Off-target Identification Research Articles

In silico off-target profiling for enhanced drug safety assessment

Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.

Massively parallel CRISPR off-target detection enables rapid off-target prediction model building

CRISPR (clustered regularly interspaced short palindromic repeats) genome editing holds tremendous potential in clinical translation. However, the off-target effect has always been a major concern. Here, we have developed a novel sensitive and specific off-target detection method, AID-seq (adaptor-mediated off-target identification by sequencing), that can comprehensively and faithfully detect the low-frequency off targets generated by different CRISPR nucleases (including Cas9 and Cas12a). Based on AID-seq, we developed a pooled strategy to simultaneously identify the on/off targets of multiple gRNAs, as well as using mixed human and human papillomavirus (HPV) genomes to screen the most efficient and safe targets from 416 HPV gRNA candidates for antiviral therapy. Moreover, we used the pooled strategy with 2,069 single-guide RNAs (sgRNAs) at a pool size of about 500 to profile the properties of our newly discovered CRISPR, FrCas9. Importantly, we successfully built an off-target detection model using these off-target data via the CRISPR-Net deep learning method (area under the receiver operating characteristic curve [AUROC]= 0.97, area under the precision recall curve [AUPRC]= 0.29). To our knowledge, AID-seq is the most sensitive and specific invitro off-target detection method to date. And the pooled AID-seq strategy can be used as a rapid and high-throughput platform to select the best sgRNAs and characterize the properties of new CRISPRs. This work was supported by The National Natural Science Foundation of China (grant nos. 32171465 and 82102392), the General Program of Natural Science Foundation of Guangdong Province of China (grant no. 2021A1515012438), Guangdong Basic and Applied Basic Research Foundation (grant no. 2020A1515110170), and the National Ten Thousand Plan-Young Top Talents of China (grant no. 80000-41180002).

Deep learning identification of cardiac transporters as targets for approved drugs.

We re-tasked a novel ligand-centered deep learning drug discovery method to identify molecular targets for approved drugs in the heart. This approach uses information about small-molecule effectors to map and probe the pharmacological space of functionally relevant targets in the heart. Here we studied the calcium pump (Sarcoplasmic reticulum Ca2+-ATPase, SERCA) as a proof-of-principle target to test our method. We chose SERCA because it plays a major role in the excitation-contraction-relaxation cycle in normal and pathological muscle, and it represents a major pharmacological target in the heart. We applied this method to demonstrate that SERCA is a pharmacological target for statins, a group of FDA-approved HMGCoA inhibitors used as lipid-lowering medications. We used in situ enzymatic assays and atomistic simulations to demonstrate that that these approved drugs are SERCA inhibitors at micromolar concentrations, inhibiting the pump by binding to two different effector sites. These proof-of-concept studies support the applicability of our approach for off-target identification and drug repurposing, ultimately minimizing the translational gap in drug development targeting the heart.

PEAC-seq adopts Prime Editor to detect CRISPR off-target and DNA translocation

CRISPR technology holds significant promise for biological studies and gene therapies because of its high flexibility and efficiency when applied in mammalian cells. But endonuclease (e.g., Cas9) potentially generates undesired edits; thus, there is an urgent need to comprehensively identify off-target sites so that the genotoxicities can be accurately assessed. To date, it is still challenging to streamline the entire process to specifically label and efficiently enrich the cleavage sites from unknown genomic locations. Here we develop PEAC-seq, in which we adopt the Prime Editor to insert a sequence-optimized tag to the editing sites and enrich the tagged regions with site-specific primers for high throughput sequencing. Moreover, we demonstrate that PEAC-seq could identify DNA translocations, which are more genotoxic but usually overlooked by other off-target detection methods. As PEAC-seq does not rely on exogenous oligodeoxynucleotides to label the editing site, we also conduct in vivo off-target identification as proof of concept. In summary, PEAC-seq provides a comprehensive and streamlined strategy to identify CRISPR off-targeting sites in vitro and in vivo, as well as DNA translocation events. This technique further diversified the toolkit to evaluate the genotoxicity of CRISPR applications in research and clinics.

