Drug Target Deconvolution Research Articles

Improved drug target deconvolution with PISA-DIA using an extended, overlapping temperature gradient.

Thermal proteome profiling (TPP) is a powerful tool for drug target deconvolution. Recently, data-independent acquisition mass spectrometry (DIA-MS) approaches have demonstrated significant improvements to depth and missingness in proteome data, but traditional TPP (a.k.a. CEllular Thermal Shift Assay "CETSA") workflows typically employ multiplexing reagents reliant on data-dependent acquisition (DDA). Herein, we introduce a new experimental design for the Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA). We highlight the proteome coverage and sensitivity achieved by using multiple overlapping thermal gradients alongside DIA-MS, which maximizes efficiencies in PISA sample concatenation and safeguards against missing protein targets that exist at high melting temperatures. We demonstrate our extended PISA-DIA design has superior proteome coverage as compared to using tandem-mass tags (TMT) necessitating DDA-MS analysis. Importantly, we demonstrate our PISA-DIA approach has the quantitative and statistical rigor using A-1331852, a specific inhibitor of BCL-xL. Due to the high melt temperature of this protein target, we utilized our extended multiple gradient PISA-DIA workflow to identify BCL-xL. We assert our novel overlapping gradient PISA-DIA-MS approach is ideal for unbiased drug target deconvolution, spanning a large temperature range whilst minimizing target dropout between gradients, increasing the likelihood of resolving the protein targets of novel compounds.

323 (PB103) - LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes

Background: Target identification is a critical step in elucidating the mechanism of action (MoA) for bioactive compounds. For target-based and phenotypic drug discovery pipelines, extensive potential target knowledge for a lead compound provides essential insights that enable potency and selectivity optimization. The tedious process of target deconvolution for a compound often necessitates a plethora of biochemical and biophysical techniques. To expand the toolbox of unbiased target ID approaches, we developed a drug target deconvolution workflow combining limited proteolysis with quantitative mass spectrometry (LiP-MS) that exploits the drug binding phenomena of protein structural alterations and steric hindrance.

A brief introduction to chemical proteomics for target deconvolution.

Drug-target relationships provide the basis for network-based polypharmacology, and target deconvolution is a key step in phenotypic-screening based drug discovery. Due to the complexity of the mammalian proteomics and the often-limited affinity of the lead compound, it is challenging to identify the drug targets, especially when the goal is to identify all targets. This paper attempts to provide a brief and comprehensive introduction to the various methods in chemical proteomics for target deconvolution by categorizing them into two groups: the biochemical enrichment and the proteomics-screening methods. Moreover, a brief introduction of related Mass Spectrometry techniques is also provided, together with recent progress. The data for this review were queried from Web of Science and PubMed, the keywords used were Drug targets, Target deconvolution, and Chemical Proteomics. A total of over 500 relevant articles, with a time limit from 1953 to 2022, were identified according to search strategy. Duplicate records and review articles were excluded by their titles and abstracts. Finally, we found about 120 articles matching our inclusion criteria, which covered representative research and reviews of various target discovery methods. Existing target discovery methods can be grouped into either biochemical enrichment or the proteomics-screening methods, with the recent emergence of a hybrid method combining these two such as lysine reactivity profiling. The advantage of the biochemical enrichment method is the ease of operation and the comprehensive target coverage. However, most biochemical enrichment methods require a high-affinity binding of the drug to the target proteins and cannot differentiate direct/indirect targets. The proteomics-screening methods do not require drug modification but have limited protein coverage, and most of them cannot differentiate direct/indirect targets. Although existing target discovery methods have greatly facilitated pharmacological research, each of these methods has advantages and disadvantages. New strategies/methods are needed to further improve both the coverage of the proteosome and the specificity.

Proteome-Wide Deconvolution of Drug Targets and Binding Sites by Lysine Reactivity Profiling.

