Drug Discovery Strategies Research Articles

Discovery of Novel PDEδ Autophagic Degraders: A Case Study of Autophagy-Tethering Compound (ATTEC).

The autophagy-tethering compound (ATTEC) technology has emerged as a promising strategy for targeted protein degradation (TPD). Here, we report the discovery of the first generation of PDEδ autophagic degraders using an ATTEC approach. The most promising compound 12c exhibited potent PDEδ binding affinity and efficiently induced PDEδ degradation in a concentration-dependent manner. Mechanistic studies confirmed that compound 12c reduced the PDEδ protein level through lysosome-mediated autophagy without affecting the PDEδ mRNA expression. Importantly, compound 12c was much more effective in suppressing the growth in KRAS mutant pancreatic cancer cells than the corresponding PDEδ inhibitor. Taken together, this study expands the application scope of the ATTEC approach and highlights the effectiveness of the PDEδ autophagic degradation strategy in antitumor drug discovery.

Based on virtual screening methods and biological activity assessment, AK-968/13030056 has been identified as a potential mTOR inhibitor

The dysregulation of the PI3K/Akt/mTOR signaling pathway is one of the crucial pathways, leading to cancer development. mTOR (Rapamycin/mammalian target of rapamycin) participates in various intracellular signaling processes, regulating cellular metabolism, proliferation, and apoptosis. Numerous mTOR inhibitors have entered clinical research but are not yet on the market. There is an urgent need to develop a drug discovery strategy to accelerate the process. This study applied a virtual screening strategy to identify an mTOR inhibitor with potential inhibitory activity against mTOR. Firstly, we employed the Vina software to dock active and inactive datasets with two proteins (4L23 and 4JT6). The sensitive protein which is based on the docking results and ROC curve analysis is selected. Then, using the KNIME molecular screening program, virtual screening, cluster analysis, and binding model analysis, the 14 hit compounds are identified. Thirdly, through kinase assay and anti-proliferative activity evaluation, the hit compound AK-968/13030056 was ultimately confirmed. Finally, we conducted molecular docking studies and anti-tumor activity research on this compound. The inhibition rate of this compound on mTOR kinase was 70.62 %, exhibiting the strongest inhibitory activity against MCF-7 cells with an IC50 of 7.49 ± 0.87 μM. Subsequently, the biocompatibility of this compound was confirmed through hemolysis testing. Pharmacological experiments including AO staining, JC-1 assays, cell migration, and quantitative real-time PCR analysis also validated its anti-tumor activity. This study has developed a precise and rapid drug discovery strategy, successfully identifying a novel compound with inhibitory activity against the mTOR protein.

Molecular characterization of chronic cutaneous wounds reveals subregion- and wound type-specific differential gene expression.

A limited understanding of the pathology underlying chronic wounds has hindered the development of effective diagnostic markers and pharmaceutical interventions. This study aimed to elucidate the molecular composition of various common chronic ulcer types to facilitate drug discovery strategies. We conducted a comprehensive analysis of leg ulcers (LUs), encompassing venous and arterial ulcers, foot ulcers (FUs), pressure ulcers (PUs), and compared them with surgical wound healing complications (WHCs). To explore the pathophysiological mechanisms and identify similarities or differences within wounds, we dissected wounds into distinct subregions, including the wound bed, border, and peri-wound areas, and compared them against intact skin. By correlating histopathology, RNA sequencing (RNA-Seq), and immunohistochemistry (IHC), we identified unique genes, pathways, and cell type abundance patterns in each wound type and subregion. These correlations aim to aid clinicians in selecting targeted treatment options and informing the design of future preclinical and clinical studies in wound healing. Notably, specific genes, such as PITX1 and UPP1, exhibited exclusive upregulation in LUs and FUs, potentially offering significant benefits to specialists in limb preservation and clinical treatment decisions. In contrast, comparisons between different wound subregions, regardless of wound type, revealed distinct expression profiles. The pleiotropic chemokine-like ligand GPR15L (C10orf99) and transmembrane serine proteases TMPRSS11A/D were significantly upregulated in wound border subregions. Interestingly, WHCs exhibited a nearly identical transcriptome to PUs, indicating clinical relevance. Histological examination revealed blood vessel occlusions with impaired angiogenesis in chronic wounds, alongside elevated expression of genes and immunoreactive markers related to blood vessel and lymphatic epithelial cells in wound bed subregions. Additionally, inflammatory and epithelial markers indicated heightened inflammatory responses in wound bed and border subregions and reduced wound bed epithelialization. In summary, chronic wounds from diverse anatomical sites share common aspects of wound pathophysiology but also exhibit distinct molecular differences. These unique molecular characteristics present promising opportunities for drug discovery and treatment, particularly for patients suffering from chronic wounds. The identified diagnostic markers hold the potential to enhance preclinical and clinical trials in the field of wound healing.