Fast Local Alignment of Protein Pockets (FLAPP): A System-Compiled Program for Large-Scale Binding Site Alignment

Protein function is a direct consequence of its sequence, structure, and the arrangement at the binding site. Bioinformatics using sequence analysis is typically used to gain a first insight into protein function. Protein structures, on the other hand, provide a higher resolution platform into understanding functions. As the protein structural information is increasingly becoming available through experimental structure determination and through advances in computational methods for structure prediction, the opportunity to utilize these data is also increasing. Structural analysis of small molecule ligand binding sites in particular provides a direct and more accurate window to infer protein function. However, it remains a poorly utilized resource due to the huge computational cost of existing methods that make large-scale structural comparisons of binding sites prohibitive. Here, we present an algorithm called FLAPP that produces very rapid atomic level alignments. By combining clique matching in graphs and the power of modern CPU architectures, FLAPP aligns a typical pair of binding sites at ∼12.5 ms using a single CPU core, ∼1 ms using 12 cores on a standard desktop machine, and performs a PDB-wide scan in 1-2 min. We perform rigorous validation of the algorithm at multiple levels of complexity and show that FLAPP provides accurate alignments. We also present a case study involving vitamin B12 binding sites to showcase the usefulness of FLAPP for performing an exhaustive alignment-based PDB-wide scan. We expect that this tool will be invaluable to the scientific community to quickly align millions of site pairs on a normal desktop machine to gain insights into protein function and drug discovery for drug target and off-target identification and polypharmacology.

Identification and Validation of CRISPR/Cas9 Off-Target Activity in Hematopoietic Stem and Progenitor Cells.

Targeted genome editing in hematopoietic stem and progenitor cells (HSPCs) using CRISPR/Cas9 can potentially provide a permanent cure for hematologic diseases. However, the utility of CRISPR/Cas9 systems for therapeutic genome editing can be compromised by their off-target effects. In this chapter, we outline the procedures for CRISPR/Cas9 off-target identification and validation in HSPCs. This method is broadly applicable to diverse CRISPR/Cas9 systems and cell types. Using this protocol, researchers can perform computational prediction and experimental identification of potential off-target sites followed by off-target activity quantification by next-generation sequencing.

Genome-wide detection and analysis of CRISPR-Cas off-targets.

The clustered, regularly interspersed, short palindromic repeats (CRISPR) technology is revolutionizing biological studies and holds tremendous promise for treating human diseases. However, a significant limitation of this technology is that modifications can occur on off-target sites lacking perfect complementarity to the single guide RNA (sgRNA) or canonical protospacer-adjacent motif (PAM) sequence. Several in vivo and in vitro genome-wide off-target profiling approaches have been developed to inform on the fidelity of gene editing. Of these, GUIDE-seq has become one of the most widely adopted and reproducible methods. To allow users to easily analyze GUIDE-seq data generated on any sequencing platform, we developed an open-source pipeline, GS-Preprocess, that takes standard base-call output in bcl format and generate all required input data for off-target identification using bioconductor package GUIDEseq for off-target identification. Furthermore, we created a Docker image with GS-Proprocess, GUIDE-seq, and all its R and system dependencies already installed. The bundled pipeline will empower end users to streamline the analysis of GUIDE-seq data and motivate their use of higher throughput sequencing with increased multiplexing for GUIDE-seq experiments.

END-seq: An Unbiased, High-Resolution, and Genome-Wide Approach to Map DNA Double-Strand Breaks and Resection in Human Cells.

DNA double-strand breaks (DSBs) represent the most toxic form of DNA damage and can arise in either physiological or pathological conditions. If left unrepaired, these DSBs can lead to genome instability which serves as a major driver to tumorigenesis and other pathologies. Consequently, localizing DSBs and understanding the dynamics of break formation and the repair process are of great interest for dissecting underlying mechanisms and in the development of targeted therapies. Here, we describe END-seq, a highly sensitive next-generation sequencing technique for quantitatively mapping DNA double-strand breaks (DSB) at nucleotide resolution across the genome in an unbiased manner. END-seq is based on the direct ligation of a sequencing adapter to the ends of DSBs and provides information about DNA processing (end resection) at DSBs, a critical determinant in the selection of repair pathways. The absence of cell fixation and the use of agarose for embedding cells and exonucleases for blunting the ends of DSBs are key advances that contribute to the technique's increased sensitivity and robustness over previously established methods. Overall, END-seq has provided a major technical advance for mapping DSBs and has also helped inform the biology of complex biological processes including genome organization, replication fork collapse and chromosome fragility, off-target identification of RAG recombinase and gene-editing nucleases, and DNA end resection at sites of DSBs.

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets.

Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.

Unbiased detection of CRISPR off-targets in vivo using DISCOVER-Seq.

CRISPR-Cas genome editing induces targeted DNA damage but can also affect off-target sites. Current off-target discovery methods work using purified DNA or specific cellular models but are incapable of direct detection in vivo. We developed DISCOVER-Seq (discovery of in situ Cas off-targets and verification by sequencing), a universally applicable approach for unbiased off-target identification that leverages the recruitment of DNA repair factors in cells and organisms. Tracking the precise recruitment of MRE11 uncovers the molecular nature of Cas activity in cells with single-base resolution. DISCOVER-Seq works with multiple guide RNA formats and types of Cas enzymes, allowing characterization of new editing tools. Off-targets can be identified in cell lines and patient-derived induced pluripotent stem cells and during adenoviral editing of mice, paving the way for in situ off-target discovery within individual patient genotypes during therapeutic genome editing.

Chemoproteomic approaches for off-target hazard identification

Chemoproteomic approaches for off-target hazard identification

PatchSearch: A Fast Computational Method for Off-Target Detection.

Many therapeutic molecules are known to bind several proteins, which can be different from the initially targeted one. Such unexpected interactions with proteins called off-targets can lead to adverse effects. Potential off-target identification is important to predict to avoid drug side effects or to discover new targets for existing drugs. We propose a new program named PatchSearch that implements local nonsequential searching for similar binding sites on protein surfaces with a controlled amount of flexibility. It is based on detection of quasi-cliques in product graphs representing all the possible matchings between two compared structures. This method has been benchmarked on a large diversity of ligands and on five data sets ranging from 12 to more than 7000 protein structures. The experiments conducted in this study show that the PatchSearch method could be useful in the early identification of off-targets. The program and the benchmarks presented in this paper are available as an R package at https://github.com/MTiPatchSearch .

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.

Mitigation of opioid off-target effects and identification of structural drivers of opioid receptor engagement for BACE-1 small molecule inhibitors

Application of safety lead optimization screening strategies during the early stage of drug discovery led to the identification of a series of CNS-active small molecule inhibitors with opioid off-target effects, as evidenced by potent agonistic activity in functional cell-based assays for mu (MOP), kappa (KOP) and delta (DOP) opioid receptors. The translation of these effects was confirmed in vivo with the following observations: hypoactivity and decreased fecal production in rats (characteristic of MOP agonism); increased urine production in rats (characteristic of KOP agonism); and decreased intestinal transit time in mice, which was partially blocked by the MOP antagonist naloxone, demonstrating that the in vivo effects were specific for MOP. Based on the confirmation of in vitro–in vivo translatability, an in vitro screening strategy was implemented that resulted in the identification of an optimized backup molecule, devoid of in vivo off-target opioid effects. In addition, in silico modeling by docking of the various molecules to the opioid receptors allowed the identification of the structural drivers of these off-target effects, which can be applied to future chemical-design criteria.Thus, implementation of the safety lead optimization strategy described in this article demonstrates the utility and impact of such approaches on risk mitigation and identification of lead small molecules with improved safety profiles.

An integrated workflow for proteome-wide off-target identification and polypharmacology drug design

Polypharmacology, which focuses on designing drugs to target multiple receptors, has emerged as a new paradigm in drug discovery. To rationally design multi-target drugs, it is fundamental to understand protein-ligand interactions on a proteome scale. We have developed a Proteome-wide Off-target Pipeline (POP) that integrates ligand binding site analysis, protein-ligand docking, the statistical analysis of docking scores, and electrostatic potential calculations. The utility of POP is demonstrated by a case study, in which the molecular mechanism of anti-cancer effect of Nelfinavir is hypothesized. By combining structural proteome-wide off-target identification and systems biology, it is possible for us to correlate drug perturbations with clinical outcomes.

Live cell off-target identification of lapatinib using ligand-directed tosyl chemistry.

We demonstrate that ligand-directed tosyl (LDT) chemistry is applicable to off-target identification in live cells. Lapatinib (Lap)-based LDT reagents not only labeled a receptor tyrosine kinase, HER2, target protein, but also the protein disulfide isomerase (PDI) that should be an off-target protein for Lap.