Recently, numerous efforts have been devoted to identifying drug targets and binding sites in complex proteomes, which is of great importance in modern drug discovery. In this study, we developed a robust lysine reactivity profiling method to systematically study drug-binding targets and binding sites at the proteome level. This method is based on the principle that binding of a drug to a specific region of target proteins will change the reactivity of lysine residues that are located at this region, and these changes can be detected with an enrichable and lysine reactive probe. Coupled with data-independent acquisition (DIA), the known target proteins and corresponding binding sites were successfully revealed from K562 cell lysates for three model drugs: geldanamycin, staurosporine, and dasatinib. In addition, the drug-induced conformational changes of certain targets were also revealed by our method during the screening of staurosporine. The screening sensitivity of our method revealed from the screening of stuarosporine and dasatinib was comparable with that of thermal proteome profiling (TPP) or machine learning-based limited proteolysis (LiP-Quant). Overall, 21 and 4 kinase targets, including adenosine 5'-triphosphate (ATP)-binding targets, were identified for staurosporine and dasatinib in K562 cell lysates, respectively. We found that target proteins identified by TPP, LiP-Quant, and our method were complementary, emphasizing that the development of new methods that probe different properties of proteins is of great importance in drug target deconvolution. We also envision further applications of our method in proteome-wide probing multiple events that involve lysine reactivity changes.

Precipitate-Supported Thermal Proteome Profiling Coupled with Deep Learning for Comprehensive Screening of Drug Target Proteins.

Although thermal proteome profiling (TPP) acts as a popular modification-free approach for drug target deconvolution, some key problems are still limiting screening sensitivity. In the prevailing TPP workflow, only the soluble fractions are analyzed after thermal treatment, while the precipitate fractions that also contain abundant information of drug-induced stability shifts are discarded; the sigmoid melting curve fitting strategy used for data processing suffers from discriminations for a part of human proteome with multiple transitions. In this study, a precipitate-supported TPP (PSTPP) assay was presented for unbiased and comprehensive analysis of protein-drug interactions at the proteome level. In PSTPP, only these temperatures where significant precipitation is observed were applied to induce protein denaturation and the complementary information contained in both supernatant fractions and precipitate fractions was used to improve the screening specificity and sensitivity. In addition, a novel image recognition algorithm based on deep learning was developed to recognize the target proteins, which circumvented the problems that exist in the sigmoid curve fitting strategy. PSTPP assay was validated by identifying the known targets of methotrexate, raltitrexed, and SNS-032 with good performance. Using a promiscuous kinase inhibitor, staurosporine, we delineated 99 kinase targets with a specificity up to 83% in K562 cell lysates, which represented a significant improvement over the existing thermal shift methods. Furthermore, the PSTPP strategy was successfully applied to analyze the binding targets of rapamycin, identifying the well-known targets, FKBP1A, as well as revealing a few other potential targets.

Proteome Integral Solubility Alteration (PISA) Assay in Mammalian Cells for Deep, High-Confidence, and High-Throughput Target Deconvolution.

