RESUMEN
Non-viral nanoparticles (NPs) have seen heightened interest as a delivery method for a variety of clinically relevant nucleic acid cargoes in recent years. While much of the focus has been on lipid NPs, non-lipid NPs, including polymeric NPs, have the possibility of improved efficacy, safety, and targeting, especially to non-liver organs following systemic administration. A safe and effective systemic approach for intracellular delivery to the lungs could overcome limitations to intratracheal/intranasal delivery of NPs and improve clinical benefit for a range of diseases including cystic fibrosis. Here, engineered biodegradable poly (beta-amino ester) (PBAE) NPs are shown to facilitate efficient delivery of mRNA to primary human airway epithelial cells from both healthy donors and individuals with cystic fibrosis. Optimized NP formulations made with differentially endcapped PBAEs and systemically administered in vivo lead to high expression of mRNA within the lungs in BALB/c and C57 B/L mice without requiring a complex targeting ligand. High levels of mRNA-based gene editing were achieved in an Ai9 mouse model across bronchial, epithelial, and endothelial cell populations. No toxicity was observed either acutely or over time, including after multiple systemic administrations of the NPs. The non-lipid biodegradable PBAE NPs demonstrate high levels of transfection in both primary human airway epithelial cells and in vivo editing of lung cell types that are targets for numerous life-limiting diseases particularly single gene disorders such as cystic fibrosis and surfactant deficiencies.
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Pulmón , Ratones Endogámicos C57BL , Nanopartículas , Polímeros , ARN Mensajero , Animales , Pulmón/metabolismo , Humanos , Nanopartículas/química , ARN Mensajero/genética , ARN Mensajero/metabolismo , Polímeros/química , Ratones Endogámicos BALB C , Ratones , Fibrosis Quística , Femenino , Ligandos , Células Epiteliales/metabolismoRESUMEN
N-acetyl-L-cysteine (NAC) as a class of thiols is commonly used in the treatment of lung diseases, detoxification and prevention of liver damage. In this paper, 4-mercaptobenzoic acid (4-MBA) coated and polyvinylpyrrolidone (PVP) attached copper nanoclusters (4-MBA@PVP-CuNCs) were successfully synthesized using a simple one-pot method with an absolute quantum yield of 10.98 %, and its synthetic conditions (like effects of single/double ligands and temperature) were studied intensively. Then Hg2+ could quench the fluorescence of the 4-MBA@PVP-CuNCs and its fluorescence was restored with the addition of NAC. Based on the above principles, an off-on switching system was established to detect NAC. That is, the 4-MBA@PVP-CuNCs-Hg probe was prepared by adding Hg2+ to switch off the fluorescence of the CuNCs by static quenching, and then NAC was added to switch on the fluorescence of the probe based on the chelation of NAC and Hg2+. Moreover, the effects of metal ion types and mercury ion doses for the probe construction were also further discussed. The method showed excellent linearity in the range of 0.05-1.25 µM and low detection limit of 16 nM. Meanwhile, good recoveries in real urine, tablets and pellets were observed, which proved the reliability of the method and provided a convenient, fast and sensitive method for NAC detection.
