the scientific community. The development of RNA sequencing techniques has provided newer and better approaches to studying disorders at the transcriptomic level. Hence, using RNA-seq data, we employed intramodular connectivity analysis and network pharmacology assessment of disease-associated variants to elucidate the biological pathways underlying the complex nature of BPD. This study was intended to characterize the expression profiles obtained from three regions in the brain, which are the nucleus accumbens (nAcc), the anterior cingulate cortex (AnCg), and the dorsolateral prefrontal cortex (DLPFC), provide insights into the specific roles of these regions in the onset of the disorder, and present potential targets for drug design and development. The nAcc was found to be highly associated with genes responsible for the deregulated transcription of neurotransmitters, while the DLPFC was greatly correlated with genes involved in the impairment of components crucial in neurotransmission. The AnCg did show association with some of the expressions, but the relationship was not as strong as the other two regions. Furthermore, disease-associated variants or single nucleotide polymorphisms (SNPs) were identified among the significant genes in BPD, which suggests the genetic interrelatedness of such a disorder and other mental illnesses. DRD2, GFRA2, and DCBLD1 were the genes with disease-associated variants expressed in the nAcc; ST8SIA2 and ADAMTS16 were the genes with disease-associated variants expressed in the AnCg; and FOXO3, ITGA9, CUBN, PLCB4, and RORB were the genes with disease-associated variants expressed in the DLPFC. Aside from unraveling the molecular and cellular mechanisms behind the expression of BPD, this investigation was envisioned to propose a new research pipeline in studying the transcriptome of psychiatric disorders to support and improve existing studies.

Antifungal Drug Resistance in Candida albicans: Identifying Novel Targets for the Development of Effective Antifungal Agents.

Candidiasis, a fungal infection initiated primarily through Candida albicans, affects various portions of the human body and is particularly prevalent in immunocompromised individuals. The pathogenicity of C. albicans is facilitated by numerous virulence causes, including adhesins, morphogenesis, and phenotypic switching. The organism’s capability in the direction of switching between yeast and hyphal forms contributes to the severity of infections. The appearance of resistant strains has rendered current treatments less effective, necessitating the exploration of new drug targets and the progress of novel antifungal agents. Antifungal drug resistance is a multifaceted phenomenon involving genetic mutations, overexpression of efflux pumps, epigenetic changes, and biofilm formation, all regulated by complex genetic and transcriptional networks. These resistance mechanisms can cause treatment failures, highlighting the need for new antifungal agents and improved diagnostic tools. The identification of potential drug targets in C. albicans is critical due to increasing resistance to existing antifungal agents. Recent studies have identified promising targets, for example the riboflavin metabolic pathway and unique protein kinases involved in regulating virulence and pathogenicity. Developing new antifungals is difficult due to C. albicans’ eukaryotic nature and resistance. Ongoing research is essential to find novel targets and strategies, especially with the limited antifungal drug classes available.

Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action

The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, e.g., inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (e.g., channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (e.g., inhibition). Here we present a novel cheminformatic workflow—computer-aided pattern scoring (C@PS)—which was specifically designed to project its prediction capabilities into an uncharted domain of applicability.

The Epidemiology of Biliary Tract Cancer and Associated Prevalence of MDM2 Amplification: A Targeted Literature Review.

Biliary tract cancer (BTC) is a rare and aggressive malignancy that is anatomically classified as gallbladder cancer (GBC), extra- and intra-hepatic cholangiocarcinoma (eCCA and iCCA) and ampullary cancer (AC). BTC is often diagnosed at an advanced stage when treatment options are limited and patients have a poor prognosis, so the identification of new drug targets is of critical importance. BTC is molecularly diverse and harbours different therapeutically actionable biomarkers, including mouse double minute 2 homolog (MDM2), which is currently being investigated as a drug target. The aim of this targeted review was to evaluate and synthesise evidence on the epidemiology of BTC and its subtypes in different geographic regions and on the frequency of MDM2 amplifications in BTC tumours. Epidemiological studies (N=33) consistently demonstrated high incidence rates in South and Central Asia for BTC overall (up to 9.00/100,000) and for all subtypes, with much lower rates in Europe and the US. Among the different types of BTC, the highest global incidence was observed for CCA, mainly driven by iCCA (1.4/100,000), followed by GBC (1.2/100,000) and AC (0.18-0.93 per 100,000). Studies of MDM2 in BTC (N=19) demonstrated variable frequency of MDM2 amplification according to subtype, with consistently high MDM2 amplification rates in GBC (up to 17.5%), and lower rates in CCA (up to 4.4%). The results from this literature review highlight the geographic heterogeneity of BTC and the need for standardised clinicopathologic assessment and reporting to allow cross-study comparisons.

