RESUMEN

This study presents a comprehensive structural analysis of the adducts formed upon the reaction of two Ru(III) complexes [HIsq][trans-RuIIICl4(dmso)(Isq)] (1) and [H2Ind][trans-RuIIICl4(dmso)(HInd)] (2) (where HInd-indazole, Isq-isoquinoline, analogs of NAMI-A) and two Ru(II) complexes, cis-[RuCl2(dmso)4] (c) and trans-[RuCl2(dmso)4] (t), with hen-egg white lysozyme (HEWL). Additionally, the crystal structure of an adduct of human lysozyme (HL) with ruthenium complex, [H2Ind][trans-RuCl4(dmso)(HInd)] was solved. X-ray crystallographic data analysis revealed that all studied Ru complexes, regardless of coordination surroundings and metal center charge, coordinate to the same amino acids (His15, Arg14, and Asp101) of HEWL, losing most of their original ligands. In the case of the 2-HL adduct, two distinct metalation sites: (i) Arg107, Arg113 and (ii) Gln127, Gln129, were identified. Crystallographic data were supported by studies of the interaction of 1 and 2 with HEWL in an aqueous solution. Hydrolytic stability studies revealed that both complexes 1 and 2 liberate the N-heterocyclic ligand under crystallization-like conditions (pH 4.5) as well as under physiological pH conditions, and this process is not significantly affected by the presence of HEWL. A comparative examination of nine crystal structures of Ru complexes with lysozyme, obtained through soaking and co-crystallization experiments, together with in-solution studies of the interaction between 1 and 2 with HEWL, indicates that the hydrolytic release of the N-heterocyclic ligand is one of the critical factors in the interaction between Ru complexes and lysozyme. This understanding is crucial in shedding light on the tendency of Ru complexes to target diverse metalation sites during the formation and in the final forms of the adducts with proteins.

RESUMEN

G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and ß-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.

Asunto(s)

RESUMEN

Ethionamide (ETO) is a prodrug that is primarily used as a second-line agent in the treatment of tuberculosis. Among the bacterial ETO activators, the monooxygenase MymA has been recently identified, and its expression is regulated by the mycobacterial regulator VirS. The discovery of VirS ligands that can enhance mymA expression and thereby increase the antimycobacterial efficacy of ETO, has led to the development of a novel therapeutic strategy against tuberculosis. This strategy involves the selection of preclinical candidates, including SMARt751. We report the first crystal structure of the AraC-like regulator VirS, in complex with SMARt751, refined at 1.69 Å resolution. Crystals were obtained via an in situ proteolysis method in the requisite presence of SMARt751. The elucidated structure corresponds to the ligand-binding domain of VirS, adopting an α/ß fold with structural similarities to H-NOX domains. Within the VirS structure, SMARt751 is situated in a completely enclosed hydrophobic cavity, where it forms hydrogen bonds with Asn11 and Asn149 as well as van der Waals contacts with various hydrophobic amino acids. Comprehensive structural comparisons within the AraC family of transcriptional regulators are conducted and analyzed to figure out the effects of the SMARt751 binding on the regulatory activity of VirS.

Asunto(s)

RESUMEN

Transient receptor potential melastatin 2 (TRPM2) is a calcium-permeable, nonselective cation channel with a widespread distribution throughout the body. It is involved in many pathological and physiological processes, making it a potential therapeutic target for various diseases, including Alzheimer's disease, Parkinson's disease, and cancers. New analytical techniques are beneficial for gaining a deeper understanding of its involvement in disease pathogenesis and for advancing the drug discovery for TRPM2-related diseases. In this work, we present the application of collision-induced affinity selection mass spectrometry (CIAS-MS) for the direct identification of ligands binding to TRPM2. CIAS-MS circumvents the need for high mass detection typically associated with mass spectrometry of large membrane proteins. Instead, it focuses on the detection of small molecules dissociated from the ligand-protein-detergent complexes. This affinity selection approach consolidates all affinity selection steps within the mass spectrometer, resulting in a streamlined process. We showed the direct identification of a known TRPM2 ligand dissociated from the protein-ligand complex. We demonstrated that CIAS-MS can identify binding ligands from complex mixtures of compounds and screened a compound library against TRPM2. We investigated the impact of voltage increments and ligand concentrations on the dissociation behavior of the binding ligand, revealing a dose-dependent relationship.