Repurposing FDA-Approved Anastrozole-based Drugs for Breast Cancer through Drug-Drug Transcriptomic Similarity and Cavity Detection Guided Blind Docking

Breast cancer remains a significant global health concern, necessitating innovative strategies for drug discovery and development. Repurposing existing drugs offers a promising avenue, leveraging the wealth of information available on food and drug administration (FDA) approved compounds. This study investigates the repurposing potential of anastrozole-based drugs, already established for their efficacy in certain conditions, for breast cancer treatment. This research presents a multifaceted investigation into the identification, structural refinement, and virtual screening of potential therapeutics targeting the aromatase CYP19A enzyme, a crucial player in hormone-related conditions such as breast cancer. The study commences with the identification of anastrozole-based drugs exhibiting transcriptomic profiles closely resembling known breast cancer therapeutics. Through a comprehensive analysis, a subset of compounds demonstrates high transcriptomic similarity, suggesting shared molecular pathways and target interactions. Notably, anastrozole, a well-known aromatase inhibitor, emerges as a top candidate, highlighting its potential in breast cancer treatment. The crystallographic structure of aromatase CYP19A is subjected to meticulous preprocessing using the PDB-REDO server, resulting in significant improvements in various validation metrics. Structural changes, including alterations in rotamers, removal of water molecules, and peptide flips, indicate the success of the refinement process in enhancing the accuracy of the protein model. The refined structure serves as a reliable foundation for subsequent studies. Further, structure-based cavity detection unveils potential binding sites on the aromatase enzyme. Docking studies employing the cb-dock server elucidate the interaction patterns and binding affinities of selected compounds within these cavities. Anastrozole, along with other candidates like dolasetron and stiripentol, exhibits promising binding scores and interacts with specific residues crucial for enzyme activity. This integrative approach, combining transcriptomic similarity analysis, structural refinement, and virtual screening, provides valuable insights into potential lead compounds for the inhibition of aromatase in breast cancer therapy. The identified compounds offer a starting point for further experimental validation and drug development. The most promising compound that can be repurposed for as an aromatase inhibitor is dolasetron. Overall, this research contributes to the ongoing efforts to leverage computational methodologies for the rational design of targeted therapeutics against hormone-related disorders.

Eriodictyol Flavanones Based Virtual Screening of Bioactive Compounds from ChEMBL 2D Database with Classic 3-point Pharmacophore Screening Method for HER2 Inhibitors for Breast Cancer

Understanding binding interactions between flavanones and human epidermal growth factor receptor 2 (HER2) is an important step in developing effective treatments for breast cancer, and this study applies computational methods to do just that. This research presents a comprehensive computational methodology for identifying potential HER2 inhibitors with a focus on breast cancer treatment. The study leverages a combination of structural and pharmacophore-based approaches, starting with bioactive compound selection from the ChEMBL 2D database. The PDB-REDO refined crystal structure of Kinase domain of Human HER2 (erbB2) was used to conduct molecular docking simulations with the identified drugs. The Kleywegt-like plot analysis demonstrates the improved structural quality of the HER2 kinase domain after refinement, showing enhanced agreement with experimental data. Molecular docking simulations, conducted using the AutoDock tool, reveal the binding affinity and interaction patterns of selected compounds with the HER2 receptor. Virtual screening results highlight compounds with high binding affinity, favorable interaction patterns, and structural compatibility as potential lead candidates. To ensure safety and efficacy, ADMETox filtering was employed, providing insights into the compound’s toxicity profile and pharmacokinetic attributes. The selected compound, Eriodictyol (C20H20O6), exhibits a generally favorable safety profile, with predicted inactivity across multiple toxicity classifications and endpoints. While immunotoxicity is predicted, the overall low probabilities suggest a relatively low risk. Physicochemical and pharmacokinetic assessments indicate Eriodictyol’s potential for drug development. With a molecular weight of 356.37 g/mol, balanced lipophilicity, and high gastrointestinal absorption, the compound aligns with drug-likeness criteria. However, careful consideration is warranted due to its inhibitory effects on certain enzymes and alerts for catechol_A and isolated_alkene. In conclusion, this integrated computational approach streamlines the identification of potential HER2 inhibitors, offering a systematic strategy for drug discovery. Eriodictyol emerges as a promising candidate, demonstrating a favorable safety profile and pharmacokinetic attributes, paving the way for further in-depth studies and development as a potential therapeutic agent for breast cancer.