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Acetilcisteína , Cobre , Límite de Detección , Nanopartículas del Metal , Espectrometría de Fluorescencia , Compuestos de Sulfhidrilo , Acetilcisteína/química , Acetilcisteína/orina , Cobre/química , Cobre/análisis , Espectrometría de Fluorescencia/métodos , Compuestos de Sulfhidrilo/química , Compuestos de Sulfhidrilo/análisis , Ligandos , Nanopartículas del Metal/química , Mercurio/análisis , Mercurio/orina , Humanos , Colorantes Fluorescentes/química , Povidona/química , Benzoatos/química , Polímeros/químicaRESUMEN
Harnessing bacteria for superoxide production in bioremediation holds immense promise, yet its practical application is hindered by slow production rates and the relatively weak redox potential of superoxide. This study delves into a cost-effective approach to amplify superoxide production using an Arthrobacter strain, a prevalent soil bacterial genus. Our research reveals that introducing a carbon source along with specific iron-binding ligands, including deferoxamine (DFO), diethylenetriamine pentaacetate (DTPA), citrate, and oxalate, robustly augments microbial superoxide generation. Moreover, our findings suggest that these iron-binding ligands play a pivotal role in converting superoxide into hydroxyl radicals by modulating the electron transfer rate between Fe(III)/Fe(II) and superoxide. Remarkably, among the tested ligands, only DTPA emerges as a potent promoter of this conversion process when complexed with Fe(III). We identify an optimal Fe(III) to DTPA ratio of approximately 1:1 for enhancing hydroxyl radical production within the Arthrobacter culture. This research underscores the efficacy of simultaneously introducing carbon sources and DTPA in facilitating superoxide production and its subsequent conversion to hydroxyl radicals, significantly elevating bioremediation performance. Furthermore, our study reveals that DTPA augments superoxide production in cultures of diverse soils, with various soil microorganisms beyond Arthrobacter identified as contributors to superoxide generation. This emphasizes the universal applicability of DTPA across multiple bacterial genera. In conclusion, our study introduces a promising methodology for enhancing microbial superoxide production and its conversion into hydroxyl radicals. These findings hold substantial implications for the deployment of microbial reactive oxygen species in bioremediation, offering innovative solutions for addressing environmental contamination challenges.
Asunto(s)
Arthrobacter , Biodegradación Ambiental , Radical Hidroxilo , Hierro , Superóxidos , Radical Hidroxilo/metabolismo , Superóxidos/metabolismo , Arthrobacter/metabolismo , Hierro/metabolismo , Ligandos , Microbiología del Suelo , Contaminantes del Suelo/metabolismo , Deferoxamina/metabolismoRESUMEN
Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.
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Simulación del Acoplamiento Molecular , Conformación Proteica , Proteínas , Ligandos , Proteínas/química , Proteínas/metabolismo , Sitios de Unión , Unión Proteica , Programas Informáticos , Diseño de Fármacos , Modelos MolecularesRESUMEN
In recent years, advancements in deep learning techniques have significantly expanded the structural coverage of the human proteome. GalaxySagittarius-AF translates these achievements in structure prediction into target prediction for druglike compounds by incorporating predicted structures. This web server searches the database of human protein structures using both similarity- and structure-based approaches, suggesting potential targets for a given druglike compound. In comparison to its predecessor, GalaxySagittarius, GalaxySagittarius-AF utilizes an enlarged structure database, incorporating curated AlphaFold model structures alongside their binding sites and ligands, predicted using an updated version of GalaxySite. GalaxySagittarius-AF covers a large human protein space compared to many other available computational target screening methods. The structure-based prediction method enhances the use of expanded structural information, differentiating it from other target prediction servers that rely on ligand-based methods. Additionally, the web server has undergone enhancements, operating two to three times faster than its predecessor. The updated report page provides comprehensive information on the sequence and structure of the predicted protein targets. GalaxySagittarius-AF is accessible at https://galaxy.seoklab.org/sagittarius_af without the need for registration.
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Proteoma , Humanos , Proteoma/química , Proteoma/metabolismo , Ligandos , Bases de Datos de Proteínas , Sitios de Unión , Programas Informáticos , Biología Computacional/métodos , Conformación Proteica , Aprendizaje Profundo , Descubrimiento de Drogas/métodos , Modelos Moleculares , Proteínas/química , Proteínas/metabolismoRESUMEN
The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.
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Simulación del Acoplamiento Molecular , Programas Informáticos , Ligandos , Algoritmos , Descubrimiento de Drogas/métodos , Unión Proteica , Humanos , Proteínas/química , Proteínas/metabolismo , Interfaz Usuario-ComputadorRESUMEN
Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.
