Deconvolution Strategy Research Articles

Identification of antimalarial targets of chloroquine by a combined deconvolution strategy of ABPP and MS-CETSA

BackgroundMalaria is a devastating infectious disease that disproportionally threatens hundreds of millions of people in developing countries. In the history of anti-malaria campaign, chloroquine (CQ) has played an indispensable role, however, its mechanism of action (MoA) is not fully understood.MethodsWe used the principle of photo-affinity labeling and click chemistry-based functionalization in the design of a CQ probe and developed a combined deconvolution strategy of activity-based protein profiling (ABPP) and mass spectrometry-coupled cellular thermal shift assay (MS-CETSA) that identified the protein targets of CQ in an unbiased manner in this study. The interactions between CQ and these identified potential protein hits were confirmed by biophysical and enzymatic assays.ResultsWe developed a novel clickable, photo-affinity chloroquine analog probe (CQP) which retains the antimalarial activity in the nanomole range, and identified a total of 40 proteins that specifically interacted and photo-crosslinked with CQP which was inhibited in the presence of excess CQ. Using MS-CETSA, we identified 83 candidate interacting proteins out of a total of 3375 measured parasite proteins. At the same time, we identified 8 proteins as the most potential hits which were commonly identified by both methods.ConclusionsWe found that CQ could disrupt glycolysis and energy metabolism of malarial parasites through direct binding with some of the key enzymes, a new mechanism that is different from its well-known inhibitory effect of hemozoin formation. This is the first report of identifying CQ antimalarial targets by a parallel usage of labeled (ABPP) and label-free (MS-CETSA) methods.

Cellular heterogeneity characterization of the acute inflammation in response to myocardial infarction with a deconvolution strategy of transcriptomic data

Myocardial infarction (MI) is a major threat worldwide and despite progress in clinical care, its morbi-mortality is still high. In the last decades, therapeutic strategies attempted to preserve cardiomyocytes from death, but clinical trials failed to translate them to the clinic. MI is associated with a strong inflammatory reaction and induction of immune cells what has recently attracted our attention both in academic and clinical research. By mean of the best tradeoff between patients number and cost of mRNAseq technologies, we seek to identify immune cell transcriptomes that correlate with clinical parameters in STEMI patients such infarct size or secondary outcome. The recent progress in the single cell RNA sequencing makes it possible to study the RNA content at single cell level. However, the higher cost of scRNAseq than this of bulk mRNAseq constrains the number of samples we could analyzed and consequently the statistical power. We decided to combine the benefits of those two methods by deconvoluting the bulk data with the single cell ones. The development of the pipeline of analysis will rely on the sequencing of 20 PBMC samples with the two techniques. Then this pipeline will be applied to the bulk RNA dataset of 60 patients. The experimental design was adapted to the 10X genomics single cell RNA sequencing and 3 parameters were set: the number of samples, the number of sequenced cells and the depth of the sequencing. To define values of these parameters, we performed statistical simulation (SCOPIT) to determine the minimal proportion of a cell phenotype that we could identify in the whole PBMC population. Then, we review the literature to choose a deconvolution algorithm. We found more than 70 algorithms, the first one was developed in 2003 and it has been followed by a bulk of new tools in the recent years. However, these algorithms have different outputs and underlying mathematical models. Among these, we have chosen BLADE, the last publish tools, for our study. This study explore transcriptomics fingerprinting of STEMI patients samples and could lead to identification of candidate biomarkers of infarct size or secondary outcomes.

Real time imaging of the excitation volume of a multiphoton microscope

Knowledge of the excitation profile in a confocal or multiphoton microscope can improve the image resolution, e.g. by using deconvolution, pixel reassignment or adaptive optics strategies. Here we demonstrate a method by which the scanning beam can be used to place a stationary, virtual ‘guide star’ at any chosen location in the sample, during imaging. This can then be used to directly image the excitation profile. The major advantage of our easy-to-install method, compared to competing methods, is that it can work for non-descanned multiphoton microscopy, the method of choice for deep tissue or ultraviolet imaging. Our experimental results reproduce diffraction theory based calculations in a minimally-scattering sample, and provide detailed information about the aberrated excitation profile in a highly scattering sample.