Dephosphorylation Switch DNAzyme-RCA Circuit: A Robust Strategy for the Homogeneous and Reliable Detection of FTO Demethylase.

Fat mass and obesity-associated protein (FTO) plays a crucial role in regulating the dynamic modification of N6-methyladenosine (m6A) in eukaryotic mRNA. Sensitive detection of the FTO level and efficient evaluation of the FTO demethylase activity are of great importance to early cancer diagnosis and anticancer drug discovery, which are currently challenged by limited sensitivity/precision and low throughput. Herein, a robust strategy based on the dephosphorylation switch DNAzyme-rolling circle amplification (RCA) circuit, termed DSD-RCA, is developed for highly sensitive detection of FTO and inhibitor screening. Initially, the catalytic activity of DNAzyme is silenced by engineering with an m6A modification in its catalytic core. Only in the presence of target FTO can the methyl group on DNAzyme be eliminated, resulting in the activation of the catalytic activity of DNAzyme and thus cleaving the hairpin substrate to release numerous primers. Different from the conventional methods that use the downstream cleavage primer with the original 3'-hydroxyl end directly as the RCA primer with the problem of high background signal, which should be compensated by additional separation and wash steps in heterogeneous format, our DSD-RCA assay uses the upstream cleavage primer with a 2',3'-cyclic phosphate terminus at the 3'-end serving as an intrinsically blocked 3' end. Only after a dephosphorylation reaction mediated by T4 polynucleotide kinase can the upstream cleavage primers with a resultant 3'-hydroxyl end be extended by RCA. With the high signal-to-noise ratio and homogeneous property, the proposed platform can sensitively detect FTO with a limit of detection of 31.4 pM, and the relative standard deviations (RSDs %) ranging from 0.8 to 2.0% were much lower than the heterogeneous methods. The DSD-RCA method was applied for analyzing FTO in cytoplasmic lysates from different cell lines and tissues of breast cancer patients and further used for screening FTO inhibitors without the need for separation or cleaning, providing an opportunity for achieving high throughput and demonstrating the potential applications of this strategy in disease diagnostics, drug discovery, and biological applications.

Exploring the antibiotic potential of cultured ‘unculturable’ bacteria

In response to the severe global antibiotic resistance crisis, this forum delves into ‘unculturable’ bacteria, believed to be a promising source of novel antibiotics. We propose remarkable drug discovery strategies that leverage these bacteria’s diversity, aspiring to transform resistance management. The urgent call for new antibiotics accentuates the essentiality of further research.

Design and Characterization of a Novel eEF2K Degrader with Potent Therapeutic Efficacy Against Triple-Negative Breast Cancer.