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Descubrimiento de Drogas , Conformación Proteica , Descubrimiento de Drogas/métodos , Simulación del Acoplamiento Molecular , Unión Proteica , Simulación de Dinámica Molecular , Humanos , Inhibidores de Proteínas Quinasas/farmacología , Inhibidores de Proteínas Quinasas/química , Ligandos , Proteínas Quinasas/química , Proteínas Quinasas/metabolismoRESUMEN
G protein-coupled receptors' conformational landscape can be affected by their local, microscopic interactions within the cell plasma membrane. We employ here a pleiotropic stimulus, namely osmotic swelling, to alter the cortical environment within intact cells and monitor the response in terms of receptor function and downstream signaling. We observe that in osmotically swollen cells the ß2-adrenergic receptor, a prototypical GPCR, favors an active conformation, resulting in cAMP transient responses to adrenergic stimulation that have increased amplitude. The results are validated in primary cell types such as adult cardiomyocytes, a model system where swelling occurs upon ischemia-reperfusion injury. Our results suggest that receptors' function is finely modulated by their biophysical context, and specifically that osmotic swelling acts as a potentiator of downstream signaling, not only for the ß2-adrenergic receptor, but also for other receptors, hinting at a more general regulatory mechanism.
Asunto(s)
AMP Cíclico , Miocitos Cardíacos , Receptores Adrenérgicos beta 2 , Transducción de Señal , Receptores Adrenérgicos beta 2/metabolismo , Miocitos Cardíacos/metabolismo , Humanos , Animales , Ligandos , AMP Cíclico/metabolismo , Membrana Celular/metabolismo , Células HEK293 , RatonesRESUMEN
Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.
Asunto(s)
Inteligencia Artificial , Descubrimiento de Drogas , Simulación del Acoplamiento Molecular , Descubrimiento de Drogas/métodos , Humanos , Canal de Sodio Activado por Voltaje NAV1.7/metabolismo , Canal de Sodio Activado por Voltaje NAV1.7/química , Unión Proteica , Cristalografía por Rayos X , Ligandos , Ubiquitina-Proteína Ligasas/metabolismo , Ubiquitina-Proteína Ligasas/química , Bibliotecas de Moléculas Pequeñas/química , Bibliotecas de Moléculas Pequeñas/farmacología , Evaluación Preclínica de Medicamentos/métodosRESUMEN
Although aminergic GPCRs are the target for ~25% of approved drugs, developing subtype selective drugs is a major challenge due to the high sequence conservation at their orthosteric binding site. Bitopic ligands are covalently joined orthosteric and allosteric pharmacophores with the potential to boost receptor selectivity and improve current medications by reducing off-target side effects. However, the lack of structural information on their binding mode impedes rational design. Here we determine the cryo-EM structure of the hD3R:GαOßγ complex bound to the D3R selective bitopic agonist FOB02-04A. Structural, functional and computational analyses provide insights into its binding mode and point to a new TM2-ECL1-TM1 region, which requires the N-terminal ordering of TM1, as a major determinant of subtype selectivity in aminergic GPCRs. This region is underexploited in drug development, expands the established secondary binding pocket in aminergic GPCRs and could potentially be used to design novel and subtype selective drugs.
Asunto(s)
Microscopía por Crioelectrón , Receptores de Dopamina D3 , Humanos , Sitios de Unión , Receptores de Dopamina D3/metabolismo , Receptores de Dopamina D3/química , Receptores de Dopamina D3/agonistas , Células HEK293 , Ligandos , Unión Proteica , Animales , Modelos MolecularesRESUMEN
We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound's spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.
Asunto(s)
Aprendizaje Automático , Simulación del Acoplamiento Molecular , Proteínas , Simulación del Acoplamiento Molecular/métodos , Proteínas/química , Proteínas/metabolismo , Unión Proteica , Ligandos , Conformación Proteica , Programas InformáticosRESUMEN
The activation of a G protein-coupled receptor (GPCR) leads to the formation of a ternary complex between agonist, receptor, and G protein that is characterized by high-affinity binding. Allosteric modulators bind to a distinct binding site from the orthosteric agonist and can modulate both the affinity and the efficacy of orthosteric agonists. The influence allosteric modulators have on the high-affinity active state of the GPCR-G protein ternary complex is unknown due to limitations on attempting to characterize this interaction in recombinant whole cell or membrane-based assays. Here, we use the purified M2 muscarinic acetylcholine receptor reconstituted into nanodiscs to show that, once the agonist-bound high-affinity state is promoted by the G protein, positive allosteric modulators stabilize the ternary complex that, in the presence of nucleotides, leads to an enhanced initial rate of signaling. Our results enhance our understanding of how allosteric modulators influence orthosteric ligand signaling and will aid the design of allosteric therapeutics.