Chemical proteomics focuses on the drug-target-phenotype relationship for target deconvolution and elucidation of the mechanism of action-key and bottleneck in drug development and repurposing. Majorly due to the limits of using chemically modified ligands in affinity-based methods, new, unbiased, proteome-wide, and MS-based chemical proteomics approaches have been developed to perform drug target deconvolution, using full proteome profiling and no chemical modification of the studied ligand. Of note among them, thermal proteome profiling (TPP) aims to identify the target(s) by measuring the difference in melting temperatures between each identified protein in drug-treated versus vehicle-treated samples, with the thermodynamic interpretation of "protein melting" and curve fitting of all quantified proteins, at all temperatures, in each biological replicate. Including TPP, all the other chemical proteomics approaches often fail to provide target deconvolution with sufficient proteome depth, statistical power, throughput, and sustainability, which could hardly fulfill the final purpose of drug development. The proteome integral solubility alteration (PISA) assay provides no thermodynamic interpretation, but a throughput 10-100-fold compared to the other proteomics methods, high sustainability, much lower time of analysis and sample amount requirements, high confidence in results, maximal proteome coverage (~10,000 protein IDs), and up to five drugs / test molecules in one assay, with at least biological triplicates of each treatment. Each drug-treated or vehicle-treated sample is split into many fractions and exposed to a gradient of heat as solubility perturbing agent before being recomposed into one sample; each soluble fraction is isolated, then deep and quantitative proteomics is applied across all samples. The proteins interacting with the tested molecules (targets and off-targets), the activated mechanistic factors, or proteins modified during the treatment show reproducible changes in their soluble amount compared to vehicle-treated controls. As of today, the maximal multiplexing capability is 18 biological samples per PISA assay, which enables statistical robustness and flexible experimental design accommodation for fuller target deconvolution, including integration of orthogonal chemical proteomics methods in one PISA assay. Living cells for studying target engagement in vivo or, alternatively, protein extracts to identify in vitro ligand-interacting proteins can be studied, and the minimal need in sample amount unlocks target deconvolution using primary cells and their derived cultures. This protocol was validated in: J Biol Chem (2021), DOI: 10.1016/j.jbc.2021.10153 Graphical abstract.

Drug Target Identification in Tissues by Thermal Proteome Profiling.

Drug target deconvolution can accelerate the drug discovery process by identifying a drug's targets (facilitating medicinal chemistry efforts) and off-targets (anticipating toxicity effects or adverse drug reactions). Multiple mass spectrometry-based approaches have been developed for this purpose, but thermal proteome profiling (TPP) remains to date the only one that does not require compound modification and can be used to identify intracellular targets in living cells. TPP is based on the principle that the thermal stability of a protein can be affected by its interactions. Recent developments of this approach have expanded its applications beyond drugs and cell cultures to studying protein-drug interactions and biological phenomena in tissues. These developments open up the possibility of studying drug treatment or mechanisms of disease in a holistic fashion, which can result in the design of better drugs and lead to a better understanding of fundamental biology.

Mechanical stress induced protein precipitation method for drug target screening

The process of protein precipitation can be used to decipher the interaction of ligand and protein. For example, the classic Thermal Proteome Profiling (TPP) method uses heating as the driving force for protein precipitation, to discover the drug target protein. Under heating or other denature forces, the target protein that binds with the drug compound will be more resistant to precipitation than the free protein. Similar to thermal stress, mechanical stress can also induce protein precipitation. Upon mechanical stress, protein will gradually precipitate along with protein conformational changes, which can be exploited for the study of the ligand-protein interaction. Herein, we proposed a Mechanical Stress Induced Protein Precipitation (MSIPP) method for drug target deconvolution. Its streamlined workflow allows in situ sample preparation on the surface of microparticles, from protein precipitation to digestion. The mechanical stress was generated by vortexing the slurry of protein solution and microparticle materials. The mechanical stress induced protein precipitate was captured by the microparticles, which guarantees the MSIPP method to be scalable and user-friendly. The MSIPP method was successfully applied to four drug compounds, Methotrexate, Raltitrexed, SHP099, Geldanamycin and a pan-inhibitor of protein kinases, Staurosporine. Besides, DHFR was demonstrated to be a target of Raltitrexed, which has not been revealed by any other modification-free drug target discovery method yet. Thus, MSIPP is a complementary method to other drug target screening methods.

Recent advances in proteome-wide label-free target deconvolution for bioactive small molecules.

