Protein Targets Of Small Molecules Research Articles

Proof of concept: Pull down assay using bovine serum albumin and an immunomodulator small molecule

In the past decade, there has been increasing interest in use of small molecules for immunomodulation. The affinity-based pull-down purification is an essential tool for target identification of small molecules and drug discovery. This study presents our recent efforts to investigate the cellular target(s) of Compound A, a small molecule with demonstrated immunomodulatory properties in human peripheral blood mononuclear cells (PBMCs). While we have previously observed the immunomodulatory activity of Compound A in PBMCs, the specific molecular targets underlying its effects remains elusive. To address this challenge, we synthesized a trifluoromethyl phenyl diazirine (TPD)-bearing trifunctional Probe 1 based on the chemical structure of Compound A, which could be used in a pull-down assay to efficiently bind to putative cellular targets via photoaffinity labelling. In this report, we utilized bovine serum albumin (BSA) as a model protein to establish a proof-of-concept in order to assess the suitability of Probe 1 for binding to an endogenous target. By the successful synthesis of Probe 1 and demonstrating the efficient binding of Probe 1 to BSA, we propose that this method can be used as a tool for further identification of potential protein targets of small molecules in living cells. Our findings provide a valuable starting point for further investigations into the molecular mechanisms underlying the immunomodulatory effects of Compound A.

Benjamin F. Cravatt III – Chemical Proteomics Trailblazer

AbstractThe advances in large‐scale bioanalytical methods and computational approaches have afforded tremendous insight into fundamental mechanisms of health and disease as well as provided new opportunities for therapeutic development. However, the majority of proteins in humans remain undrugged or lack chemical probes to explore their functions in vivo. The pioneering studies on activity‐based protein profiling (ABPP) by Ben Cravatt and his lab members have provided a systematic and generalizable approach to address this major challenge in chemical biology and drug discovery. Notably, the Cravatt laboratory paved the way for employing activity‐ and reactivity‐based chemical probes with quantitative proteomics to identify small molecule protein targets and discover unpredicted protein hot spots for native and synthetic ligands. Importantly, these chemical proteomic methods have revealed new protein targets and mechanisms of action for drug discovery and have been widely adopted by many other laboratories around the world to discover new biology and pharmacology. The recognition of Ben Cravatt as a co‐recipient of the 2022 Wolf Prize in Chemistry for this trailblazing work in chemical proteomics is most well‐deserved.

Epigenetic Target Profiler: A Web Server to Predict Epigenetic Targets of Small Molecules.

The identification of protein targets of small molecules is essential for drug discovery. With the increasing amount of chemogenomic data in the public domain, multiple ligand-based models for target prediction have emerged. However, these models are generally biased by the number of known ligands for different targets, which involves an under-representation of epigenetic targets, and despite the increasing importance of epigenetic targets in drug discovery, there are no open tools for epigenetic target prediction. In this work, we introduce Epigenetic Target Profiler (ETP), a freely accessible and easy-to-use web application for the prediction of epigenetic targets of small molecules. For a query compound, ETP predicts its bioactivity profile over a panel of 55 different epigenetic targets. To that aim, ETP uses a consensus model based on two binary classification models for each target, relying on support vector machines and built on molecular fingerprints of different design. A distance-to-model parameter related to the reliability of the predictions is included to facilitate their interpretability and assist in the identification of small molecules with potential epigenetic activity. Epigenetic Target Profiler is freely available at http://www.epigenetictargetprofiler.com.

A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound.

A Semi-Quantitative Drug Affinity Responsive Target Stability (DARTS) assay for studying Rapamycin/mTOR interaction.

Drug Affinity Responsive Target Stability (DARTS) is a robust method for detection of novel small molecule protein targets. It can be used to verify known small molecule-protein interactions and to find potential protein targets for natural products. Compared with other methods, DARTS uses native, unmodified, small molecules and is simple and easy to operate. In this study, we further enhanced the data analysis capabilities of the DARTS experiment by monitoring the changes in protein stability and estimating the affinity of protein-ligand interactions. The protein-ligand interactions can be plotted into two curves: a proteolytic curve and a dose-dependence curve. We have used the mTOR-rapamycin interaction as an exemplary case for establishment of our protocol. From the proteolytic curve we saw that the proteolysis of mTOR by pronase was inhibited by the presence of rapamycin. The dose-dependency curve allowed us to estimate the binding affinity of rapamycin and mTOR. This method is likely to be a powerful and simple method for accurately identifying novel target proteins and for the optimization of drug target engagement.