Asunto(s)

RESUMEN

Fragment-based drug design is an emerging technology in pharmaceutical research and development. One of the key aspects of this technology is the identification and quantitative characterization of molecular fragments. This study presents a strategy for identifying important molecular fragments based on molecular fingerprints and decision tree algorithms and verifies its feasibility in predicting protein-ligand binding affinity. Specifically, the three-dimensional (3D) structures of protein-ligand complexes are encoded using extended-connectivity fingerprints (ECFP), and three decision tree models, namely Random Forest, XGBoost, and LightGBM, are used to quantitatively characterize the feature importance, thereby extracting important molecular fragments with high reliability. Few-shot learning reveals that the extracted molecular fragments contribute significantly and consistently to the binding affinity even with a small sample size. Despite the absence of location and distance information for molecular fragments in ECFP, 3D visualization, in combination with the reverse ECFP process, shows that the majority of the extracted fragments are located at the binding interface of the protein and the ligand. This alignment with the distance constraints critical for binding affinity further supports the reliability of the strategy for identifying important molecular fragments.

Asunto(s)

RESUMEN

Transporters of the solute carrier family 12 (SLC12) carry inorganic cations such as Na+ and/or K+ alongside Cl across the plasma membrane of cells. These tightly coupled, electroneutral, transporters are expressed in almost all tissues/organs in the body where they fulfil many critical functions. The family includes two key transporters participating in salt reabsorption in the kidney: the Na-K-2Cl cotransporter-2 (NKCC2), expressed in the loop of Henle, and the Na-Cl cotransporter (NCC), expressed in the distal convoluted tubule. NCC and NKCC2 are the targets of thiazides and "loop" diuretics, respectively, drugs that are widely used in clinical medicine to treat hypertension and edema. Bumetanide, in addition to its effect as a loop diuretic, has recently received increasing attention as a possible therapeutic agent for neurodevelopmental disorders. This chapter also describes how over the past two decades, the pharmacology of Na+ independent transporters has expanded significantly to provide novel tools for research. This work has indeed led to the identification of compounds that are 100-fold to 1000-fold more potent than furosemide, the first described inhibitor of K-Cl cotransport, and identified compounds that possibly directly stimulate the function of the K-Cl cotransporter. Finally, the recent cryo-electron microscopy revolution has begun providing answers as to where and how pharmacological agents bind to and affect the function of the transporters.

Asunto(s)

RESUMEN

Quantitative Structure-activity Relationship (QSAR) studies gained a foothold in the mid-1960s to rationalise the biological activity of medicinally important compounds. Since then, the advancements in computer hardware and software added many new techniques and areas to this field of study. Molecular dynamics (MD) simulations are one such technique in direct drug design approaches. MD simulations have a special place in drug design studies because they decode the dynamics of intermolecular interactions between a biological target and its potential ligands/inhibitors. The trajectories from MD simulations provide different non-bonding interaction parameters to assess the compatibility of the protein-ligand complex and thereby facilitate the design of prospective compounds prior to their wet-lab exploration. Histone deacetylases (HDACs) play a key role in epigenetics and they are promising drug targets for cancer and various other diseases. This review attempts to shed some light on the modelling studies of HDAC inhibitors as anticancer agents. In view of the advantages of MD simulations in direct drug design, this review also discusses the fragment-based approach in designing new inhibitors of HDAC8 and HDAC2, starting from the interaction energies of ligand fragments obtained from the MD simulations of respective protein-ligand complexes. Here, the design of new anticancer compounds from largazole thiol, trichostatin A, vorinostat, and several other prototype compounds are reviewed. These studies may stimulate the interest of medicinal chemists in MD simulations as a direct drug design approach for new drug development.

Asunto(s)

RESUMEN

Alkaloids are among the most important and best-known secondary metabolites as sources of new drugs from medicinal plants and marine organisms. A phytochemical investigation of the whole plant of Crinum asiaticum var. sinicum resulted in the isolation of seven alkaloids (1-7), including one new dimeric compound, bis-(-)-8-demethylmaritidine (1). Their structures were elucidated using NMR and HR-ESI-MS. The absolute configuration of new compound 1 was established by circular dichroism spectroscopy. All isolated compounds were evaluated for their inhibitory effects on acetylcholinesterase (AChE) activity in vitro. Among them, compound 1 exhibited the most potent AChE inhibition. Moreover, molecular docking and molecular dynamics simulations were carried out for the most active compound to investigate their binding interactions and dynamics behavior of the AChE protein-ligand complex. Therefore, compound 1 may be a potential candidate for effectively treating Alzheimer's disease.

RESUMEN

CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.

Asunto(s)

RESUMEN

BACKGROUND: Three-dimensional structures of protein-ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug design. However, their high-dimensional and multimodal nature hinders end-to-end modeling, and earlier approaches depend inherently on existing protein structures. To overcome these limitations and expand the range of complexes that can be accurately modeled, it is necessary to develop efficient end-to-end methods. RESULTS: We introduce an equivariant diffusion-based generative model that learns the joint distribution of ligand and protein conformations conditioned on the molecular graph of a ligand and the sequence representation of a protein extracted from a pre-trained protein language model. Benchmark results show that this protein structure-free model is capable of generating diverse structures of protein-ligand complexes, including those with correct binding poses. Further analyses indicate that the proposed end-to-end approach is particularly effective when the ligand-bound protein structure is not available. CONCLUSION: The present results demonstrate the effectiveness and generative capability of our end-to-end complex structure modeling framework with diffusion-based generative models. We suppose that this framework will lead to better modeling of protein-ligand complexes, and we expect further improvements and wide applications.