FC014: Target Deconvolution from Phenotypic Screens—Identification of Ataxia Telangiectasia Mutated as a Potential Therapeutic Target for Podocyte Protection

Abstract BACKGROUND AND AIMS As part of the renal filtration barrier, podocytes are critical to the function of kidney glomeruli. Their loss is one of the hallmarks of chronic kidney disease, including diabetic kidney disease (DKD). Indeed, many drug discovery efforts focus on preventing podocyte apoptosis triggered by diabetic stressors to delay kidney disease progression. In order to discover and characterize new therapeutic agents, phenotypic screens are a powerful tool to directly screen for the desired cellular outcome. These need to be complemented by target-centric assays to support the chemical optimization of compounds. Generally, the deconvolution and confirmation of molecular targets are considered challenging, but necessary in the follow-up of phenotype-based drug discovery programmes to allow progression towards the clinic. We have combined novel technologies like high-throughput next-generation sequencing (NGS) and Cell Painting [1] to develop an unbiased approach for deconvolution of targets from phenotypic screens. As an outcome, we identified a small molecule compound that can protect conditionally immortalized human podocytes (CIHP) from apoptosis in vitro. In a second step, we were able to identify the target responsible for this effect. METHOD Phenotypic screen: CIHP [2] were treated with high glucose and palmitate, a common DKD injury model, as the main driver for apoptosis as evaluated by caspase 3/7 activation [3]. Potentially protective agents were screened against apoptosis from a library of 50 000 small molecules. We selected a hit compound (CpdX) for subsequent target deconvolution to identify the target causing the protective effect. Target deconvolution and compound profiling: We compared the molecular signatures triggered by CpdX treatment to those of a reference panel of known, bio-annotated compounds (#L3500; Selleckchem). Dimensional reduction based on UMAP and PCA applied to transcriptomic data (NGS) as well as Cell Painting fingerprints were used to identify known compounds exhibiting a similar transcriptomic and morphological profile as the CpdX. We hypothesized that the molecular targets of these compounds also constitute likely targets for the new CpdX. RESULTS We developed a screening cascade (Figure 1A) to identify novel targets for kidney disease. A first-pass phenotypic podocyte apoptosis screen delivered a potent compound, able to rescue CIHP from a diabetic challenge in vitro (Figure 1B, C). Integrated analysis and alignment of cellular changes in transcriptomic profile and morphology following CpdX or reference compound treatment enabled us to identify the serine/threonine-protein kinase Ataxia Telangiectasia Mutated (ATM) as one of the putative targets of the orphan compound. Both KU-60019, a known inhibitor of ATM [4] (Figure 1D) and CpdX showed remarkably similar effects in NGS and Cell Painting (Figure 2A, B) and were also equally protecting the cells from apoptosis in vitro (Figure 1B). From this data, we inferred that ATM is a likely effector of CpdX treatment. CONCLUSION The initial lack of specific target candidates following primary phenotypic screens is a widespread problem in drug discovery. In the absence of a target deconvolution strategy, many potential candidates cannot be progressed easily to optimization. Here, we demonstrated that a combination of state-of-the-art technologies used to benchmark hit compounds from phenotypic screens versus known bio-annotated compound libraries represent a viable approach to perform target deconvolution of hits from phenotypic screening approaches. In this work, a phenotypic screen and subsequent deconvolution identified a novel target in combination with a novel compound, making a suitable starting point for progression into a potential kidney therapeutic agent.

Parametric estimation of hidden Markov models by least squares type estimation and deconvolution

This paper develops a simple and computationally efficient parametric approach to the estimation of general hidden Markov models (HMMs). For non-Gaussian HMMs, the computation of the maximum likelihood estimator (MLE) involves a high-dimensional integral that has no analytical solution and can be difficult to approach accurately. We develop a new alternative method based on the theory of estimating functions and a deconvolution strategy. Our procedure requires the same assumptions as the MLE and deconvolution estimators. We provide theoretical guarantees about the performance of the resulting estimator; its consistency and asymptotic normality are established. This leads to the construction of confidence intervals. Monte Carlo experiments are investigated and compared with the MLE. Finally, we illustrate our approach using real data for ex-ante interest rate forecasts.