High-throughput virtual screening of phloroglucinol derivatives against HIV-reverse transcriptase

Therapeutic agents exert their pharmacological and adverse effects by interacting with molecular targets. Even if drug molecules are intended to interact with specific targets in a desirable manner, they are often found to bind to other targets. Nowadays, research is focused on a single molecule that simultaneously targets multiple disease-causing proteins. Therefore, off-target identification of existing chemical space can be a valuable tool to find safe and effective multi-targeted therapeutic agents at a significantly lower cost to patients. Phloroglucinols represent a class of compounds, which exhibits a diverse range of biological activities, such as anti-HIV, antimalarial, antileishmanial, antituberculosis, antibacterial, and antifungal. The aim of the current study is to explore untapped potential of various series of phloroglucinols against HIV reverse transcriptase (HIV-RTase). A series of filtering parameters was applied in search of viable phloroglucinol derivatives against HIV-RTase. A library of phloroglucinol derivatives was screened based on their toxicity potential followed by predicted ADME parameters. The filtered compounds were then carried forward for docking analysis against HIV-RTase. A set of 37 phloroglucinol compounds with diverse pharmacological profile was found to have good binding affinity towards HIV-RTase. These molecules formed hydrogen bonds with Lys101, Lys103, Val106, and Leu234 residues and π–π stacking interaction with Tyr318 residue of the protein. Here, we propose potential phloroglucinol derivatives with different known biological activity that can be repurposed as potential hits against HIV.

Computational structural analysis of proteins of Mycobacterium tuberculosis and a resource for identifying off-targets

Advancement in technology has helped to solve structures of several proteins including M. tuberculosis (MTB) proteins. Identifying similarity between protein structures could not only yield valuable clues to their function, but can also be employed for motif finding, protein docking and off-target identification. The current study has undertaken analysis of structures of all MTB gene products with available structures was analyzed. Majority of the MTB proteins belonged to the α/β class. 23 different protein folds are used in the MTB protein structures. Of these, the TIM barrel fold was found to be highly conserved even at very low sequence identity. We identified 21 paralogs and 27 analogs of MTB based on domains and EC classification. Our analysis revealed that many of the current drug targets share structural similarity with other proteins within the MTB genome, which could probably be off-targets. Results of this analysis have been made available in the Mycobacterium tuberculosis Structural Database (http://bmi.icmr.org.in/mtbsd/MtbSD.php/search.php) which is a useful resource for current and novel drug targets of MTB.

Chemical-protein interactome and its application in off-target identification

Drugs exert their therapeutic and adverse effects by interacting with molecular targets. Although designed to interact with specific targets in a desirable manner, drug molecules often bind to unexpected proteins (off-targets). By activating or inhibiting off-targets and the associated biological processes and pathways, the resulting chemical-protein interactions can influence drug reaction directly or indirectly. Exploring the relationship between drug and off-targets and the downstream drug reaction can help understand the polypharmacology of the drug, hence significantly advance the drug repositioning pipeline and the application of personalized medicine in understanding and preventing adverse drug reaction. This review summarizes works on predicting off-targets via chemical-protein interactome (CPI), an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc. Effective recall on previous knowledge, de novo prediction and subsequent experimental validation conferred us strong confidence in these methods. Such studies present prospect of large scale in silico methodologies for off-target discovery with low cost and high efficiency.

Activity-Based Proteome Profiling of Potential Cellular Targets of Orlistat − An FDA-Approved Drug with Anti-Tumor Activities

Orlistat, or tetrahydrolipstatin (THL), is an FDA-approved antiobesity drug with potential antitumor activities. Cellular off-targets and potential side effects of Orlistat in cancer therapies, however, have not been extensively explored thus far. In this study, we report the total of synthesis of THL-like protein-reactive probes, in which extremely conservative modifications (i.e., an alkyne handle) were introduced in the parental THL structure to maintain the native biological properties of Orlistat, while providing the necessary functionality for target identification via the bio-orthogonal click chemistry. With these natural productlike, cell-permeable probes, we were able to demonstrate, for the first time, this chemical proteomic approach is suitable for the identification of previously unknown cellular targets of Orlistat. In addition to the expected fatty acid synthase (FAS), we identified a total of eight new targets, some of which were further validated by experiments including Western blotting, recombinant protein expression, and site-directed mutagenesis. Our findings have important implications in the consideration of Orlistat as a potential anticancer drug at its early stages of development for cancer therapy. Our strategy should be broadly useful for off-target identification against quite a number of existing drugs and/or candidates, which are also covalent modifiers of their biological targets.