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Unión Proteica , Receptor Muscarínico M2 , Receptores Acoplados a Proteínas G , Regulación Alostérica , Humanos , Receptores Acoplados a Proteínas G/metabolismo , Receptores Acoplados a Proteínas G/química , Receptor Muscarínico M2/metabolismo , Receptor Muscarínico M2/química , Ligandos , Sitios de Unión , Transducción de Señal , Proteínas de Unión al GTP/metabolismo , Sitio AlostéricoRESUMEN
Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical entities with experimentally confirmed activity on intended targets. Here, we probe the application of CLM to generate multi-target ligands for designed polypharmacology. We capitalize on the ability of CLM to learn from small fine-tuning sets of molecules and successfully bias the model towards designing drug-like molecules with similarity to known ligands of target pairs of interest. Designs obtained from CLM after pooled fine-tuning are predicted active on both proteins of interest and comprise pharmacophore elements of ligands for both targets in one molecule. Synthesis and testing of twelve computationally favored CLM designs for six target pairs reveals modulation of at least one intended protein by all selected designs with up to double-digit nanomolar potency and confirms seven compounds as designed dual ligands. These results corroborate CLM for multi-target de novo design as source of innovation in drug discovery.
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Aprendizaje Profundo , Diseño de Fármacos , Ligandos , Descubrimiento de Drogas/métodos , Humanos , Modelos Químicos , Polifarmacología , Proteínas/química , Proteínas/metabolismoRESUMEN
Sol g 2, a major protein found in the venom of the tropical fire ant (Solenopsis geminata), is well-known for its ability to bind various hydrophobic molecules. In this study, we investigate the binding activity of recombinant Sol g 2.1 protein (rSol g 2.1) with potential molecules, including (E)-ß-Farnesene, α-Caryophyllene, and 1-Octen-3-ol at different pH levels (pH 7.4 and 5.5) using fluorescence competitive binding assays (FCBA). Our results revealed that Sol g 2.1 protein has higher affinity binding with these ligands at neutral pH. Relevance to molecular docking and molecular dynamics simulations were utilized to provide insights into the stability and conformational dynamics of Sol g 2.1 and its ligand complexes. After simulation, we found that Sol g 2.1 protein has higher affinity binding with these ligands as well as high structural stability at pH 7.4 than at an acidic pH level, indicating by RMSD, RMSF, Rg, SASA, and principal component analysis (PCA). Additionally, the Sol g 2.1 protein complexes at pH 7.4 showed significantly lower binding free energy (∆Gbind) and higher total residue contributions, particularly from key non-polar amino acids such as Trp36, Met40, Cys62, and Ile104, compared to the lower pH environment. These explain why they exhibited higher binding affinity than the lower pH. Therefore, we suggested that Sol g 2.1 protein is a pH-responsive carrier protein. These findings also expand our understanding of protein-ligand interactions and offer potential avenues for the development of innovative drug delivery strategies targeting Sol g 2.1 protein.
