A Review of Recent Advances and Research on Drug Target Identification Methods

Circulating Proteins and IgA Nephropathy: A Multiancestry Proteome-Wide Mendelian Randomization Study

Key Points

Background

The therapeutic options for IgA nephropathy are rapidly evolving, but early diagnosis and targeted treatment remain challenging. We aimed to identify circulating plasma proteins associated with IgA nephropathy by proteome-wide Mendelian randomization studies across multiple ancestry populations.

Methods

In this study, we applied Mendelian randomization and colocalization analyses to estimate the putative causal effects of 2615 proteins on IgA nephropathy in Europeans and 235 proteins in East Asians. Following two-stage network Mendelian randomization, multitrait colocalization analysis and protein-altering variant annotation were performed to strengthen the reliability of the results. A protein–protein interaction network was constructed to investigate the interactions between the identified proteins and the targets of existing medications.

Results

Putative causal effects of 184 and 13 protein–disease pairs in European and East Asian ancestries were identified, respectively. Two protein–disease pairs showed shared causal effects across them (CFHR1 and FCRL2). Supported by the evidence from colocalization analysis, potential therapeutic targets were prioritized and four drug-repurposing opportunities were suggested. The protein–protein interaction network further provided strong evidence for existing medications and pathways that are known to be therapeutically important.

Conclusions

Our study identified a number of circulating proteins associated with IgA nephropathy and prioritized several potential drug targets that require further investigation.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Identification of potential drug targets for varicose veins: a Mendelian randomization analysis

Introduction

Varicose veins are a common chronic disease that creates a significant economic burden on the healthcare system. Current treatment options, including pharmacological treatments, are not always effective, and there is a need for more targeted therapies. A Mendelian randomization (MR) method uses genetic variants as instrumental variables to estimate the causal effect of an exposure on an outcome, and it has been successful in identifying therapeutic targets in other diseases. However, few studies have used MR to explore potential protein drug targets for varicose veins.

Methods

To identify potential drug targets for varicose veins of lower extremities, we undertook a comprehensive screen of plasma protein with a two-sample MR method. We used recently reported cis-variants as genetic instruments of 2,004 plasma proteins, then applied MR to a recent meta-analysis of genome-wide association study on varicose veins (22,037 cases and 437,665 controls). Furthermore, pleiotropy detection, reverse causality testing, colocalization analysis, and external replication were utilized to strengthen the causal effects of prioritized proteins. Phenome-wide MR (PheW-MR) of the prioritized proteins for the risk of 525 diseases was conducted to screen potential side effects.

Results

We identified eight plasma proteins that are significantly associated with the risk of varicose veins after Bonferroni correction (P < 2.495 × 10^-5), with five being protective (LUM, POSTN, RPN1, RSPO3, and VAT1) and three harmful (COLEC11, IRF3, and SARS2). Most identified proteins showed no pleiotropic effects except for COLLEC11. Bidirectional MR and MR Steiger testing excluded reverse causal relationship between varicose veins and prioritized proteins. The colocalization analysis indicated that COLEC11, IRF3, LUM, POSTN, RSPO3, and SARS2 shared the same causal variant with varicose veins. Finally, seven identified proteins replicated with alternative instruments except for VAT1. Furthermore, PheW-MR revealed that only IRF3 had potential harmful adverse side effects.

Conclusions

We identified eight potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that IRF3, LUM, POSTN, RSPO3, and SARS2 might be potential drug targets for varicose veins.

Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes

Prematurity is the foremost cause of death in children under 5 years of age. Genetics contributes to 25-40% of all preterm births (PTB) yet we still need to identify specific targets for intervention based on genetic pathways. This study involved the effect of region-specific non-synonymous variations and their transcript level mutational impact on protein functioning and stability by various in-silico tools. This investigation identifies potential therapeutic targets to manage the challenge of PTB, corresponding protein cavities and explores their binding interactions with intervening compounds. We searched 20 genes coding 55 PTB proteins from NCBI. Single Nucleotide Polymorphisms (SNPs) of concerned genes were extracted from ENSEMBL, and filtration of exonic variants (non-synonymous) was performed. Several in-silico downstream protein functional effect prediction tools were used to identify damaging variants. Rare coding variants were selected with an allele frequency of ≤1% in 1KGD, further supported by South Asian ALFA frequencies and GTEx gene/tissue expression database. CNN1, COL24A1, IQGAP2 and SLIT2 were identified with 7 rare pathogenic variants found in 17 transcript sequences. The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). After structural protein identification, homology modelling of CNN1, which has been previously reported as a biomarker for the prediction of PTB, was performed, followed by the stereochemical quality checks of the 3D model. Blind docking approach were used to search the binding cavities and molecular interactions with progesterone, ranked with energetic estimations. Molecular interactions of CNN1 with progesterone were investigated through LigPlot 2D. Further, molecular docking experimentation of CNN1 showed the significant interactions at S102, L105, A106, K123, Y124 with five selected PTB-drugs, Allylestrenol (-7.56 kcal/mol), Hydroxyprogesterone caproate (-8.19 kcal/mol), Retosiban (-9.43 kcal/mol), Ritodrine (-7.39 kcal/mol) and Terbutaline (-6.87 kcal/mol). Calponin-1 gene and its molecular interaction analysis could serve as an intervention target for the prevention of PTB.