Deconvolution Tactics and Normalization in Renal Spatial Transcriptomics.

The kidney is composed of heterogeneous groups of epithelial, endothelial, immune, and stromal cells, all in close anatomic proximity. Spatial transcriptomic technologies allow the interrogation of in situ expression signatures in health and disease, overlaid upon a histologic image. However, some spatial gene expression platforms have not yet reached single-cell resolution. As such, deconvolution of spatial transcriptomic spots is important to understand the proportion of cell signature arising from these varied cell types in each spot. This article reviews the various deconvolution strategies discussed in the 2021 Indiana O’Brien Center for Microscopy workshop. The unique features of Seurat transfer score methodology, SPOTlight, Robust Cell Type Decomposition, and BayesSpace are reviewed. The application of normalization and batch effect correction across spatial transcriptomic samples is also discussed.

Matrix-Augmented Pooling Strategy for High-Throughput Target Deconvolution Reveals Cell Type Specific Off-Targets and Responses

Matrix-Augmented Pooling Strategy for High-Throughput Target Deconvolution Reveals Cell Type Specific Off-Targets and Responses

Dissection of Targeting Molecular Mechanisms of Aristolochic Acid-induced Nephrotoxicity via a Combined Deconvolution Strategy of Chemoproteomics and Metabolomics.

Aristolochic acid (AA), mainly derived from herbal Aristolochia and Asarum plants, was listed as a human carcinogen class I in 2002. Aristolochic acid nephropathy (AAN) is a rapidly progressive tubulointerstitial nephritis and urothelial cancer caused by AA. However, the targeting molecular mechanisms of AAs-induced nephrotoxicity are largely unclear. This study aims to dissect targeting molecular mechanisms of AA-induced nephrotoxicity. Activity-based protein profiling (ABPP) in combination with cellular thermal shift assay (CETSA) was performed to identify the AAs binding target proteins. Our data indicated that several key enzymes in the metabolic process and mitochondrial respiration including IDH2 and MDH2 (Krebs cycle), PKM and LDH (aerobic respiration), FASN (fatty acid beta-oxidation), HK2 (glucose metabolism), and ATP synthase were identified as directly binding targets of AAs. Metabolomics and oxygen consumption rate (OCR) experiments further confirmed that AAs targeting proteins disrupted metabolic biosynthesis processes and impaired mitochondrial functions. Ultimately, AAs induced renal cells apoptosis by disturbing various biological processes. Cumulatively, AAs may directly bind to key proteins involved in the metabolic process and mitochondrial homeostasis, and finally induce aristolochic acid nephropathy. Our findings provide novel insight into underlying mechanisms of AAs-induced kidney toxicity, which may help to develop therapeutic strategies for AAN.

Contrast estimation for noisy observations of diffusion processes via closed-form density expansions

When a continuous-time diffusion is observed only at discrete times with measurement noise, in most cases the transition density is not known and the likelihood is in the form of a high-dimensional integral that does not have a closed-form solution and is difficult to compute accurately. Using Hermite expansions and deconvolution strategy, we provide a general explicit sequence of closed-form contrast for noisy and discretely observed diffusion processes. This work allows the estimation of many diffusion processes. We show that the approximation is very accurate and prove that minimizing the sequence results in a consistent and asymptotically normal estimator. Monte Carlo evidence for the Ornstein–Uhlenbeck process reveals that this method works well and outperforms the Euler expansion of the transition density in situations relevant for financial models.

Depth resolution in multifocus laser speckle contrast imaging.

Laser speckle contrast imaging (LSCI) can be used to evaluate blood flow based on spatial or temporal speckle statistics, but its accuracy is undermined by out-of-focus image blur. In this Letter, we show how the fraction of dynamic versus static light scattering is dependent on focus, and describe a deconvolution strategy to correct for out-of-focus blur. With the aid of a z-splitter, which enables instantaneous multifocus imaging, we demonstrate depth-resolved LSCI that can robustly extract multi-plane structural and flow-speed information simultaneously. This method is applied to in vivo imaging of blood vessels in a mouse cortex and provides improved estimates of blood flow speed throughout a depth range of 300µm.