Asunto(s)
Simulación de Dinámica Molecular , Unión Proteica , Conformación Proteica , Concentración de Iones de Hidrógeno , Ligandos , Animales , Simulación del Acoplamiento Molecular , Proteínas de Insectos/química , Proteínas de Insectos/metabolismo , Hormigas/metabolismoRESUMEN
Schistosomiasis, also known as bilharzia or snail fever, is a tropical parasitic disease resulting from flatworms of the Schistosoma genus. This often overlooked disease has significant impacts in affected regions, causing enduring morbidity, hindering child development, reducing productivity, and creating economic burdens. Praziquantel (PZQ) is currently the only treatment option for schistosomiasis. Given the potential rise of drug resistance and the limited treatment choices available, there is a need to develop more effective inhibitors for this neglected tropical disease (NTD). In view of this, quantitative structure-activity relationship studies (QSAR), molecular docking, molecular dynamics simulations, drug-likeness, and ADMET predictions were applied to 31 inhibitors of Schistosoma mansoni Dihydroorotate dehydrogenase (SmDHODH). The designed QSAR model demonstrated robust statistical parameters including an R2 of 0.911, R2adj of 0.890, Q2cv of 0.686, R2pred of 0.807, and cR2p of 0.825, confirming its robustness. Compound 26, identified as the most active derivative, emerged as a lead candidate for new potential inhibitors through ligand-based drug design. Subsequently, 12 novel compounds (26A-26L) were designed with enhanced inhibition activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding and hydrophobic interactions, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules (26A and 26L). Furthermore, drug-likeness and ADMET prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for treating schistosomiasis.
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Dihidroorotato Deshidrogenasa , Diseño de Fármacos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Oxidorreductasas actuantes sobre Donantes de Grupo CH-CH , Relación Estructura-Actividad Cuantitativa , Schistosoma mansoni , Schistosoma mansoni/efectos de los fármacos , Schistosoma mansoni/enzimología , Oxidorreductasas actuantes sobre Donantes de Grupo CH-CH/antagonistas & inhibidores , Oxidorreductasas actuantes sobre Donantes de Grupo CH-CH/química , Animales , Esquistosomiasis/tratamiento farmacológico , Ligandos , Inhibidores Enzimáticos/química , Inhibidores Enzimáticos/farmacología , Humanos , Antihelmínticos/farmacología , Antihelmínticos/química , Descubrimiento de Drogas , Esquistosomiasis mansoni/tratamiento farmacológicoRESUMEN
Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.
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Simulación de Dinámica Molecular , Proteínas , Programas Informáticos , Ligandos , Proteínas/química , Proteínas/metabolismo , Bases de Datos de Proteínas , Modelos Moleculares , Conformación Proteica , Interfaz Usuario-Computador , Descubrimiento de Drogas/métodosRESUMEN
Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-ligand binding interactions. However, beyond binding affinity, effective drug molecules must manifest other essential properties such as high drug-likeness, which are not explicitly addressed by current target-aware generative methods. In this article, aiming to bridge the gap of multi-objective target-aware molecule generation in the field of deep learning-based drug discovery, we propose ParetoDrug, a Pareto Monte Carlo Tree Search (MCTS) generation algorithm. ParetoDrug searches molecules on the Pareto Front in chemical space using MCTS to enable synchronous optimization of multiple properties. Specifically, ParetoDrug utilizes pretrained atom-by-atom autoregressive generative models for the exploration guidance to desired molecules during MCTS searching. Besides, when selecting the next atom symbol, a scheme named ParetoPUCT is proposed to balance exploration and exploitation. Benchmark experiments and case studies demonstrate that ParetoDrug is highly effective in traversing the large and complex chemical space to discover novel compounds with satisfactory binding affinities and drug-like properties for various multi-objective target-aware drug discovery tasks.
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Algoritmos , Descubrimiento de Drogas , Método de Montecarlo , Descubrimiento de Drogas/métodos , Ligandos , Aprendizaje Profundo , HumanosRESUMEN
Hyperpolarization derived from water protons enhances the NMR signal of 15N nuclei in a small molecule, enabling the sensitive detection of a protein-ligand interaction. The water hyperpolarized by dissolution dynamic nuclear polarization (D-DNP) acts as a universal signal enhancement agent. The 15N signal of benzamidine was increased by 1480-fold through continuous polarization transfer by J-coupling-mediated cross-polarization (J-CP) via the exchangeable protons. The signal enhancement factor favorably compares to factors of 110- or 17-fold using non-CP-based polarization transfer mechanisms. The hyperpolarization enabled detection of the binding of benzamidine to the target protein trypsin with a single-scan measurement of 15N R2 relaxation. J-CP provides an efficient polarization mechanism for 15N or other low-frequency nuclei near an exchangeable proton. The hyperpolarization transfer sustained within the relaxation time limit of water protons additionally can be applied for the study of macromolecular structure and biological processes.