Asunto(s)

RESUMEN

Molecular clustering analysis has been developed to facilitate visual inspection in the process of structure-based virtual screening. However, traditional methods based on molecular fingerprints or molecular descriptors limit the accuracy of selecting active hit compounds, which may be attributed to the lack of representations of receptor structural and protein-ligand interaction during the clustering. Here, a novel deep clustering framework named ClusterX is proposed to learn molecular representations of protein-ligand complexes and cluster the ligands. In ClusterX, the graph was used to represent the protein-ligand complex, and the joint optimisation can be used efficiently for learning the cluster-friendly features. Experiments on the KLIFs database show that the model can distinguish well between the binding modes of different kinase inhibitors. To validate the effectiveness of the model, the clustering results on the virtual screening dataset further demonstrated that ClusterX achieved better or more competitive performance against traditional methods, such as SIFt and extended connectivity fingerprints. This framework may provide a unique tool for clustering analysis and prove to assist computational medicinal chemists in visual decision-making.

Asunto(s)

RESUMEN

SARS-CoV-2 is a positive-sense single-stranded RNA virus that causes a deadly coronavirus disease (COVID-19) in humans. The infection of SARS-CoV-2 in humans involves a viral surface spike glycoprotein containing the receptor-binding domain (RBD). The interactions of SARS-CoV-2 with the host angiotensin-converting enzyme 2 (ACE2) receptor are mediated by RBD. It binds to the host ACE2 and influences viral replication and disease pathogenesis. Therefore, targeting the RBD to prevent SARS-CoV-2 infections is of utmost importance. In this study, we used docking and molecular dynamics simulations to understand the binding effect of andrographolide on the SARS-CoV-2 spike protein. During docking, a strong binding affinity was observed between the ligand and the target receptor protein. MD results demonstrated higher conformational fluctuations in the ligand-free protein compared to the bound form. Several residues in the active sites make conformational rearrangements for the S protein to interact with the ligand. While RBD experiences conformational transition to gain more stability upon binding with the ligand. This binding is strengthened via several non-covalent interactions that make the complex structure more stable with higher binding affinity. Overall findings of the study may shed some valuable insights concerning the development of potential therapeutics in the strategies for COVID-19 prevention.

Asunto(s)

RESUMEN

Covalent inhibitors have received extensive attentions in the past few decades because of their long residence time, high binding efficiency and strong selectivity. Therefore, it is valuable to develop computational tools like molecular docking for modeling of covalent protein-ligand interactions or screening of potential covalent drugs. Meeting the needs, we have proposed HCovDock, an efficient docking algorithm for covalent protein-ligand interactions by integrating a ligand sampling method of incremental construction and a scoring function with covalent bond-based energy. Tested on a benchmark containing 207 diverse protein-ligand complexes, HCovDock exhibits a significantly better performance than seven other state-of-the-art covalent docking programs (AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro and MOE). With the criterion of ligand root-mean-squared distance < 2.0 Å, HCovDock obtains a high success rate of 70.5% and 93.2% in reproducing experimentally observed structures for top 1 and top 10 predictions. In addition, HCovDock is also validated in virtual screening against 10 receptors of three proteins. HCovDock is computationally efficient and the average running time for docking a ligand is only 5 min with as fast as 1 sec for ligands with one rotatable bond and about 18 min for ligands with 23 rotational bonds. HCovDock can be freely assessed at http://huanglab.phys.hust.edu.cn/hcovdock/.

Asunto(s)

RESUMEN

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common enzyme deficiency disorder affecting over 400 million individuals worldwide. G6PD protects red blood cells (RBC) from the harmful effects of oxidative substances. There are more than 400 G6PD mutations, of which 186 variants have shown to be linked to G6PD deficiency by decreasing the activity or stability of the enzyme. Different variants manifest different clinical phenotypes which complicate comprehending the mechanism of the disease. In order to carry out computational approaches to elucidate the structural changes of different G6PD variants that are common to the Asian population, a complete G6PD monomer-ligand complex was constructed using AutoDock 4.2, and the molecular dynamics simulation package GROMACS 4.6.7 was used to study the protein dynamics. The G410D and V291M variants were chosen to represent classes I and II respectively and were created by in silico site-directed mutagenesis. Results from the Root mean square deviation (RMSD), Root mean square fluctuation (RMSF) and Radius of gyration (Rg) analyses provided insights on the structure - function relationship for the variants. G410D indicated impaired dimerization and structural NADP binding while the impaired catalytic activity for V291M was indicated by a conformational change at its mutation site.