In Silico Identification of Novel Drug Targets in the Honeybee Pathogen Paenibacillus larvae and Molecular Docking Studies

In Silico Identification of Novel Drug Targets in the Honeybee Pathogen <i>Paenibacillus larvae</i> and Molecular Docking Studies

Oxadiazolines as Photoreleasable Labels for Drug Target Identification.

Photoaffinity labeling is a widely used technique for studying ligand-protein and protein-protein interactions. Traditional photoaffinity labels utilize nonspecific C-H bond insertion reactions mediated by a highly reactive intermediate. Despite being the most widely used photoaffinity labels, diazirines exhibit limited compatibility with downstream organic reactions and suffer from storage stability concerns. This study introduces oxadiazolines as innovative and complementary photoactivatable labels for addition to the toolbox and demonstrates their application in vitro and through in cellulo labeling experiments. Oxadiazolines can be easily synthesized from ketone moieties and cleaved with 302-330 nm light to cleanly liberate a diazo reactive moiety that can covalently modify nucleophilic amino acid residues. Notably, oxadiazolines are compatible with various organic reaction conditions and functional groups, allowing for the exploration of a large chemical space. Several known inhibitors featuring the oxadiazoline functionality were prepared without affecting their binding affinity. Furthermore, we confirmed the ability of oxadiazolines to form covalent bonds with proteins upon UV-irradiation, both in vitro and in cellulo, yielding comparable results to those of the matched diazirine compounds.

The 3D-QSAR Study, Molecular Docking, and ADMET Analysis of Darunavir Derivatives of HIV-1 Protease Inhibitors

Background: Acquired Immunodeficiency Syndrome (AIDS) is one of most prevalent infectious diseases in the world , and HIV-1 protease (PR) is a vital target of drug design. Nowadays, three-dimensional quantitative structure-activity relationships (3D-QSAR) are applied to help design new protease inhibitions (PIs). Objective: The primary objective of this study is to apply the 3D-QSAR study to a series of 42 derivatives of Darunavir (DRV) and to design new molecules possessing high antivirus activity. Methods: Partial Least Squares (PLS) were used to cross-validate the dataset of compounds, and the optimal number of principal components (ONC), cross-validate coefficient (q²), standard error of estimate (SEE), non-cross-validated correlation coefficient (R²) and fisher test value (F) were calculated to assess model robustness. In this study, the CoMSIA-DAH model (q²=0.754, r²= 0.988, rpred2=0.57) possessed the highest predicted activity. Newly designed molecules were analyzed by docking studies with compound 25 taken as a template. Results: Within eight newly designed drugs, compound N02 possessed the highest antivirus activity (IC50=0.00461 nM) predicted by the CoMSIA-DAH model. The Surflex-Dock module of SYBYL-X 2.0 was used to affirm the predicted anti-PR activity of the newly designed compounds and the results of docking complex structure could be visualized. All newly designed molecules were in agreement with CSore above four and the docking study revealed that Asp29, Asp30, Ile50, Asp124, Asp128, Asp129 and Ile149 were critical residues in the process of inhibiting PR. Conclusion: One of the main aspects of this study is the successful design of a series of molecules with excellent investigatory values, which elucidates explicit quantitative structure-activity relationships of DRV derivatives and will provide significant suggestions for future pharmaceutical research.