Small-molecule drugs modulate biological processes and disease states through engagement of target proteins in cells. Assessing drug-target engagement on a proteome-wide scale is of utmost importance in better understanding the molecular mechanisms of action of observed beneficial and adverse effects, as well as in developing next generation tool compounds and drugs with better efficacies and specificities. However, systematic assessment of drug-target engagement has been an arduous task. With the continuous development of mass spectrometry-based proteomics instruments and techniques, various chemical proteomics approaches for drug target deconvolution (i.e., the identification of molecular target for drugs) have emerged. Among these, the label-free target deconvolution approaches that do not involve the chemical modification of compounds of interest, have gained increased attention in the community. Here we provide an overview of the basic principles and recent biological applications of the most important label-free methods including the cellular thermal shift assay, pulse proteolysis, chemical denaturant and protein precipitation, stability of proteins from rates of oxidation, drug affinity responsive target stability, limited proteolysis, and solvent-induced protein precipitation. The state-of-the-art technical implications and future outlook for the label-free approaches are also discussed.

LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes

Background: Target identification is a critical step in elucidating the mechanism of action (MoA) for bioactive compounds. For phenotypic drug discovery pipelines, current unbiased, label-free chemoproteomics-based methods rely predominantly on the modulation of target thermal stability upon drug binding. We developed an automated drug target deconvolution workflow combining limited proteolysis with mass spectrometry (LiP-Quant) that exploits protein structural alterations, as well as steric effects driven by drug binding. A major advantage of LiP-Quant is its unique focus on the detection of signature peptides that discern ligand binding, peptides that are generated by a limited digestion and identified by proteomic analysis. Here we demonstrate the performance of LiP-Quant using two well-characterized kinase inhibitors (KIs), Selumetinib (SE) and Staurosporine (ST), as well as two natural product-derived phosphatase inhibitors (PIs) Calyculin A and Fostriecin in human cell lysate. Furthermore, LiP-Quant can be deployed to estimate in-lysate EC50 value of compound binding.

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.

Abstract 6404: LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes

Abstract Background Target identification is a critical step in elucidating the mechanism of action (MoA) for bioactive compounds. For phenotypic drug discovery pipelines, current unbiased, label-free chemoproteomics-based methods rely predominantly on the modulation of target thermal stability upon drug binding. We developed an automated drug target deconvolution workflow combining limited proteolysis with mass spectrometry (LiP-Quant) that exploits protein structural alterations, as well as steric effects driven by drug binding. A major advantage of LiP-Quant is its unique focus on the detection of signature peptides that discern ligand binding, peptides that are generated by a limited digestion and identified by proteomic analysis. Here we demonstrate the performance of LiP-Quant using two well-characterized kinase inhibitors (KIs), Selumetinib (SE) and Staurosporine (ST), as well as two natural product-derived phosphatase inhibitors (PIs) Calyculin A and Fostriecin in human cell lysate. Furthermore, LiP-Quant can be deployed to estimate in-lysate EC50 value of compound binding. Methods Mechanically sheared HeLa cell lysate was incubated with compound at multiple concentrations. Next, a limited digest was performed using proteinase K. Finally, the limited digests were processed to peptides with trypsin for mass spectrometry analysis. A project-specific spectral library was generated using data-dependent acquisition (DDA) mass spectrometry and for quantitative analysis data-independent acquisition (DIA) data were recorded and analyzed using Spectronaut Pulsar X. Results Herein, we demonstrate the ability of our LiP-Quant approach to identify unique peptides generated by the binding of either a highly specific (selumetinib) or broad specificity (staurosporine) KI in human cell line lysate. While > 20 kinases met the qualifying LiP-score cutoff for staurosporine, the direct targets MEK1 and MEK2 were clearly identified as main hits in the unbiased ranking by LiP scores. Both cases represent a highly specific enrichment given that we quantified > 100,000 peptides in each of the experiments. These findings confirm our approach's ability to identify genuine drug targets regardless of drug specificity in a complex biological matrix. To characterize the specificity of LiP-Quant, we treated lysate with two separate protein PIs. According to literature calyculin A targets protein phosphatase 1 and 2 (PP1A and PP2A) and fostriecin also targets PP2A, in addition to protein phosphatase 4 (PP4C) but does not bind PP1A. Robust phosphatase identification was achieved for both calyculin A and fostriecin treatment. Importantly, with LiP-Quant, we could recapitulate the known relative affinities of the PIs towards their respective targets. Conclusions Collectively, this data demonstrate that LiP-Quant can be used to effectively identify protein drug targets and characterize the binding properties in complex proteomes, without compound modification or labeling, and regardless of the specificity of the compound. These capabilities make LiP-Quant a powerful target deconvolution and identification strategy. Citation Format: Yuehan Feng, Nigel Beaton, Roland Bruderer, Ilaria Piazza, Paola Picotti, Lukas Reiter. LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6404.