RESUMEN

Epidermal growth factor receptors are constitutively overexpressed in breast cancer cells, which in turn stimulate many downstream signaling pathways that are involved in many carcinogenic processes. This makes EGFR a striking target for cancer therapy. This study focuses on the EGFR kinase domain inactivation by novel synthesized indoline derivatives. The compounds used are N-(2-hydroxy-5-nitrophenyl (4'-methyl phenyl) methyl) indoline (HNPMI), alkylaminophenols - 2-((3,4-Dihydroquinolin-1(2H)-yl) (p-tolyl) methyl) phenol (THTMP) and 2-((1, 2, 3, 4-Tetrahydroquinolin-1-yl) (4 methoxyphenyl) methyl) phenol (THMPP). To get a clear insight into the molecular interaction of EGFR and the three compounds, we have used ADME/Tox prediction, Flexible docking analysis followed by MM/GB-SA, QM/MM analysis, E-pharmacophore mapping of the ligands and Molecular dynamic simulation of protein-ligand complexes. All three compounds showed good ADME/Tox properties obeying the rules of drug-likeliness and showed high human oral absorption. Molecular docking was performed with the compounds and EGFR using Glide Flexible docking mode. This showed that the HNPMI was best among the three compounds and had interactions with key residue Lys 721. The protein-ligand complexes were stable when simulated for 100 ns using Desmond software. The interactions were further substantiated using QM/MM analysis and MM-GB/SA analysis in which HNPMI was scored as the best molecule. All the analyses were carried out with a reference molecule-Gefitinib which is a known standard inhibitor of EGFR. Thus, the study elucidates the potential role of the indoline derivatives as an anti-cancer agent against breast cancer by effectively inhibiting EGFR.Communicated by Ramaswamy H. Sarma.

Asunto(s)

RESUMEN

RcdA is a helix-turn-helix (HTH) transcriptional regulator belonging to the TetR family. The protein regulates the transcription of curlin subunit gene D, the master regulator of biofilm formation. Moreover, it was predicted that it might be involved in the regulation of up to 27 different genes. However, an effector of RcdA and the environmental conditions which trigger RcdA action remain unknown. Herein, we report the first crystal structures of RcdA in complexes with ligands, trimethylamine N-oxide (TMAO) and tris(hydroxymethyl)aminomethane (Tris), which might serve as RcdA effectors. Based on these structures, the ligand-binding pocket of RcdA was characterized in detail. The conservation of the amino acid residues forming the ligand-binding cavity was analyzed and the comprehensive search for RcdA structural homologs was performed. This analysis indicated that RcdA is structurally similar to multidrug-binding TetR family members, however, its ligand-binding cavity differs significantly from the pockets of its structural homologs. The interaction of RcdA with TMAO and Tris indicates that the protein might be involved in alkaline stress response.

Asunto(s)

RESUMEN

BACKGROUND: Accurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein-ligand complex is ongoing. RESULTS: In this study, we proposed a deep-neural network model to improve the prediction accuracy of protein-ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein-ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets. CONCLUSIONS: We confirmed that an attention mechanism can capture the binding sites in a protein-ligand complex to improve prediction performance. Our code is available at https://github.com/Blue1993/BAPA .

Asunto(s)

RESUMEN

Molecular dynamics (MD) simulation is a powerful method of investigating the interaction between molecular species. Defining the mechanical properties and topologies for all components involved is critical. While parameters for proteins are well established, those for the wide range of ligands and substrates are not. Here we introduce a very useful service which is designed for small organic molecules. We describe a protocol to extend this tool to beyond its current size (200 atoms) and formal charge (2+ to 2-) limits.

Asunto(s)

RESUMEN

With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholinesterase show detectable activity; beyond this, 10 of 23 compounds have IC50 better than 50 µM. Without any medicinal chemistry optimization, the most potent hit has IC50 280 nM, corresponding to Ki of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

Asunto(s)

RESUMEN

Producing an accurate atomic model of biomolecule-ligand interactions from maps generated by cryoelectron microscopy (cryo-EM) often presents challenges inherent to the methodology and the dynamic nature of ligand binding. Here, we present GemSpot, an automated pipeline of computational chemistry methods that take into account EM map potentials, quantum mechanics energy calculations, and water molecule site prediction to generate candidate poses and provide a measure of the degree of confidence. The pipeline is validated through several published cryo-EM structures of complexes in different resolution ranges and various types of ligands. In all cases, at least one identified pose produced both excellent interactions with the target and agreement with the map. GemSpot will be valuable for the robust identification of ligand poses and drug discovery efforts through cryo-EM.

Asunto(s)