Dysregulated eEF2K expression is implicated in the pathogenesis of many human cancers, including triple-negative breast cancer (TNBC), making it a plausible therapeutic target. However, specific eEF2K inhibitors with potent anti-cancer activity have not been available so far. Targeted protein degradation has emerged as a new strategy for drug discovery. In this study, a novel small molecule chemical is designed and synthesized, named as compound C1, which shows potent activity in degrading eEF2K. C1 selectively binds to F8, L10, R144, C146, E229, and Y236 of the eEF2K protein and promotes its proteasomal degradation by increasing the interaction between eEF2K and the ubiquitin E3 ligase βTRCP in the form of molecular glue. C1 significantly inhibits the proliferation and metastasis of TNBC cells both in vitro and in vivo and in TNBC patient-derived organoids, and these antitumor effects are attributed to the degradation of eEF2K by C1. Additionally, combination treatment of C1 with paclitaxel, a commonly used chemotherapeutic drug, exhibits synergistic anti-tumor effects against TNBC. This study not only generates a powerful research tool to investigate the therapeutic potential of targeting eEF2K, but also provides a promising lead compound for developing novel drugs for the treatment of TNBC and other cancers.

Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques.

The opioid epidemic has cast a shadow over public health, necessitating immediate action to address its devastating consequences. To effectively combat this crisis, it is crucial to discover better opioid drugs with reduced addiction potential. Artificial intelligence-based and other machine learning tools, particularly deep learning models, have garnered significant attention in recent years for their potential to advance drug discovery. However, using these tools poses challenges, especially when training samples are insufficient to achieve adequate prediction performance. In this study, we investigate the effectiveness of transfer learning in building robust deep learning models to enhance ligand bioactivity prediction for each individual opioid receptor (OR) subtype. This is achieved by leveraging knowledge obtained from pretraining a model using supervised learning on a larger data set of bioactivity data combined with ligand-based and structure-based molecular descriptors related to the entire OR subfamily. Our studies hold the potential to advance opioid research by enabling the rapid identification of novel chemical probes with specific bioactivities, which can aid in the study of receptor function and contribute to the future development of improved opioid therapeutics.

Discovery of JN122, a Spiroindoline-Containing Molecule that Inhibits MDM2/p53 Protein-Protein Interaction and Exerts Robust In Vivo Antitumor Efficacy.

MDM2 and MDM4 cooperatively and negatively regulate p53, while this pathway is often hijacked by cancer cells in favor of their survival. Blocking MDM2/p53 interaction with small-molecule inhibitors liberates p53 from MDM2 mediated degradation, which is an attractive strategy for drug discovery. We reported herein structure-based discovery of highly potent spiroindoline-containing MDM2 inhibitor (-)60 (JN122), which also exhibited moderate activities against MDM4/p53 interactions. In a panel of cancer cell lines harboring wild type p53, (-)60 efficiently promoted activation of p53 and its target genes, inhibited cell cycle progression, and induced cell apoptosis. Interestingly, (-)60 also promoted degradation of MDM4. More importantly, (-)60 exhibited good PK properties and exerted robust antitumor efficacies in a systemic mouse xenograft model of MOLM-13. Taken together, our study showcases a class of potent MDM2 inhibitors featuring a novel spiro-indoline scaffold, which is promising for future development targeting cancer cells with wild-type p53.

An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors.

Cytochrome P450 3A4 (CYP3A4), a prominent member of the P450 enzyme superfamily, plays a crucial role in metabolizing various xenobiotics, including over 50% of clinically significant drugs. Evaluating CYP3A4 inhibition before drug approval is essential to avoiding potentially harmful pharmacokinetic drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Despite the development of several CYP inhibitor prediction models, the primary approach for screening CYP inhibitors still relies on experimental methods. This might stem from the limitations of existing models, which only provide deterministic classification outcomes instead of precise inhibition intensity (e.g., IC50) and often suffer from inadequate prediction reliability. To address this challenge, we propose an uncertainty-guided regression model to accurately predict the IC50 values of anti-CYP3A4 activities. First, a comprehensive data set of CYP3A4 inhibitors was compiled, consisting of 27,045 compounds with classification labels, including 4395 compounds with explicit IC50 values. Second, by integrating the predictions of the classification model trained on a larger data set and introducing an evidential uncertainty method to rank prediction confidence, we obtained a high-precision and reliable regression model. Finally, we use the evidential uncertainty values as a trustworthy indicator to perform a virtual screening of an in-house compound set. The in vitro experiment results revealed that this new indicator significantly improved the hit ratio and reduced false positives among the top-ranked compounds. Specifically, among the top 20 compounds ranked with uncertainty, 15 compounds were identified as novel CYP3A4 inhibitors, and three of them exhibited activities less than 1 μM. In summary, our findings highlight the effectiveness of incorporating uncertainty in compound screening, providing a promising strategy for drug discovery and development.