Drug target deconvolution by chemical proteomics

Drug target identification can help establish the association between drug activity and phenotypes, elucidate the mechanism of action, discover off-target effects and drug resistance mechanisms, discover new targets for therapeutic drugs, and predict possible side effects and toxicity at an early stage of drug discovery, thereby reducing the risk of failure in drug development. Although rapid progress has been made in scientific discoveries and technological advancements, the identification of drug targets is still a daunting task. Herein, the research progress in drug target identification during the past decade, especially novel technologies without chemical labeling, have been reviewed.

Comprehensive chemical proteomics for target deconvolution of the redox active drug auranofin

Chemical proteomics encompasses novel drug target deconvolution methods in which compound modification is not required. Herein we use Thermal Proteome Profiling, Functional Identification of Target by Expression Proteomics and multiplexed redox proteomics for deconvolution of auranofin targets to aid elucidation of its mechanisms of action. Auranofin (Ridaura®) was approved for treatment of rheumatoid arthritis in 1985. Because several clinical trials are currently ongoing to repurpose auranofin for cancer therapy, comprehensive characterization of its targets and effects in cancer cells is important. Together, our chemical proteomics tools confirmed thioredoxin reductase 1 (TXNRD1, EC:1.8.1.9) as a main auranofin target, with perturbation of oxidoreductase pathways as the top mechanism of drug action. Additional indirect targets included NFKB2 and CHORDC1. Our comprehensive data can be used as a proteomic signature resource for further analyses of the effects of auranofin. Here we also assessed the orthogonality and complementarity of different chemical proteomics methods that can furnish invaluable mechanistic information and thus the approach can facilitate drug discovery efforts in general.

Abstract C015: Limited proteolysis coupled to mass spectrometry (LiP-Quant), a novel drug target deconvolution strategy

Abstract Background: High attrition rates in target-centric drug development approaches, as well as a limited number of targets, have shifted the focus of drug development back towards phenotypic screening. In parallel, novel proteomics-based target deconvolution approaches to drug target identification have gained popularity. Limited proteolysis coupled with mass spectrometry (LiP-MS) is a new target deconvolution technique that exploits protein structural alterations, as well as steric effects driven by drug binding. A major advantage of LiP-MS is its unique focus on detection of signature peptides that report on ligand binding, which are generated by a limited digestion and identified by proteomic analysis. Here we demonstrate the performance of LiP-MS using two well-characterized kinase inhibitors, Selumetinib (SE) and Staurosporine (ST), as well as two natural product-derived phosphatase inhibitors Calyculin A and Fostriecin in HeLa cell lysate. Additionally, we introduce a LiP workflow that can be used to dissect protein-drug interactions in a more nuanced manner. Results Herein, we demonstrate the ability of our LiP-MS approach to identify unique peptides generated by the binding of either a highly-specific (SE) or broad specificity (ST) kinase inhibitor. Taking the top 200 identified target candidate peptides ranked by LiP score from the broad inhibitor (staurosporine), GO enrichment analysis confirmed a highly significant 3-fold enrichment for kinase targets (p < 2E-5). For the more challenging selumetinib experiment, the direct targets MEK1 and MEK2 were clearly identified as main hits with 5 peptides in the top 16 (sorted by LiP score). This represents a highly specific enrichment given that we quantified > 100,000 peptide precursors in this experiment. These findings confirm our approach’s ability to identify genuine drug targets regardless of drug specificity. To characterize the specificity of LiP-MS, we treated lysate with two separate protein phosphatase inhibitors. According to literature calyculin A targets protein phosphatase 1 and 2 (PP1A and PP2A) and fostriecin also targets PP2A, in addition to protein phosphatase 4 (PP4C) but does not bind PP1A. We identified both targets of calyculin A, with 14 of the top 15 peptides by LiP score being from either PP2A or PP1A. Robust phosphatase identification (three main targets PP2A, PP4C and PP6C) was also observed with fostriecin treatment with a different profile from calyculin A. In line with literature, the hits of fostriecin did not include PP1A, despite the high homology of the protein phosphatase family. Finally, taking advantage of the peptide resolution of LiP-MS, we employed this workflow to investigate the Ca2+-induced conformational switch in the recombinant protein calmodulin by monitoring structure-specific peptides which cover its entire amino acid sequence. Conclusions Collectively, this data demonstrates that LiP-MS can be used to effectively identify protein drug targets and characterize the binding properties, regardless of the specificity of the compound. These capabilities make LiP-MS a powerful target deconvolution and identification strategy. In addition, we envision the development of LiP-MS for recombinant proteins as a high-throughput strategy for binding site characterization of protein-small molecule and protein-protein interactions. Citation Format: Yuehan Feng, Nigel Beaton, Roland Bruderer, Ilaria Piazza, Paola Picotti, Lukas Reiter. Limited proteolysis coupled to mass spectrometry (LiP-Quant), a novel drug target deconvolution strategy [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C015. doi:10.1158/1535-7163.TARG-19-C015