iPseU-TWSVM: Identification of RNA pseudouridine sites based on TWSVM

Biological sequence analysis is an important basic research work in the field of bioinformatics. With the explosive growth of data, machine learning methods play an increasingly important role in biological sequence analysis. By constructing a classifier for prediction, the input sequence feature vector is predicted and evaluated, and the knowledge of gene structure, function and evolution is obtained from a large amount of sequence information, which lays a foundation for researchers to carry out in-depth research. At present, many machine learning methods have been applied to biological sequence analysis such as RNA gene recognition and protein secondary structure prediction. As a biological sequence, RNA plays an important biological role in the encoding, decoding, regulation and expression of genes. The analysis of RNA data is currently carried out from the aspects of structure and function, including secondary structure prediction, non-coding RNA identification and functional site prediction. Pseudouridine (У) is the most widespread and rich RNA modification and has been discovered in a variety of RNAs. It is highly essential for the study of related functional mechanisms and disease diagnosis to accurately identify У sites in RNA sequences. At present, several computational approaches have been suggested as an alternative to experimental methods to detect У sites, but there is still potential for improvement in their performance. In this study, we present a model based on twin support vector machine (TWSVM) for У site identification. The model combines a variety of feature representation techniques and uses the max-relevance and min-redundancy methods to obtain the optimum feature subset for training. The independent testing accuracy is improved by 3.4% in comparison to current advanced У site predictors. The outcomes demonstrate that our model has better generalization performance and improves the accuracy of У site identification. iPseU-TWSVM can be a helpful tool to identify У sites.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains homeostasis inside the central nervous system (CNS). The BBB permeability of compounds is an important consideration during CNS-acting drug development and is difficult to formulate in a succinct manner. Clinical experiments are the most accurate method of measuring BBB permeability. However, they are time taking and labor-intensive. Therefore, numerous efforts have been made to predict the BBB permeability of compounds using computational methods. However, the accuracy of BBB permeability prediction models has always been an issue. To improve the accuracy of the BBB permeability prediction, we applied deep learning and machine learning algorithms to a dataset of 3,605 diverse compounds. Each compound was encoded with 1,917 features containing 1,444 physicochemical (1D and 2D) properties, 166 molecular access system fingerprints (MACCS), and 307 substructure fingerprints. The prediction performance metrics of the developed models were compared and analyzed. The prediction accuracy of the deep neural network (DNN), one-dimensional convolutional neural network, and convolutional neural network by transfer learning was found to be 98.07, 97.44, and 97.61%, respectively. The best performing DNN-based model was selected for the development of the “DeePred-BBB” model, which can predict the BBB permeability of compounds using their simplified molecular input line entry system (SMILES) notations. It could be useful in the screening of compounds based on their BBB permeability at the preliminary stages of drug development. The DeePred-BBB is made available at https://github.com/12rajnish/DeePred-BBB.

Systematic analysis of the potential off-target activities of osimertinib by computational target fishing

Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor used to treat non-small cell lung cancer. However, its off-targets are obscure, and systematic analysis of off-target activities remains to be performed. Here, we identified the off-targets of osimertinib using PharmMapper and DRAR-CPI and analyzed the intersected targets using the GeneMANIA and DAVID servers. A drug-target-pathway network was constructed to visualize the associations. The results showed that osimertinib is associated with 31 off-targets, 40 Kyoto Encyclopedia of Genes and Genomes pathways, and 9 diseases. Network analysis revealed that the targets were involved in cancer and other physiological processes. In addition to EGFR, molecular docking analysis showed that seven proteins, namely Janus kinase 3, peroxisome proliferator-activated receptor alpha, renin, mitogen-activated protein kinases, lymphocyte-specific protein tyrosine kinase, cell division protein kinase 2 and proto-oncogene tyrosine-protein kinase Src, could also be potential targets of osimertinib. In conclusion, osimertinib is predicted to target multiple proteins and pathways, resulting in the formation of an action network via which it exerts systematic pharmacological effects.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