Identifying candidate drugs based on transcriptional landscape associated with triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a special type of breast cancer in which ER, PR and HER2 are all negative, characterized by high malignancy, strong invasiveness, and high recurrence rate. It is critical to develop novel drugs for improved therapies for triple-negative breast cancers. Here, we unveiled the transcriptional landscape associated with triple-negative breast cancer and identified candidate drugs using a comprehensive connectivity map. The candidate components could induce reverse expressions of the TNBC gene expression signature and trigger transcriptional reprogramming with a positive impact on modulating chemoresistance and the survival probability of breast cancer patients. Our study also provided an example of drug discovery using in silico drug screening followed by further validations, illustrating an effective computational drug discovery strategy.

Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease.

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately. Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD. Conclusion: The retained molecules and metabolite-protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

Late-Stage Halogenation of Peptides, Drugs and (Hetero)aromatic Compounds with a Nucleophilic Hydrazide Catalyst.

Unlike its other halogen atom siblings, chlorination of a bioactive compound can change its physiological characteristics, improve its pharmacological profile, and function as a point of diversification through cross-coupling reactions. As a result, it has been a crucial strategy for drug discovery and development. However, functional groups such as amines, amides, hydroxy groups, or carboxylic acids trap the Cl+ , severely limiting the reactivity and making direct chlorination far too difficult to be practical. Herein, we introduce a nucleophilic sulfonohydrazide catalyst for late-stage halogenation of peptides and drugs. This direct, mild and metal-free protocol shows high functional-group tolerance and is compatible with a range of structurally diverse peptides, drugs and aromatic compounds. Furthermore, DFT studies indicate that the reaction most likely proceeds via a cationic transition state. The gram-scale synthesis, high stability and efficiency of the catalyst provide a facile route for late-stage functionalization and intermediates for further derivatization.

Late‐Stage Halogenation of Peptides, Drugs and (Hetero)aromatic Compounds with a Nucleophilic Hydrazide Catalyst

AbstractUnlike its other halogen atom siblings, chlorination of a bioactive compound can change its physiological characteristics, improve its pharmacological profile, and function as a point of diversification through cross‐coupling reactions. As a result, it has been a crucial strategy for drug discovery and development. However, functional groups such as amines, amides, hydroxy groups, or carboxylic acids trap the Cl+, severely limiting the reactivity and making direct chlorination far too difficult to be practical. Herein, we introduce a nucleophilic sulfonohydrazide catalyst for late‐stage halogenation of peptides and drugs. This direct, mild and metal‐free protocol shows high functional‐group tolerance and is compatible with a range of structurally diverse peptides, drugs and aromatic compounds. Furthermore, DFT studies indicate that the reaction most likely proceeds via a cationic transition state. The gram‐scale synthesis, high stability and efficiency of the catalyst provide a facile route for late‐stage functionalization and intermediates for further derivatization.

Exploring drug discovery and development strategies in the context of monkeypox: A comprehensive investigation

Monkeypox is a rare but potentially serious viral infection that has re-emerged as a major public health problem due to recent outbreaks and the increased likelihood of zoonotic transmission. This review delves into the dynamic landscape of monkeypox drug discovery and drug development, revealing the multifaceted challenges and remarkable progress in the fight against this evolving infectious disease. This summary provides a comprehensive overview of ongoing efforts to identify effective antivirals, explore the potential for repurposing medicines, and pioneer innovative therapeutic strategies. As the search for effective treatments gains momentum, the abstract highlights the critical role of collaborative research efforts, the integration of cutting-edge technologies, and the need to adopt a multidisciplinary approach to address monkeypox complexity. In addition, the abstract highlights the critical importance of assessing the current status and future prospects of monkeypox drug development. By analysing the many factors shaping the field, including viral pathogenesis, host immune responses, and viral genetic diversity, this review contributes to a comprehensive understanding of the challenges inherent in addressing monkeypox.