Strategies for cellular deconvolution in human brain RNA sequencing data

Background: Statistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. However, no study has been undertaken to assess the extent to which expression-based and DNAm-based cell type composition estimates agree. Results: Using estimated neuronal fractions from DNAm data, from the same brain region (i.e., matched) as our bulk RNA-Seq dataset, as proxies for the true unobserved cell-type fractions (i.e., as the gold standard), we assessed the accuracy (RMSE) and concordance (R2) of four reference-based deconvolution algorithms: Houseman, CIBERSORT, non-negative least squares (NNLS)/MIND, and MuSiC. We did this for two cell-type populations - neurons and non-neurons/glia - using matched single nuclei RNA-Seq and mismatched single cell RNA-Seq reference datasets. With the mismatched single cell RNA-Seq reference dataset, Houseman, MuSiC, and NNLS produced concordant (high correlation; Houseman R2 = 0.51, 95% CI [0.39, 0.65]; MuSiC R2 = 0.56, 95% CI [0.43, 0.69]; NNLS R2 = 0.54, 95% CI [0.32, 0.68]) but biased (high RMSE, >0.35) neuronal fraction estimates. CIBERSORT produced more discordant (moderate correlation; R2 = 0.25, 95% CI [0.15, 0.38]) neuronal fraction estimates, but with less bias (low RSME, 0.09). Using the matched single nuclei RNA-Seq reference dataset did not eliminate bias (MuSiC RMSE = 0.17). Conclusions: Our results together suggest that many existing RNA deconvolution algorithms estimate the RNA composition of homogenate tissue, e.g. the amount of RNA attributable to each cell type, and not the cellular composition, which relates to the underlying fraction of cells.

Leveraging Preconcentration and Partial Separations with Silicon Nanowire Arrays to Improve the Detection of Low Volatility Trace Explosive Vapors

Trace detection of explosive vapors in real world environments is hindered by strict sensitivity and selectivity requirements that need to be met in order for these techniques to be effective. The physical properties of most explosive molecules limit environmentally relevant vapor concentrations to the part per billion (ppbv) – part per trillion (pptv) range due to their saturated vapor pressures under real world conditions. Additionally, trace explosive vapor detectors must be able to discriminate against a number of potential chemical and biological interferents present in complex real world environments. In this work, we present silicon nanowire arrays (SiNW) as a novel tunable material aimed at providing trace explosive vapor detectors with the sensitivity and selectivity necessary for operational use.Dense, highly ordered silicon nanowires are fabricated through a nanosphere lithography process and topped with a porous gold electrode resulting in a high surface area adsorptive substrate intrinsically capable of rapid, precise temperature control via Joule heating. Explosive vapors are selectively preconcentrated onto the SiNW array by exploiting their affinities for the surface (SiO2 or otherwise functionalized) of the nanowires. Chromatographic like partial separations of preconcentrated material on the array can be achieved through careful selection of desorption parameters. Using a multichannel detector, such as an IMS or MS, we demonstrate the preconcentration of a number of explosive vapors including 2,6-DNT, TNT, and RDX, while simultaneously exploring the effects of desorption flow rates and temperatures on the partial separation of 2,6-DNT, 2,4-DNT, TNT, and RDX vapors. In addition, we provide a strategy for chemometric deconvolution of array-based partial separations based upon evolving factor analysis and multivariate curve resolution by alternating least squares. This approach enabled qualitative and quantitative analysis of individual components without target analyte libraries or complete chromatographic separation.

The Chemistry of Nonclassical Taxane Diterpene.