Epigenetic drug target deconvolution by mass spectrometry-based technologies.

The identification of the full target spectrum of active molecules, known as target deconvolution, has become an indispensable step during the drug discovery process. It is now achievable thanks to mass spectrometry-based technologies. Here we discuss these approaches in the context of epigenetic drug discovery.

Abstract 2755: Limited proteolysis coupled to mass spectrometry (LiP-MS), a novel drug target deconvolution strategy

Abstract Background High attrition rates in target-centric drug development approaches, as well as a limited number of targets, have shifted the focus of drug development back towards phenotypic screening. In parallel, novel proteomics-based target deconvolution approaches to drug target identification have gained popularity. Limited proteolysis coupled with mass spectrometry (LiP-MS) is a new target deconvolution technique that exploits protein structural alterations driven by drug binding. A major advantage of LiP-MS is its unique focus on detection of peptides that report on ligand binding induced structural changes that are generated by a limited digestion and identified by proteomic analysis. Here we demonstrate the performance of LiP-MS using the protein phosphatase inhibitor Calyculin A, as well as two well known kinase inhibitors, selumetinib (SE) and staurosporine (ST), in HeLa cell lysate. Materials and Methods Mechanically sheared HeLa cell lysate was incubated with compound at multiple concentrations. Next, a limited digest was performed using proteinase K. Finally, the limited digests were processed to peptides with trypsin for mass spectrometry analysis. A project-specific spectral library was generated using data-dependent acquisition (DDA) mass spectrometry and for quantitative analysis data-independent acquisition (DIA) data were recorded and analyzed using Spectronaut Pulsar X. Results LiP-MS identifies several peptides for the phosphatase inhibitor Calyculin A, with IC50 values of 52 nM and 17 nM for PP1A and PP2A respectively. Additionally, a previously unknown target PP1B was also identified among the same family, although with a higher IC50 (74 nM). Through structural inspection of ligand-sensitive peptides we were able to map the drug’s binding site within the phosphatases and predict distal conformational changes, demonstrating that LiP-MS can be used to provide structural insights to ligand-protein binding. Similar dose response relationships were observed for both specific (SE) and broad (ST) kinase inhibitors. Amongst the top 200 identified target candidate peptides ranked by LiP score, GO enrichment analysis confirmed a highly significant 3-fold enrichment for kinase targets (p < 0.00002) in ST-treated lysate, while no such enrichment was observed for SE. However, in SE-treated lysate robust identification of multiple MEK1 peptides, one of the compound’s main targets, was observed. In the case of both kinase inhibitors LiP peptides could be successfully mapped to ATP binding sites, confirming the ability of LiP-MS to model drug-bound protein structure. Conclusions This data demonstrates that LiP-MS can be used to effectively identify protein drug targets and characterize the binding properties, regardless of the specificity of the compound. These capabilities make LiP-MS a powerful target deconvolution and identification strategy. Citation Format: Nigel Beaton, Roland Bruderer, Kristina Beeler, Nicholas Dupuis, Ilaria Piazza, Paola Picotti, Lukas Reiter. Limited proteolysis coupled to mass spectrometry (LiP-MS), a novel drug target deconvolution strategy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2755.

