Identification Of Drug Targets Research Articles

Comprehensive applications of the artificial intelligence technology in new drug research and development.

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Evaluations of FDA-approved Drugs Targeting 3CLP of SARS-CoV-2 Employing a Repurposing Strategy.

The SARS-CoV-2 coronavirus (COVID-19) has raised innumerable global concerns, and few effective treatment strategies have yet been permitted by the FDA to lighten the disease burden. SARS-CoV-2 3C-like proteinase (3CLP) is a crucial protease and plays a key role in the viral life cycle, as it controls replication, and thus, it is viewed as a target for drug design. In this study, we performed structure-based virtual screening of FDA drugs approved during 2015-2019 (a total of 220 drugs) for interaction with the active site of 3CLP (PDB ID 6LU7) using AutoDock 4.2. We report the top ten drugs that outperform the reported drugs against 3CLP (Elbasvir and Nelfinavir), particularly Cefiderocol, having the highest affinity among the compounds tested, with a binding energy of -9.97 kcal/mol. H-bond (LYS102:HZ2-ligand: O49), hydrophobic (ligand-VAL104), and electrostatic (LYS102:NZ-ligand: O50) interactions were observed in the cefiderocol-3CLP complex. The docked complex was subjected to a 50 ns molecular dynamics study to check its stability, and stable RMSD and RMSF graphs were observed. Accordingly, we suggest cefiderocol might be effective against SARS-CoV-2 and urge that experimental validation be performed to determine the antiviral efficacy of cefiderocol against SARS-CoV-2. Along with these, cefiderocol is effective for treating respiratory tract pathogens and a wide range of gram-negative bacteria for whom there are limited therapeutic alternatives. This article aimed to explore the FDA-approved drugs as a repurposing study against 3CLP for COVID-19 management.

Discovery of a Potential Allosteric Site in the SARS-CoV-2 Spike Protein and Targeting Allosteric Inhibitor to Stabilize the RBD Down State using a Computational Approach.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide public health crisis. At present, the development of effective drugs and/or related therapeutics is still the most urgent and important task for combating the virus. The viral entry and associated infectivity mainly rely on its envelope spike protein to recognize and bind to the host cell receptor angiotensin-converting enzyme 2 (ACE2) through a conformational switch of the spike receptor binding domain (RBD) from inactive to active state. Thus, it is of great significance to design an allosteric inhibitor targeting spike to lock it in the inactive and ACE2-inaccessible state. This study aims to discover the potential broad-spectrum allosteric inhibitors capable of binding and stabilizing the diverse spike variants, including the wild type, Delta, and Omicron, in the inactive RBD down state. In this work, we first detected a potential allosteric pocket within the SARS-CoV-2 spike protein. Then, we performed large-scale structure-based virtual screening by targeting the putative allosteric pocket to identify allosteric inhibitors that could stabilize the spike inactive state. Molecular dynamics simulations were further carried out to evaluate the effects of compound binding on the stability of spike RBD. Finally, we identified three potential allosteric inhibitors, CPD3, CPD5, and CPD6, against diverse SARS-CoV-2 variants, including Wild-type, Delta, and Omicron variants. Our simulation results showed that the three compounds could stably bind the predicted allosteric site and effectively stabilize the spike in the inactive state. The three compounds provide novel chemical structures for rational drug design targeting spike protein, which is expected to greatly assist in the development of new drugs against SARS-CoV-2.

Advancements in p53-Based Anti-Tumor Gene Therapy Research

The p53 gene is one of the genes most closely associated with human tumors and has become a popular target for tumor drug design. Currently, p53-based gene therapy techniques have been developed, but these therapies face challenges such as immaturity, high safety hazards, limited efficacy, and low patient acceptance. However, researchers are no less enthusiastic about the treatment because of its theoretical potential to treat cancer. In this paper, the advances in p53-based gene therapy and related nucleic acid delivery technologies were reviewed and prospected in order to support further development in this field.