The taxane diterpenes are a pharmaceutically vital family of natural products, consisting of more than 550 congeners. All taxane diterpenes are isolated from slow growing evergreen shrubs (genus Taxus) commonly known as "yews" and have a history of over 50 years as potent anticancer compounds. The most prominent congener, taxol (paclitaxel = PTX), has been used in clinics for more than 25 years and is one of the top-selling anticancer drugs worldwide, with annual sales reaching 1.5 billion USD in 1999. Within the taxane diterpene family 11 different scaffolds originating from rearrangements, fragmentations, or transannular C-C bond formations of the "classical taxane core" are known. Among them, five different scaffolds alone belong to the so-called complex or cyclotaxane subfamily, their signature structural feature bearing different types and numbers of transannular C-C bonds across the classical taxane backbone. For synthetic chemists, these five scaffolds represent by far the most challenging of all and have thus evaded total synthesis as well as detailed pharmaceutical evaluation-the latter due to extremely poor sourcing from natural producers. The cousinship of complex taxanes to taxol renders them potentially interesting compounds for drug research in the fight against cancer.This Account specifically summarizes the work on nonclassical taxanes from a biosynthetic, as well as a synthetic, point and provides a synthetic perspective on complex taxanes. Special attention is given to the biosynthetic relationship of complex taxanes and their biological emergence from classical taxanes. The transannular C-C bond forming events in the biosynthesis leading to the five individual scaffolds within this subfamily are structured on the basis of the exact type and number of these specific C-C bond formations. Since functionalization of the classical taxane core in the "oxidase phase" of the biosynthesis precedes the formation of complex taxanes, and is in part prerequisite for these transannular cyclization events, a detailed discussion of these oxidations of the classical taxane backbone is provided. Synthetic efforts toward nonclassical taxanes are scarce in literature and are thus presented in a comprehensive manner for abeotaxanes and complex taxanes. The last part of this Account deals with a synthetic perspective on the synthesis of complex taxanes (cyclotaxanes) and how these most intricate scaffolds can potentially be obtained via a deconvolution strategy. This discussion involves in part unpublished results by our group and is based upon synthetic studies in the literature. The deconvolution strategy we advocate aims for selective fragmentations of the signature transannular C-C bonds of the most intricate scaffold represented by the natural product canataxpropellane, which has recently been synthesized by our group. This strategy represents the converse process of the biosynthesis of complex taxanes (e.g., transannular cyclizations) and is enabled and feasible due to our approach to the canataxpropellane scaffold. We show that, by following this deconvolution strategy, all five scaffolds of complex taxanes can thereby be accessed.

Masked Multiplexed Separations to Enhance Duty Cycle for Structures for Lossless Ion Manipulations.

The experimental paradigm of one ion packet release per spectrum severely hinders throughput in broadband ion mobility spectrometry (IMS) systems (e.g., drift tube and traveling wave systems). Ion trapping marginally mitigates this problem, but the duty cycle deficit is amplified when moving to high resolution, long pathlength systems. As a consequence, new multiplexing strategies that maximize throughput while preserving peak fidelity are essential for high-resolution IMS separations [e.g., structures for lossless ion manipulations (SLIMs) and multi-pass technologies]. Currently, broadly applicable deconvolution strategies for Hadamard-based ion multiplexing are limited to a narrow range of modulation sequences and do not fully maximize the ion signal generated during separation across an extended path length. Compared to prior Hadamard deconvolution errors that rely upon peak picking or discrete error classification, the masked deconvolution matrix technique exploits the knowledge that Hadamard transform artifacts are reflected about the central, primary signal [i.e., the true arrival time distribution (ATD)]. By randomly inducing mathematical artifacts, it is possible to identify spectral artifacts simply by their high degree of variability relative to the core ATD. It is important to note that the deweighting approach using the masked deconvolution matrix does not make any assumptions about the underlying transform and is applicable to any multiplexing strategy employing binary sequences. In addition to demonstrating a 100-fold increase in the total number of ions detected, the effective deconvolution of data from 5, 6, 7, and 8-bit pseudo-random sequences expands the utility and efficiency of the SLIM platform.

Improving Spectral Validation Rates in Hydrogen-Deuterium Exchange Data Analysis.