SLGCN: Structure-enhanced line graph convolutional network for predicting drug–disease associations

Drug repositioning is a rapidly growing strategy in drug discovery, as the time and cost needed are considerably less compared to developing new drugs. In addition to traditional wet experiments, designing effective computational methods to discover potential drug–disease associations is an attractive shortcut in drug repositioning. Most current methods based on graph neural networks ignore the heterophily of the constructed drug–disease network, resulting in inefficient predictions. In this paper, a novel structure-enhanced line graph convolutional network (SLGCN) is proposed to learn comprehensive representations of drug–disease pairs, incorporating structural information to conduct heterophily. First, line graphs centered around drug–disease pairs are extracted. This process turns the association prediction task into a node classification problem, which better displays the learning ability of SLGCN. Then, in message aggregation, a relation matrix is proposed to mark the structural importance of neighboring nodes. In this way, messages from nodes with lower structural importance can be assigned small weights. Unlike vanilla GCN, which adds self-loops to average ego representations and aggregated messages, an update gate is proposed to integrate biology information contained in ego representations with topology information contained in aggregated messages. Extensive experiments show that SLGCN achieves better performance than other advanced methods among the two datasets.

Development of covalent inhibitors: Principle, design, and application in cancer

AbstractCovalent inhibitors have been a rapidly growing field in drug discovery due to their therapeutic potential and unique advantages in cancer therapy. As opposed to noncovalent inhibitory drugs, covalent inhibitors reversibly or irreversibly modify proximal nucleophilic amino acid residues on proteins, aiming to selectively recognize and bind to protein targets and addressing some of the challenges faced by noncovalent drugs. Most successful targeted covalent inhibitors depend primarily on binding‐site cysteine residues, but this has limitations for certain protein targets that lack targetable cysteine residues. Recently, the rational design of covalent inhibitors or covalent probes targeting other nucleophilic residues, such as lysine, tyrosine, serine, has turned out to be another promising strategy for cancer therapy. Thus, the development of novel strategies to extend the scope of covalent binding and improve the binding properties is required. This review gives a summary of the development of covalent inhibitors targeting noncysteine from different aspects, including target identification, structure–activity relationships, drug discovery strategies, and binding properties, in the hope of providing a scientific reference for future covalent drug discovery as a means of expanding research in cancer therapy.

RNA therapeutics for epilepsy: an emerging modality for drug discovery.

Drug discovery in epilepsy began with the finding of potassium bromide by Sir Charles Locock in 1857. The following century witnessed the introduction of phenotypic screening tests for discovering antiseizure medications (ASMs). Despite the high success rate of developing ASMs, they have so far failed in eliminating drug-resistance and in delivering disease-modifying treatments. This emphasises the need for new drug discovery strategies in epilepsy. RNA-based drugs have recently shown promise as a new modality with the potential of providing disease-modification and counteracting drug-resistance in epilepsy. RNA therapeutics can either be directed toward non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), or towards messenger RNAs (mRNAs). The former show promise in sporadic, non-genetic epilepsies as interference with ncRNAs allow for modulation of entire disease pathways, while the latter seems more promising in monogenic childhood epilepsies. Here, we describe therapeutic strategies for modulating disease-associated RNA molecules and highlight the potential of RNA therapeutics for the treatment of different patient populations such as sporadic, drug-resistant epilepsy, and childhood monogenic epilepsies.

UDock2: interactive real-time multi-body protein-protein docking software.

Protein-protein docking aims at predicting the geometry of protein interactions to gain insights into the mechanisms underlying these processes and develop new strategies for drug discovery. Interactive and user-oriented manipulation tools can support this task complementary to automated software. This article presents an interactive multi-body protein-protein docking software, UDock2, designed for research but also usable for teaching and popularization of science purposes due to its high usability. In UDock2, the users tackle the conformational space of protein interfaces using an intuitive real-time docking procedure with on-the-fly scoring. UDock2 integrates traditional computer graphics methods to facilitate the visualization and to provide better insight into protein surfaces, interfaces, and properties. UDock2 is open-source, cross-platform (Windows and Linux), and available at http://udock.fr. The code can be accessed at https://gitlab.com/Udock/Udock2.