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in tumorigenesis and anticancer drug resistance, making it a critical target for drug design. miR21 biogenesis involves precise biochemical pathways, including the cleavage of its precursor, pre-miR21, by the enzyme Dicer. The present work investigates the conformational dynamics of pre-miR21, focusing on the role of adenine29 in switching between Dicer-binding-prone and inactive states. We also investigated the effect of L50, a cyclic peptide binder of pre-miR21 and a weak inhibitor of its processing. Using time series data and our novel collective variable-based enhanced sampling technique, OneOPES, we simulated these conformational changes and assessed the effect of L50 on the conformational plasticity of pre-miR21. Our results provide insight into peptide-induced conformational changes and pave the way for the development of a computational platform for the screening of inhibitors of pre-miR21 processing that considers RNA flexibility, a stepping stone for effective structure-based drug design, with potentially broad applications in drug discovery.

Computational proteomics analysis of Taphrina deformans for the identification of antifungal drug targets and validation with commercial fungicides

Computational proteomics analysis of Taphrina deformans for the identification of antifungal drug targets and validation with commercial fungicides

Rational drug design targeting g-protein-coupled receptors: a structural biology perspective

G protein-coupled Receptors (G protein-coupled Receptors, GPCRs) play a key role in the transmission of extracellular signals and regulation of many biological processes, which makes these membrane proteins one of the most important classes of targets for pharmacological agents. The significant increase in the number of atomic structures of GPCRs recently has paved the way for Structure Based Drug Design (SBDD). SBDD uses information on the structure of the receptor-ligand complex to search for affinity and selective ligands without the need for high-throughput experimental ligand screening and allows a significant expansion of the chemical ligand search space. In our review we describe the process of GPCR structure obtaining by X-ray diffraction analysis and cryo-electron microscopy (cryo-EM) – an important step in rational drug design targeting GPCRs. Our main goal is to highlight to a wide range of specialists the current aspects and key features of experimental structural biology methods necessary for a detailed understanding of SBDD GPCRs.

Targeting MurG Enzyme in Klebsiella pneumoniae: An In Silico Approach to Novel Antimicrobial Discovery

Antibiotic resistance poses a global crisis fuelled by widespread antibiotic use, particularly against Gram-negative bacteria like Klebsiella pneumoniae, a leading cause of hospital-acquired infections with high mortality rates. Urgent identification of effective drug targets is imperative, with a focus on metabolic pathways to inhibit bacterial growth. Targeting the crucial metabolic pathways of K. pneumoniae would be a more efficient way to prevent its growth and the diseases that it causes. The present study focused on inhibiting the UDP-N-acetylglucosamine--N-acetylmuramyl-(pentapeptide)pyrophosphoryl-undecaprenol N-acetylglucosamine transferase (MurG) enzyme, which is a key enzyme in peptidoglycan synthesis pathway. A high throughput virtual screening was used to find possible lead molecules from Enamine -High-Throughput Screening Centre library. The resulting high binding affinity ligands were further assessed for their drug-likeness and other pharmacokinetic properties. Based on these analyses, the three ligands Z95813755_1, Z324718246_1 and Z324718246_2 were selected for further molecular dynamic simulation studies. The molecular dynamic simulation results and MM/PBSA analysis predicted that both Z95813755_1 and Z324718246_2, molecules show higher affinity towards MurG. For the first time we are reporting potential inhibitors against MurG from K. pneumoniae, providing new insights in management of multi drug resistant K. pneumoniae infections.

In silico analysis of ventricular action potential with a current-voltage-time representation: Thresholds, membrane resistance, repolarization reserve.