Unbiased Forward Genetic Screening with Chemical Mutagenesis to Uncover Drug-Target Interactions.

The steadily increasing throughput in next-generation sequencing technologies is revolutionizing a number of fields in biology. One application requiring massive parallel sequencing is forward genetic screening based on chemical mutagenesis. Such screens interrogate the entire genome in an entirely unbiased fashion and can be applied to a number of research questions. CRISPR/Cas9-based screens, which are largely limited to a gene's loss of function, have already been very successful in identifying drug targets and pathways related to the drug's mode of action. By inducing single nucleotide changes using an alkylating reagent, it is possible to generate amino acid changes that perturb the interaction between a drug and its direct target, resulting in drug resistance. This chemogenomic approach combined with latest sequencing technologies allows deconvolution of drug targets and characterization of drug-target binding interfaces at amino acid resolution, therefore nicely complementing existing biochemical approaches. Here we describe a general protocol for a chemical mutagenesis-based forward genetic screen applicable for drug-target deconvolution.

Translating the Trypanosome Surface

This talk will cover our current understanding of the African trypanosome cell surface, and in particular the biosynthesis and roles of protein N‐glycosylation and glycosylphosphatidylinositol (GPI) membrane anchoring in this organism.The peculiarities of these post‐translational modifications, and associated nucleotide sugar metabolic pathways, provide drug discovery opportunities for trypanosomiasis.The desire to exploit these, and several other, discoveries led groups at The University of Dundee to establish a Drug Discovery Unit that both translates parasite biology through to preclinical candidates and develops drug candidates phenotypically, performing drug target deconvolution at the end of the process. Examples of both approaches will be given.The Drug Discovery Unit capabilities established for parasitic diseases are also applied to innovative targets in several other therapeutic areas, and some of these will be briefly mentioned.Support or Funding InformationWe are particularly grateful to The Wellcome Trust, which has supported our work on trypanosomatids since 1988. We also thank all of our colleagues in the Division of Biological Chemistry & Drug Discovery and the Drug Discovery Unit who's work we are drawing on in this overview. Finally, we thank Alice and C.C. Wang for the great honor of giving this award lecture.

Functional Identification of Target by Expression Proteomics (FITExP) reveals protein targets and highlights mechanisms of action of small molecule drugs.

Phenomenological screening of small molecule libraries for anticancer activity yields potentially interesting candidate molecules, with a bottleneck in the determination of drug targets and the mechanism of anticancer action. We have found that, for the protein target of a small-molecule drug, the abundance change in late apoptosis is exceptional compared to the expectations based on the abundances of co-regulated proteins. Based on this finding, a novel method to drug target deconvolution is proposed. In a proof of principle experiment, the method yielded known targets of several common anticancer agents among a few (often, just one) likely candidates identified in an unbiased way from cellular proteome comprising more than 4,000 proteins. A validation experiment with a different set of cells and drugs confirmed the findings. As an additional benefit, mapping most specifically regulated proteins on known protein networks highlighted the mechanism of drug action. The new method, if proven to be general, can significantly shorten drug target identification, and thus facilitate the emergence of novel anticancer treatments.