RESUMEN

Vacuolar-type H+-ATPases (V-ATPases) play a crucial role in the life cycle of agricultural pests and represent a promising target for the development of novel insecticides. In this study, S18, a derivative of vanillin acquired from Specs database using a structure-based virtual screening methodology, was first identified as a V-ATPase inhibitor. It binds to subunit A of the enzyme with a Kd of 1 nM and exhibits insecticidal activity against M. separata. Subsequently, using S18 as the lead compound, a new series of vanillin derivatives were rationally designed and efficiently synthesized. and their biological activities were assessed. Among them, compound 3b-03 showed the strongest insecticidal activity against M. separata by effectively targeting the V-ATPase subunit A with Kd of 0.803 µM. Isothermal titration calorimetric measurements and docking results provided insights into its interaction with subunit A of V-ATPase, which could facilitate future research aimed at the development of novel chemical insecticides.

RESUMEN

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

RESUMEN

Various therapeutic targets and approaches are commonly employed in the management of Type 2 Diabetes. These encompass diverse groups of drugs that target different mechanisms involved in glucose regulation. Inhibition of the DPP-4 enzyme has been proven an excellent target for antidiabetic drug design. Our previous work on discovering multitarget antidiabetic drugs led to the identification of a gallic acid-thiazolidinedione hybrid as a potent DPP4 inhibitor (IC50 = 36 nM). In current research, our efforts resulted in a new dihydropyrimidine-based scaffold with enhanced DPP4 inhibition potential. After virtual evaluation, the designed molecules with excellent interaction patterns and binding energy values were synthesized in the wet laboratory. The inhibition potential of synthesized compounds was assessed against the DPP-4 enzyme. Compound 46 with single digit IC50 value 2 nM exhibited 4-fold and 18-fold higher activity than Sitagliptin and our previously reported hybrid respectively. Moreover, compounds 46, 47 and 50 have shown manyfold selectivity against DPP8 and DPP9. Further pretreatment with compounds 43, 45-47 and 50 (at doses of 10 and 20 mg/kg) in OGTT conducted on rats resulted in a significant decrease in the serum glucose levels compared to the control group. In the long-term STZ-induced diabetic rats, tested compound 50 performed similarly to the reference drug. Molecular dynamics simulations and in-silico molecular docking studies were employed to elucidate the time-dependent interactions of inhibitors within the active sites of DPP4. The compounds examined in this work might serve as a possible lead in the development of effective diabetic mellitus treatments.

RESUMEN

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.

Asunto(s)

RESUMEN

The filamentous fungus Magnaporthe oryzae is widely recognized as a notorious plant pathogen responsible for causing rice blasts. With rapid advancements in molecular biology technologies, numerous regulatory mechanisms have been thoroughly investigated. However, most recent studies have predominantly focused on infection-related pathways or host defence mechanisms, which may be insufficient for developing novel structure-based prevention strategies. A substantial body of literature has utilized cryo-electron microscopy and X-ray diffraction to explore the relationships between functional components, shedding light on the identification of potential drug targets. Owing to the complexity of protein extraction and stochastic nature of crystallization, obtaining high-quality structures remains a significant challenge for the scientific community. Emerging computational tools such as AlphaFold for structural prediction, docking for interaction analysis, and molecular dynamics simulations to replicate in vivo conditions provide novel avenues for overcoming these challenges. In this review, we aim to consolidate the structural biological advancements in M. oryzae, drawing upon mature experimental experiences from other species such as Saccharomyces cerevisiae and mammals. We aim to explore the potential of protein construction to address the invasion and proliferation of M. oryzae, with the goal of identifying new drug targets and designing small-molecule compounds to manage this disease.

Asunto(s)

RESUMEN

Owing to the increasing use of computers, computer-aided drug design (CADD) has become an essential component of drug discovery research. In structure-based drug design (SBDD), including inhibitor design and in silico screening of drug target molecules, concordance with wet experimental data is important to provide insights on unique perspectives derived from calculations. Fragment molecular orbital (FMO) method is a quantum chemical method that facilitates precise energy calculations. Fragmentation method makes it possible to apply the quantum chemical method to biological macromolecules for energy calculation based on the electron behavior. Furthermore, interaction energies calculated on a residue-by-residue basis via fragmentation aid in the analysis of interactions between the target and ligand molecule residues and molecular design. In this review, we outline the recent developments in SBDD and FMO methods and highlight the prospects of developing machine learning approaches for large computational data using the FMO method.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

The inhibition of intracellular protein-protein interactions is challenging, in particular, when involved interfaces lack pronounced cavities. The transcriptional co-activator protein and oncogene ß­catenin is a prime example of such a challenging target. Despite extensive targeting efforts, available high-affinity binders comprise only large molecular weight Inhibitors. This hampers the further development of therapeutically useful compounds. Herein, we report the design of a considerably smaller peptidomimetic scaffold derived from the α-helical ß­catenin-binding motif of Axin. Sequence maturation and bicyclization provided a stitched peptide with an unprecedented crosslink architecture. The binding mode and site were confirmed by a crystal structure. Further derivatization yielded a ß-catenin inhibitor with single-digit micromolar activity in a cell-based assay. This study sheds a light on how to design helix mimetics with reduced molecular weight thereby improving their biological activity.