Discovery of the Anti-Tumor Mechanism of Calycosin Against Colorectal Cancer by Using System Pharmacology Approach.

BackgroundThe aim of our study was to elucidate the biological targets and pharmacological mechanisms for calycosin (CC) against colorectal cancer (CRC) through an approach of system pharmacology.Material/MethodsUsing a web-based platform, all CRC-causing genes were identified using a database of gene-disease associations (DisGeNET), and all well-known genes of CC identified using the databases of prediction of protein targets of small molecules (Swiss Target Prediction), drug classification, and target prediction (SuperPred). The carefully selected genes of CRC and CC were concurrently constructed by using a database of functional protein association networks (STRING), and use of software for visualizing complex networks (Cytoscape), characterized with production of protein-protein interaction (PPI) network of CC against CRC. The important biological targets of CC against CRC were identified through topological analysis, then the biological processes and molecular pathways of CC against CRC were further revealed for testing these important biotargets by enrichment assays.ResultsWe found that the key predictive targets of CC against CRC were estrogen receptor 2 (ESR2), ATP-binding cassette sub-family G member 2 (ABCG2), breast cancer type 1 susceptibility protein (BRCA1), estrogen receptor 1 (ESR1), cytochrome p450 19A1 (CYP19A1), and epidermal growth factor receptor (EGFR). Visual analysis revealed that the biological processes of CC against CRC were positively linked to hormonal metabolism, regulation of genes, transport, cell communication, and signal transduction. Further, the interrelated molecular pathways were chiefly related to endogenous nuclear estrogen receptor alpha network, forkhead box protein A1 (FOXA1) transcription factor network, activating transcription factor 2 (ATF2) transcription factor network, regulation of telomerase, plasma membrane estrogen receptor signaling, estrogen biosynthesis, androgen receptor, FOXA transcription factor networks, estrogen biosynthesis, and phosphorylation of repair proteins.ConclusionsUse of system pharmacology revealed the biotargets, biological processes, and pharmacological pathways of CC against CRC. Intriguingly, the identifiable predictive biomolecules are likely potential targets for effectively treating CRC.

SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules.

SwissTargetPrediction is a web tool, on-line since 2014, that aims to predict the most probable protein targets of small molecules. Predictions are based on the similarity principle, through reverse screening. Here, we describe the 2019 version, which represents a major update in terms of underlying data, backend and web interface. The bioactivity data were updated, the model retrained and similarity thresholds redefined. In the new version, the predictions are performed by searching for similar molecules, in 2D and 3D, within a larger collection of 376 342 compounds known to be experimentally active on an extended set of 3068 macromolecular targets. An efficient backend implementation allows to speed up the process that returns results for a druglike molecule on human proteins in 15–20 s. The refreshed web interface enhances user experience with new features for easy input and improved analysis. Interoperability capacity enables straightforward submission of any input or output molecule to other on-line computer-aided drug design tools, developed by the SIB Swiss Institute of Bioinformatics. High levels of predictive performance were maintained despite more extended biological and chemical spaces to be explored, e.g. achieving at least one correct human target in the top 15 predictions for >70% of external compounds. The new SwissTargetPrediction is available free of charge (www.swisstargetprediction.ch).

Discovering Putative Protein Targets of Small Molecules: A Study of the p53 Activator Nutlin.

Small molecule drugs bind to a pocket in disease causing target proteins based on complementarity in shape and physicochemical properties. There is a likelihood that other proteins could have binding sites that are structurally similar to the target protein. Binding to these other proteins could alter their activities leading to off target effects of the drug. One such small molecule drug Nutlin binds the protein MDM2, which is upregulated in several types of cancer and is a negative regulator of the tumor suppressor protein p53. To investigate the off target effects of Nutlin, we present here a shape-based data mining effort. We extracted the binding pocket of Nutlin from the crystal structure of Nutlin bound MDM2. We next mined the protein structural database (PDB) for putative binding pockets in other human protein structures that were similar in shape to the Nutlin pocket in MDM2 using our topology-independent structural superimposition tool CLICK. We detected 49 proteins which have binding pockets that were structurally similar to the Nutlin binding site of MDM2. All of the potential complexes were evaluated using molecular mechanics and AutoDock Vina docking scores. Further, molecular dynamics simulations were carried out on four of the predicted Nutlin-protein complexes. The binding of Nutlin to one of these proteins, gamma glutamyl hydrolase, was also experimentally validated by a thermal shift assay. These findings provide a platform for identifying potential off-target effects of existing/new drugs and also opens the possibilities for repurposing drugs/ligands.