The waveform of ventricular action potential (AP) is a key determinant of the cardiac cycle, a marker of beating pathophysiology, and a target for anti-arrhythmic drug design. The information contained in the waveform, though, is limited to the actual dynamics of the AP under consideration. By measuring quasi-instantaneous current-voltage relationships during repolarization, I propose a three-dimensional representation of the ventricular AP which includes potential dynamic responses that the beat can show when electrically perturbed. This representation is described in the case of a numerically reconstructed ventricular AP, but it can be, at least partially, derived in real cardiomyocytes. Simulation allows to disclose the potentialities and the limitations of the approach, that can be extended to any non-cardiac AP. By reporting, at any AP time, the ion current available within the physiological membrane potential range at that time, the representation makes all together available: (1) refractory period, (2) thresholds for eliciting full or calcium-driven APs, (3) threshold for all-or-none repolarization, (4) membrane resistance during repolarization, (5) the safety of membrane repolarization. It provides further evidence of a negative membrane resistance during the late phase of ventricular AP and a quantitative estimate of repolarization reserve (RR), key determinants of repolarization dynamics.

Polyphenol Tannin‐Induced Protein Precipitation for Drug Target Discovery by Chemical Proteomics

AbstractHigh‐throughput drug discovery is highly dependent on the targets available to accelerate the process of candidate screening. Unbiased drug target identification is of crucial importance to reveal the mechanisms of protein–drug interactions. In recent years, energetics‐based protein conformation methods have become an alternative strategy for protein target discovery of bioactive drugs. In this study, we present a novel tannin protein precipitation method (TAP) for identifying target proteins of small molecules using the effect on protein solubility of tannin. The feasibility of this method was validated using a drug model of methotrexate. It was found that the known target DHFR yields a significant solubility shift. This method was further investigated with Annexin A2, the target protein of protopanaxadiol (PPD)‐S by western blot, and by screening potential target proteins of PPD‐S in K562 cells by quantitative proteomics. Finally, the TAP method was employed to differentiate the target proteins of pair chiral molecules PPD‐S and PPD‐R, which can bring up novel insights into the mechanism of drug–protein interactions. This study not only provides an effective tool for drug research and development but also offers new ideas for understanding the mechanism of action of natural chiral bioactive compounds.

3DSTarPred: A Web Server for Target Prediction of Bioactive Small Molecules Based on 3D Shape Similarity.

Target identification plays a critical role in preclinical drug development. The in silico approach has been developed and widely applied to assist medicinal chemists and pharmacologists in drug target identification. There are many target prediction web servers available today that have revealed both advantages and shortcomings in practical applications. Here, we present 3DSTarPred, a web server for three-dimensional (3D) shape similarity-based target prediction of small molecules. A benchmark study showed that 3DSTarPred achieved a target prediction success rate of 76.27%, which was higher than that of existing target prediction web servers. In addition, the performance of 3DSTarPred in the target prediction of diverse substructures/superstructures was also better than that of the existing target prediction web servers. In case studies, 3DSTarPred was used to identify the potential targets of two small molecules, one being kaempferol, a natural lead compound for the treatment of Alzheimer's disease (AD), and the other being sildenafil, a candidate for drug repurposing in AD. The case studies further demonstrated the reliability and success of 3DSTarPred in practice. The 3DSTarPred server is freely available at http://3dstarpred.pumc.wecomput.com.

Protein-protein interaction network study of metallo-beta-lactamase-L1 present in Stenotrophomonas maltophilia and identification of potential drug targets.

The online version contains supplementary material available at 10.1007/s40203-024-00270-9.

A structural snapshot of the multiple working states of the Mpox virus helicase-primase D5.

The Mpox virus (or Monkeypox virus, MPXV) uses its own encoded proteins to form a replication machine that replicates the viral genome in the host cell cytoplasm, making this machinery a key target for antiviral drug design. The D5 (also known as the OPG117 or E5) protein, a bi-functional helicase-primase enzyme, is crucial in the MPXV replication machinery and genome uncoating process. Recently, cryo-electron microscopy (cryo-EM) structures of D5 in multiple states have been determined. These structures have elucidated the full trajectory of the MPXV D5 helicase-primase as it moves along single-stranded DNA, providing unprecedented advancements in the molecular dynamics and unwinding mechanism. This structural snapshot describes the structural features of the D5 protein and dissects the broader implications of its pivotal role in MPXV replication.