Fast and Accurate Classification of Meta-Genomics Long Reads With deSAMBA

There is still a lack of fast and accurate classification tools to identify the taxonomies of noisy long reads, which is a bottleneck to the use of the promising long-read metagenomic sequencing technologies. Herein, we propose de Bruijn graph-based Sparse Approximate Match Block Analyzer (deSAMBA), a tailored long-read classification approach that uses a novel pseudo alignment algorithm based on sparse approximate match block (SAMB). Benchmarks on real sequencing datasets demonstrate that deSAMBA enables to achieve high yields and fast speed simultaneously, which outperforms state-of-the-art tools and has many potentials to cutting-edge metagenomics studies.

Critical assessment of AI in drug discovery

Introduction: Artificial Intelligence (AI) has become a component of our everyday lives, with applications ranging from recommendations on what to buy to the analysis of radiology images. Many of the techniques originally developed for other fields such as language translation and computer vision are now being applied in drug discovery. AI has enabled multiple aspects of drug discovery including the analysis of high content screening data, and the design and synthesis of new molecules.Areas covered: This perspective provides an overview of the application of AI in several areas relevant to drug discovery including property prediction, molecule generation, image analysis, and organic synthesis planning.Expert opinion: While a variety of machine learning methods are now being routinely used to predict biological activity and ADME properties, methods of representing molecules continue to evolve. Molecule generation methods are relatively new and unproven but hold the potential to access new, unexplored areas of chemical space. The application of AI in drug discovery will continue to benefit from dedicated research, as well as AI developments in other fields. With this pairing algorithmic advancements and high-quality data, the impact of AI in drug discovery will continue to grow in the coming years.

Exploring the Causality Between Hypothyroidism and Non-alcoholic Fatty Liver: A Mendelian Randomization Study

The etiology of non-alcoholic fatty liver disease (NAFLD) involves complex interaction of genetic and environmental factors. A large number of observational studies have shown that hypothyroidism contributes to a high risk of NAFLD. However, the exact causality is still unknown. Due to the progress of genome-wide association study (GWAS) and the discovery of Mendelian randomization (MR), it is possible to explore the causality between the two diseases. In this study, in order to research into the influence of intermediate phenotypes on outcome, nine independent genetic variants of hypothyroidism obtained from the GWAS were used as instrumental variables (IVs) to perform MR analysis on NAFLD. Since there was no heterogeneity between IVs (P = 0.70), a fixed-effects model was used. The correlation between hypothyroidism and NAFLD was evaluated by using inverse-variance weighted (IVW) method and weighted median method. Then the sensitivity test was analyzed. The results showed that there was a high OR (1.7578; 95%CI 1.1897–2.5970; P = 0.0046) and a low intercept (−0.095; P = 0.431). None of the genetic variants drove the overall result (P < 0.01). Simply, we proved for the first time that the risk of NAFLD increases significantly on patients with hypothyroidism. Furthermore, we explained possible causes of NAFLD caused by hypothyroidism.

Functional metabolomics innovates therapeutic discovery of traditional Chinese medicine derived functional compounds

Traditional Chinese medicines (TCMs) produce chemically diverse functional compounds that are importantly chemical resource for facilitating new drug discovery and development against a diversity of diseases. However, modern exploration of TCM derived functional compounds is significantly hindered by the inefficient elucidation of pharmacological functions over past decades, because conventional research methods are incapable of efficiently elucidating therapeutic potential of TCM conferred by multiple functional compounds. Functional metabolomics has the priority-capacity to characterize systems therapeutic actions of TCM by precisely capturing molecular interactions between disease response metabolite biomarkers (DRMB) and functional compounds (secondary metabolites), which underline pharmacological efficiency and associated therapeutic mechanisms. In this critical review, we innovatively summarize systems therapeutic feature of TCM derived functional compounds from a functional-metabolism perspective, then systems metabolic targets (SMT) identified by functional metabolomics method are strategically proposed to better understanding of therapeutic discovery of TCM derived functional compounds. In addition, we propose the perspective strategy as Spatial Temporal Operative Real Metabolomics (STORM) to considerably improve analytical capacity of functional metabolomics method by selectively incorporating the cutting edge technologies of mass spectrometry imaging, isotope-metabolic fluxomics, synthetic and biosynthetic chemistry, which could considerably enhance the precision and resolution of elucidating pharmacological efficiency and associated therapeutic mechanisms of TCM derived functional compounds. Collectively, such critical review is expected to provide novel perspective-strategy that could significantly improve modern exploration and exploitation of TCM derived functional compounds that further promote new drug discovery and development against the complex diseases.