Asunto(s)
Protones , Agua , Agua/química , Ligandos , Unión Proteica , Benzamidinas/química , Resonancia Magnética Nuclear Biomolecular , Tripsina/química , Tripsina/metabolismo , Isótopos de Nitrógeno/químicaRESUMEN
γ-Hydroxybutyric acid (GHB) analogs are small molecules that bind competitively to a specific cavity in the oligomeric CaMKIIα hub domain. Binding affects conformation and stability of the hub domain, which may explain the neuroprotective action of some of these compounds. Here, we describe molecular details of interaction of the larger-type GHB analog 2-(6-(4-chlorophenyl)imidazo[1,2-b]pyridazine-2-yl)acetic acid (PIPA). Like smaller-type analogs, PIPA binding to the CaMKIIα hub domain promoted thermal stability. PIPA additionally modulated CaMKIIα activity under sub-maximal CaM concentrations and ultimately led to reduced substrate phosphorylation. A high-resolution X-ray crystal structure of a stabilized CaMKIIα (6x mutant) hub construct revealed details of the binding mode of PIPA, which involved outward placement of tryptophan 403 (Trp403), a central residue in a flexible loop close to the upper hub cavity. Small-angle X-ray scattering (SAXS) solution structures and mass photometry of the CaMKIIα wild-type hub domain in the presence of PIPA revealed a high degree of ordered self-association (stacks of CaMKIIα hub domains). This stacking neither occurred with the smaller compound 3-hydroxycyclopent-1-enecarboxylic acid (HOCPCA), nor when Trp403 was replaced with leucine (W403L). Additionally, CaMKIIα W403L hub was stabilized to a larger extent by PIPA compared to CaMKIIα hub wild type, indicating that loop flexibility is important for holoenzyme stability. Thus, we propose that ligand-induced outward placement of Trp403 by PIPA, which promotes an unforeseen mechanism of hub domain stacking, may be involved in the observed reduction in CaMKIIα kinase activity. Altogether, this sheds new light on allosteric regulation of CaMKIIα activity via the hub domain.
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Proteína Quinasa Tipo 2 Dependiente de Calcio Calmodulina , Dominios Proteicos , Proteína Quinasa Tipo 2 Dependiente de Calcio Calmodulina/química , Proteína Quinasa Tipo 2 Dependiente de Calcio Calmodulina/metabolismo , Proteína Quinasa Tipo 2 Dependiente de Calcio Calmodulina/genética , Cristalografía por Rayos X , Humanos , Ligandos , Modelos Moleculares , Dispersión del Ángulo Pequeño , Triptófano/química , Triptófano/metabolismo , Piridazinas/química , Piridazinas/metabolismo , FosforilaciónRESUMEN
The epidermal growth factor (EGF) receptor (EGFR) is activated by the binding of one of seven EGF-like ligands to its ectodomain. Ligand binding results in EGFR dimerization and stabilization of the active receptor conformation subsequently leading to activation of downstream signaling. Aberrant activation of EGFR contributes to cancer progression through EGFR overexpression/amplification, modulation of its positive and negative regulators, and/or activating mutations within EGFR. EGFR targeted therapeutic antibodies prevent dimerization and interaction with endogenous ligands by binding the ectodomain of EGFR. However, these antibodies have had limited success in the clinic, partially due to EGFR ectodomain resistance mutations, and are only applicable to a subset of patients with EGFR-driven cancers. These limitations suggest that alternative EGFR targeted biologics need to be explored for EGFR-driven cancer therapy. To this end, we analyze the EGFR interfaces of known inhibitory biologics with determined structures in the context of endogenous ligands, using the Rosetta macromolecular modeling software to highlight the most important interactions on a per-residue basis. We use this analysis to identify the structural determinants of EGFR targeted biologics. We suggest that commonly observed binding motifs serve as the basis for rational design of new EGFR targeted biologics, such as peptides, antibodies, and nanobodies.