Dual COX-2/TNF-α Inhibitors as Promising Anti-inflammatory and Cancer Chemopreventive Agents: A Review

: Cyclooxygenases (COX) play a pivotal role in inflammation and are responsible for the production of prostaglandins (PGs). Two types of COXs have been identified as key biological targets for drug design: Constitutive COX-1 and inducible COX-2. Nonsteroidal anti-inflammatory drugs (NSAIDs) target COX-1, while selective COX-2 inhibitors are designed for COX-2. These COX isoforms are involved in multiple physiological and pathological pathways throughout the body. Overproduction of tumor necrosis factor-alpha (TNF-α) plays a role in COX-2's inflammatory activity. Tumor necrosis factor-alpha can contribute to cardiac fibrosis, heart failure, and various cancers by upregulating the COX-2/PGE2 axis. Therefore, suppressing COX activity has emerged as a potentially effective treatment for chronic inflammatory disorders and cancer. This review explores the mechanisms of TNF-α-induced COX-2/PGE2 expression, a significant pathophysiological feature of cancer development. Furthermore, we summarize chemical compounds with dual COX-2/TNF-α inhibitory actions, providing an overview of their structure-activity relationship. These insights may contribute to the development of new generations of dual-acting COX-2/TNF-α inhibitors with enhanced efficacy.

Cryo-EM Structures of the Plasmodium falciparum Apicoplast DNA Polymerase

The apicoplast DNA polymerase (apPol) from Plasmodium falciparum is essential for the parasite’s survival, making it a prime target for antimalarial therapies. Here, we present cryo-electron microscopy structures of the apPol in complex with DNA and incoming nucleotide, offering insights into its molecular mechanisms. Our structural analysis reveals that apPol contains critical residues for high-fidelity DNA synthesis, but lacks certain structural elements to confer processive DNA synthesis during replication, suggesting the presence of additional accessory factors. The enzyme exhibits large-scale conformational changes upon DNA and nucleotide binding, particularly within the fingers and thumb subdomains. These movements reveal potential allosteric sites that could serve as targets for drug design. Our findings provide a foundation for advancing the understanding of apPol’s unique functional mechanisms and potentially offering new avenues for the development of novel inhibitors and therapeutic interventions against malaria.

Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease

AimDue to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease. MethodsFour Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach. ResultsThese three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity. ConclusionPredictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.

Ensemble Docking as a Tool for the Rational Design of Peptidomimetic Staphylococcus aureus Sortase A Inhibitors.

Sortase A (SrtA) of Staphylococcus aureus has long been shown to be a relevant molecular target for antibacterial development. Moreover, the designed SrtA inhibitors act via the antivirulence mechanism, potentially causing less evolutional pressure and reduced antimicrobial resistance. However, no marketed drugs or even drug candidates have been reported until recently, despite numerous efforts in the field. SrtA has been shown to be a tough target for rational structure-based drug design (SBDD), which hampers the regular development of small-molecule inhibitors using the available arsenal of drug discovery tools. Recently, several oligopeptides resembling the sorting sequence LPxTG (Leu-Pro-Any-Thr-Gly) of the native substrates of SrtA were reported to be active in the micromolar range. Despite the good experimental design of those works, their molecular modeling parts are still not convincing enough to be used as a basis for a rational modification of peptidic inhibitors. In this work, we propose to use the ensemble docking approach, in which the relevant SrtA conformations are extracted from the molecular dynamics simulation of the LPRDA (Leu-Pro-Arg-Asp-Ala)-SrtA complex, to effectively represent the most significant and diverse target conformations. The developed protocol is shown to describe the known experimental data well and then is applied to a series of new peptidomimetic molecules resembling the active oligopeptide structures reported previously in order to prioritize structures from this work for further synthesis and activity testing. The proposed approach is compared to existing alternatives, and further directions for its development are outlined.