Drug Affinity Responsive Target Stability (DARTS ) Assay to Detect Interaction Between a Purified Protein and a Small Molecule

Drug affinity responsive target stability (DARTS) assay is used to detect the interaction between a ligand and a protein based on the observation that some ligands can protect the target protein from degradation by proteases when mixed in a solution. To set up the assay, a ligand is first mixed with a purified candidate target protein or a total cell lysate that contains a candidate target protein. Then, different amounts of protease are added to the mixture to allow the enzyme to digest the protein in the mixture. After protease digestion, the candidate target protein is detected by assays such as western blot, silver staining, or Coomassie blue staining. In theory, the candidate protein should be protected by the ligand from protease digestion, which is reflected by higher abundance of the candidate protein in mixtures containing the ligand compared with the control treatment. There are a few significant advantages of DARTS: (a) the ligand does not need to be modified so the native ligand could be used; (b) the candidate target protein could be either purified protein or protein that is present in the total cell lysate; and (c) the assay can be used together with proteomics analysis to identify an unknown target protein. The assay is especially valuable to test the interaction between the ligand and membrane proteins that are often challenging to purify. In this chapter, we use Endosidin2 (ES2) and its target protein Arabidopsis thaliana EXO70A1 (AtEXO70A1) as an example to show the step-by-step procedure of the DARTS assay.

Macrocybin, a Natural Mushroom Triglyceride, Reduces Tumor Growth In Vitro and In Vivo through Caveolin-Mediated Interference with the Actin Cytoskeleton

Mushrooms have been used for millennia as cancer remedies. Our goal was to screen several mushroom species from the rainforests of Costa Rica, looking for new antitumor molecules. Mushroom extracts were screened using two human cell lines: A549 (lung adenocarcinoma) and NL20 (immortalized normal lung epithelium). Extracts able to kill tumor cells while preserving non-tumor cells were considered “anticancer”. The mushroom with better properties was Macrocybe titans. Positive extracts were fractionated further and tested for biological activity on the cell lines. The chemical structure of the active compound was partially elucidated through nuclear magnetic resonance, mass spectrometry, and other ancillary techniques. Chemical analysis showed that the active molecule was a triglyceride containing oleic acid, palmitic acid, and a more complex fatty acid with two double bonds. The synthesis of all possible triglycerides and biological testing identified the natural compound, which was named Macrocybin. A xenograft study showed that Macrocybin significantly reduces A549 tumor growth. In addition, Macrocybin treatment resulted in the upregulation of Caveolin-1 expression and the disassembly of the actin cytoskeleton in tumor cells (but not in normal cells). In conclusion, we have shown that Macrocybin constitutes a new biologically active compound that may be taken into consideration for cancer treatment.

A Review of Drug Side Effect Identification Methods

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial for various cellular biological and physiological processes. To gain more insight into the functional mechanisms involved, it is of fundamental importance to precisely identify pseudouridine sites in RNA. Several useful machine learning approaches have become available recently, with the increasing progress of next-generation sequencing technology; however, existing methods cannot predict sites with high accuracy. Thus, a more accurate predictor is required. In this study, a random forest-based predictor named RF-PseU is proposed for prediction of pseudouridylation sites. To optimize feature representation and obtain a better model, the light gradient boosting machine algorithm and incremental feature selection strategy were used to select the optimum feature space vector for training the random forest model RF-PseU. Compared with previous state-of-the-art predictors, the results on the same benchmark data sets of three species demonstrate that RF-PseU performs better overall. The integrated average leave-one-out cross-validation and independent testing accuracy scores were 71.4% and 74.7%, respectively, representing increments of 3.63% and 4.77% versus the best existing predictor. Moreover, the final RF-PseU model for prediction was built on leave-one-out cross-validation and provides a reliable and robust tool for identifying pseudouridine sites. A web server with a user-friendly interface is accessible at http://148.70.81.170:10228/rfpseu.