Emerging Approaches for the Identification of Protein Targets of Small Molecules - A Practitioners' Perspective.

Small-molecule (SM) leads in the early drug discovery pipeline are progressed primarily based on potency against the intended target(s) and selectivity against a very narrow slice of the proteome. So, why is there a tendency to wait until SMs are matured before probing for a deeper mechanistic understanding? For one, there is a concern about the interpretation of complex -omic data outputs and the resources needed to test these hypotheses. However, with recent advances in broad endpoint profiling assays that have companion reference databases and refined technology integration strategies, we argue that data complexity can translate into meaningful decision-making. This same strategy can also prioritize phenotypic screening hits to increase the likelihood of accessing unprecedented target space. In this Perspective. we will highlight a cohesive process that supports SM hit prosecution, providing a data-driven rationale and a suite of methods for direct identification of SM targets driving relevant biological end points.

Advanced Activity-Based Protein Profiling Application Strategies for Drug Development.

Drug targets and modes of action remain two of the biggest challenges in drug development. To address these problems, chemical proteomic approaches have been introduced to profile targets in complex proteomes. Activity-based protein profiling (ABPP) is one of a growing number chemical proteomic approaches that uses small-molecule chemical probes to understand the interaction mechanisms between compounds and targets. ABPP can be used to identify the protein targets of small molecules and even the active sites of target proteins. This review focuses on the overall workflow of the ABPP technology and on additional advanced strategies for target identification and/or drug discovery. Herein, we mainly describe the design strategies for small-molecule probes and discuss the ways in which these probes can be used to identify targets and even validate the interactions of small molecules with targets. In addition, we discuss some basic strategies that have been developed to date, such as click chemistry-ABPP, competitive strategies and, recently, more advanced strategies, including isoTOP-ABPP, fluoPol-ABPP, and qNIRF-ABPP. The isoTOP-ABPP strategy has been coupled with quantitative proteomics to identify the active sites of proteins and explore whole proteomes with specific amino acid profiling. FluoPol-ABPP combined with HTS can be used to discover new compounds for some substrate-free enzymes. The qNIRF-ABPP strategy has a number of applications for in vivo imaging. In this review, we will further discuss the applications of these advanced strategies.

Direct Proximity Tagging of Small Molecule Protein Targets Using an Engineered NEDD8 Ligase.

Identifying the protein targets of bioactive small molecules remains a major problem in the discovery of new chemical probes and therapeutics. While activity-based probes and photo-cross-linkers have had success in identifying protein targets of small molecules, each technique has limitations. Here we describe a method for direct proximity tagging of proteins that bind small molecules. We engineered a promiscuous ligase based on the NEDD8 conjugating enzyme, Ubc12, which can be covalently linked to a small molecule of interest. When target proteins bind the small molecule, they are directly labeled on surface lysines with a biotinylated derivative of the small ubiquitin homologue, NEDD8. This unique covalent tag can then be used to identify the small molecule binding proteins. Utilizing the drug dasatinib, we have shown that dasatinib-directed NEDDylation occurs for known endogenous protein binders in complex cell lysates. In addition, we have been able to improve NEDDylation efficiency through rational mutagenesis. Finally, we have shown that affinity-directed NEDDylation can be applied to two other protein-ligand interactions beyond kinases. Proximity tagging using this engineered ligase requires direct binding of the target and, thus, provides a useful and orthogonal approach to facilitate small molecule target identification.

Hitting the target.

Thermal profiling in combination with quantitative proteomics makes it possible to identify cellular membrane protein targets of small molecules.

ChemInform Abstract: Hide and Seek: Identification and Confirmation of Small Molecule Protein Targets

AbstractReview: 65 refs.

Hide and seek: Identification and confirmation of small molecule protein targets.

Target identification and confirmation for small molecules is often the rate limiting step in drug discovery. A robust method to identify proteins addressed by small molecules is affinity chromatography using chemical probes. These usually consist of the compound of interest equipped with a linker molecule and a proper tag. Recently, methods emerged that allow the identification of protein targets without prior functionalization of the small molecule of interest. The digest offers an update on the newest developments in the area of target identification with special focus on confirmation techniques.