Single-cell technology for drug discovery and development

The success rate of drug development today remains low, with long development cycles and high costs, especially in areas such as oncology, neurology, immunology, and infectious diseases. Single-cell omics, encompassing transcriptomics, genomics, epigenomics, proteomics, and metabolomics enable the analysis of gene expression profiles and cellular heterogeneity from the perspective of individual cells, offering a high-resolution view of their functional diversity. These technologies can help reveal disease mechanisms, drug target identification and validation, selection of preclinical models and candidate drugs, and clinical decision-making based on disease response to drugs, all at the single-cell level. The development of deep learning technology has provided a powerful tool for research in drug discovery based on single-cell techniques, which has evolved with the advent of large-scale public databases to predict drug responses and targets. In addition, traditional Chinese medicine (TCMs) research has also entered the era of single-cell technology. Single-cell omics technologies offer an alternative way in deciphering the mechanisms of TCMs in disease treatment, revealing drug targets, screening new drugs, and designing combinations of TCMs. This review aims to explore the application of single-cell omics technologies in drug screening and development comprehensively, highlighting how they accelerate the drug development process and facilitate personalized medicine by precisely identifying therapeutic targets, predicting drug responsiveness, deciphering mechanisms of action. It is also concluded that drug development process and therapeutic efficacy of drugs can be improved by combining single-cell omics and artificial intelligence techniques.

Identification of Potential Drug Targets for Sleep Apnea Through Mendelian Randomization

Background: Sleep apnea is a significant health impediment, and it presently lacks efficacious therapeutic interventions. Thus, it is imperative to discover novel therapeutic targets that could guide clinical treatment strategies. Objective: This study aims to utilize an integrative analytic approach to unearth previously unappreciated protein-encoding genes implicated in sleep apnea susceptibility. Methods: Through the Multi-Marker Analysis of Genomic Annotation (MAGMA), we aligned Single-Nucleotide Polymorphism (SNP) summary statistics from Genome-Wide Association Studies (GWAS) of gene bodies to discern potential risk genes. Following the MAGMA results, we conducted a round of Transcriptome-Wide Association Studies (TWAS) and Proteome-Wide Association Studies (PWAS) to expedite the conversion of genetic associations into probable protein targets. Mendelian Randomization (MR) and co-localization analysis were employed to ascertain the causal linkage between the candidate target genes and sleep apnea. Finally, a mediation analysis was undertaken to explore the possible intermediary role of 150 inflammatory metabolites and 1,124 proteins. Results: The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset via blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetyl carnosine, and RELT, pointing towards their potential mediating roles in sleep apnea's etiological pathway. result: The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset via blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetylcarnosine, and RELT, pointing towards their potential mediating roles in sleep apnea's etiological pathway. Conclusion: Our findings indicate that ACADVL, CCDC134, and UPP1 genes are potentially significant targets for further functional investigation and therapeutic interventions for sleep apnea.

Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor

Deoxyribonucleic acid (DNA) is able to form non-canonical four-stranded helical structures with diverse folding patterns known as G-quadruplexes (G4s). G4 topologies are classified based on their relative strand orientation following the 5’ to 3’ phosphate backbone polarity. Broadly, G4 topologies are either parallel (4+0), antiparallel (2+2), or hybrid (3+1). G4s play crucial roles in biological processes such as DNA repair, DNA replication, transcription and have thus emerged as biological targets in drug design. While computational models have been developed to predict G4 formation, there is currently no existing model capable of predicting G4 folding topology based on its nucleic acid sequence. Therefore, we introduce G4ShapePredictor (G4SP), an application featuring a collection of multi-classification machine learning models that are trained on a custom G4 dataset combining entries from existing literature and in-house circular dichroism experiments. G4ShapePredictor is designed to accurately predict G4 folding topologies in potassium (K+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {K}^+$$\\end{document}) buffer based on its primary sequence and is able to incorporate a threshold optimization strategy allowing users to maximise precision. Furthermore, we have identified three topological sequence motifs that suggest specific G4 folding topologies of (4+0), (2+2) or (3+1) when utilising the decision-making mechanisms of G4ShapePredictor.