The data analysis practices associated with hydrogen-deuterium exchange mass spectrometry (HX-MS) lag far behind that of most other MS-based protein analysis tools. A reliance on external tools from other fields and a persistent need for manual data validation restrict this powerful technology to the expert user. Here, we provide an extensive upgrade to the HX data analysis suite available in the Mass Spec Studio in the form of two new apps (HX-PIPE and HX-DEAL), completing a workflow that provides an HX-tailored peptide identification capability, accelerated validation routines, automated spectral deconvolution strategies, and a rich set of exportable graphics and statistical reports. With these new tools, we demonstrate that the peptide identifications obtained from undeuterated samples generated at the start of a project contain information that helps predict and control the extent of manual validation required. We also uncover a large fraction of HX-usable peptides that remains unidentified in most experiments. We show that automated spectral deconvolution routines can identify exchange regimes in a project-wide manner, although they remain difficult to accurately assign in all scenarios. Taken together, these new tools provide a robust and complete solution suitable for the analysis of high-complexity HX-MS data.

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems.

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

High-contrast multifocus microscopy with a single camera and z-splitter prism.

Optical microscopy has been an indispensable tool for studying complex biological systems, but is often hampered by problems of speed and complexity when performing 3D volumetric imaging. Here, we present a multifocus imaging strategy based on the use of a simple z-splitter prism that can be assembled from off-the-shelf components. Our technique enables a widefield image stack to be distributed onto a single camera and recorded simultaneously. We exploit the volumetric nature of our image acquisition by further introducing a novel extended-volume 3D deconvolution strategy to suppress far-out-of-focus fluorescence background to significantly improve the contrast of our recorded images, conferring to our system a capacity for quasi-optical sectioning. By swapping in different z-splitter configurations, we can prioritize high speed or large 3D field-of-view imaging depending on the application of interest. Moreover, our system can be readily applied to a variety of imaging modalities in addition to fluorescence, such as phase-contrast and darkfield imaging. Because of its simplicity, versatility, and performance, we believe our system will be a useful tool for general biological or biomedical imaging applications.

Deep learning for a space-variant deconvolution in galaxy surveys

The deconvolution of large survey images with millions of galaxies requires developing a new generation of methods that can take a space-variant point spread function into account. These methods have also to be accurate and fast. We investigate how deep learning might be used to perform this task. We employed a U-net deep neural network architecture to learn parameters that were adapted for galaxy image processing in a supervised setting and studied two deconvolution strategies. The first approach is a post-processing of a mere Tikhonov deconvolution with closed-form solution, and the second approach is an iterative deconvolution framework based on the alternating direction method of multipliers (ADMM). Our numerical results based on GREAT3 simulations with realistic galaxy images and point spread functions show that our two approaches outperform standard techniques that are based on convex optimization, whether assessed in galaxy image reconstruction or shape recovery. The approach based on a Tikhonov deconvolution leads to the most accurate results, except for ellipticity errors at high signal-to-noise ratio. The ADMM approach performs slightly better in this case. Considering that the Tikhonov approach is also more computation-time efficient in processing a large number of galaxies, we recommend this approach in this scenario.

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.

Comparative analyses of error handling strategies for next-generation sequencing in precision medicine

Next-generation sequencing (NGS) offers the opportunity to sequence millions and billions of DNA sequences in a short period, leading to novel applications in personalized medicine, such as cancer diagnostics or antiviral therapy. Nevertheless, sequencing technologies have different error rates, which occur during the sequencing process. If the NGS data is used for diagnostics, these sequences with errors are typically neglected or a worst-case scenario is assumed. In the current study, we focused on the impact of ambiguous bases on therapy recommendations for Human Immunodeficiency Virus 1 (HIV-1) patients. Concretely, we analyzed the treatment recommendation with entry blockers based on prediction models for co-receptor tropism. We compared three different error handling strategies that have been used in the literature, namely (i) neglection, (ii) worst-case assumption, and (iii) deconvolution with a majority vote. We could show that for two or more ambiguous positions per sequence a reliable prediction is generally no longer possible. Moreover, also the position of ambiguity plays a crucial role. Thus, we analyzed the error probability distributions of existing sequencing technologies, e.g., Illumina MiSeq or PacBio, with respect to the aforementioned error handling strategies and it turned out that neglection outperforms the other strategies in the case where no systematic errors are present. In other cases, the deconvolution strategy with the majority vote should be preferred.