Comparison of ultra‐fast 2D and 3D ligand and target descriptors for side effect prediction and network analysis in polypharmacology

Some existing computational methods are used to infer protein targets of small molecules and can therefore be used to find new targets for existing drugs, with the goals of re-directing the molecule towards a different therapeutic purpose or explaining off-target effects due to multiple targeting. Inherent limitations, however, arise from the fact that chemical analogy is calculated on the basis of common frameworks or scaffolds and also because target information is neglected. The method we present addresses these issues by taking into account 3D information from both the ligand and the target. ElectroShape is an established method for ultra-fast comparison of the shapes and charge distributions of ligands that is validated here for prediction of on-target activities, off-target profiles and adverse effects of drugs and drug-like molecules taken from the DrugBank database. The method is shown to predict polypharmacology profiles and relate targets from two complementary viewpoints (ligand- and target-based networks). The open-access web tool presented here (http://ub.cbm.uam.es/chemogenomics/) allows interactive navigation in a unified 'pharmacological space' from the viewpoints of both ligands and targets. It also enables prediction of pharmacological profiles, including likely side effects, for new compounds. We hope this web interface will help many pharmacologists to become aware of this new paradigm (up to now mostly used in the realm of the so-called 'chemical biology') and encourage its use with a view to revealing 'hidden' relationships between new and existing compounds and pharmacologically relevant targets.

Affinity‐based proteomic profiling: Problems and achievements

Affinity chromatography becomes a more and more popular method used in proteomic studies for separation of various groups of proteins (subproteomes). The review highlights the role of affinity chromatography fractionation for proteomic profiling of the most of intensively studied groups of proteins including cyclic nucleotide-binding proteins, protein kinases (kinomes), phosphoproteins, glycoproteins, ubiquitinated proteins. Special attention is paid to the use of affinity chromatography for the characterization of small-molecule protein targets. The latter is especially important for the elucidation of direct protein targets of potential drug substances for evaluation of their possible side-effects or additional pharmacological application.

Target Identification Using Drug Affinity Responsive Target Stability (DARTS)

Drug Affinity Responsive Target Stability is a general methodology for identifying and studying protein-ligand interactions. The technique is based on the principle that when a small molecule compound binds to a protein, the interaction stabilizes the target protein's structure such that it becomes protease resistant. DARTS is particularly useful for the initial identification of the protein targets of small molecules, but can also be used to validate potential protein-ligand interactions predicted or identified by other means and to estimate the affinity of interactions. The approach is simple and advantageous because it can be performed using crude cell lysates and other complex protein mixtures (without requiring purified proteins), and uses native, unmodified small molecules. The protocols provided in this article describe the general approach for performing DARTS experiments, which can be easily modified and scaled to fit the criteria and purpose of any individual project.

From in silico target prediction to multi-target drug design: Current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.

Identification of Direct Protein Targets of Small Molecules

Small-molecule target identification is a vital and daunting task for the chemical biology community as well as for researchers interested in applying the power of chemical genetics to impact biology and medicine. To overcome this “target ID” bottleneck, new technologies are being developed that analyze protein–drug interactions, such as drug affinity responsive target stability (DARTS), which aims to discover the direct binding targets (and off targets) of small molecules on a proteome scale without requiring chemical modification of the compound. Here, we review the DARTS method, discuss why it works, and provide new perspectives for future development in this area.

Empirical Bayes Analysis of Quantitative Proteomics Experiments

BackgroundAdvances in mass spectrometry-based proteomics have enabled the incorporation of proteomic data into systems approaches to biology. However, development of analytical methods has lagged behind. Here we describe an empirical Bayes framework for quantitative proteomics data analysis. The method provides a statistical description of each experiment, including the number of proteins that differ in abundance between 2 samples, the experiment's statistical power to detect them, and the false-positive probability of each protein.Methodology/Principal FindingsWe analyzed 2 types of mass spectrometric experiments. First, we showed that the method identified the protein targets of small-molecules in affinity purification experiments with high precision. Second, we re-analyzed a mass spectrometric data set designed to identify proteins regulated by microRNAs. Our results were supported by sequence analysis of the 3′ UTR regions of predicted target genes, and we found that the previously reported conclusion that a large fraction of the proteome is regulated by microRNAs was not supported by our statistical analysis of the data.Conclusions/SignificanceOur results highlight the importance of rigorous statistical analysis of proteomic data, and the method described here provides a statistical framework to robustly and reliably interpret such data.