RESUMEN

One of the central issues in the understanding of early cellular evolution is the characterisation of the cenancestor. This includes the description of the chemical nature of its genome. The disagreements on this question comprise several proposals, including the possibility that AlkB-mediated methylation repair of alkylated RNA molecules may be interpreted as evidence of a cenancestral RNA genome. We present here an evolutionary analysis of the cupin-like protein superfamily based on tertiary structure-based phylogenies that includes the oxygen-dependent AlkB and its homologs. Our results suggest that the repair of methylated RNA molecules is the outcome of the enzyme substrate ambiguity, and doesn´t necessarily indicates that the last common ancestor was endowed with an RNA genome.

RESUMEN

In search of new opportunities to develop Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors that have selectivity over the off-target human PDE4 (hPDE4), different stages of a fragment-growing campaign were studied using a variety of biochemical, structural, thermodynamic, and kinetic binding assays. Remarkable differences in binding kinetics were identified and this kinetic selectivity was explored with computational methods, including molecular dynamics and interaction fingerprint analyses. These studies indicate that a key hydrogen bond between GlnQ.50 and the inhibitors is exposed to a water channel in TbrPDEB1, leading to fast unbinding. This water channel is not present in hPDE4, leading to inhibitors with a longer residence time. The computer-aided drug design protocols were applied to a recently disclosed TbrPDEB1 inhibitor with a different scaffold and our results confirm that shielding this key hydrogen bond through disruption of the water channel represents a viable design strategy to develop more selective inhibitors of TbrPDEB1. Our work shows how computational protocols can be used to understand the contribution of solvent dynamics to inhibitor binding, and our results can be applied in the design of selective inhibitors for homologous PDEs found in related parasites.

RESUMEN

Viruses have been responsible for many epidemics and pandemics that have impacted human life globally. The COVID-19 pandemic highlighted both our vulnerability to viral outbreaks, as well as the mobilization of the scientific community to come together to combat the unprecedented threat to humanity. Cryo-electron microscopy (cryo-EM) played a central role in our understanding of SARS-CoV-2 during the pandemic and continues to inform about this evolving pathogen. Cryo-EM with its two popular imaging modalities, single particle analysis (SPA) and cryo-electron tomography (cryo-ET), has contributed immensely to understanding the structure of viruses and interactions that define their life cycles and pathogenicity. Here, we review how cryo-EM has informed our understanding of three distinct viruses, of which two - HIV-1 and SARS-CoV-2 infect humans, and the third, bacteriophages, infect bacteria. For HIV-1 and SARS-CoV-2 our focus is on the surface glycoproteins that are responsible for mediating host receptor binding, and host and cell membrane fusion, while for bacteriophages, we review their structure, capsid maturation, attachment to the bacterial cell surface and infection initiation mechanism.

RESUMEN

G-quadruplex (G4) sequences, which can fold into higher-order G4 structures, are abundant in the human genome and are over-represented in the promoter regions of many genes involved in human cancer initiation, progression, and metastasis. They are plausible targets for G4-binding small molecules, which would, in the case of promoter G4s, result in the transcriptional downregulation of these genes. However, structural information is currently available on only a very small number of G4s and their ligand complexes. This limitation, coupled with the currently restricted information on the G4-containing genes involved in most complex human cancers, has led to the development of a phenotypic-led approach to G4 ligand drug discovery. This approach was illustrated by the discovery of several generations of tri- and tetra-substituted naphthalene diimide (ND) ligands that were found to show potent growth inhibition in pancreatic cancer cell lines and are active in in vivo models for this hard-to-treat disease. The cycles of discovery have culminated in a highly potent tetra-substituted ND derivative, QN-302, which is currently being evaluated in a Phase 1 clinical trial. The major genes whose expression has been down-regulated by QN-302 are presented here: all contain G4 propensity and have been found to be up-regulated in human pancreatic cancer. Some of these genes are also upregulated in other human cancers, supporting the hypothesis that QN-302 is a pan-G4 drug of potential utility beyond pancreatic cancer.

Asunto(s)

RESUMEN

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies.

RESUMEN

Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large databases is possible with cloud-scale computing. However, rapid docking necessitates compromises in scoring, often leading to poor enrichment and an abundance of false positives in docking results. This work describes a new scoring function composed of two parts - a knowledge-based component that predicts the probability of a particular atom type being in a particular receptor environment, and a tunable weight matrix that converts the probability predictions into a dimensionless score suitable for virtual screening enrichment. This score, the FitScore, represents the compatibility between the ligand and the binding site and is capable of a high degree of enrichment across standardized docking test sets.

Asunto(s)

RESUMEN

The enterovirus is a genus of single-stranded, highly diverse positive-sense RNA viruses, including Human Enterovirus A-D and Human Rhinovirus A-C species. They are responsible for numerous diseases and some infections can progress to life-threatening complications, particularly in children or immunocompromised patients. To date, there is no treatment against enteroviruses on the market, except for polioviruses (vaccine) and EV-A71 (vaccine in China). Following a decrease in enterovirus infections during and shortly after the (SARS-Cov2) lockdown, enterovirus outbreaks were once again detected, notably in young children. This reemergence highlights on the need to develop broad-spectrum treatment against enteroviruses. Over the last year, our research team has identified a new class of small-molecule inhibitors showing anti-EV activity. Targeting the well-known hydrophobic pocket in the viral capsid, these compounds show micromolar activity against EV-A71 and a high selectivity index (SI) (5h: EC50, MRC-5 = 0.57 µM, CC50, MRC-5 >20 µM, SI > 35; EC50, RD = 4.38 µM, CC50, RD > 40 µM, SI > 9; 6c: EC50, MRC-5 = 0.29 µM, CC50, MRC-5 >20 µM, SI > 69; EC50, RD = 1.66 µM, CC50, RD > 40 µM, SI > 24; Reference: Vapendavir EC50, MRC-5 = 0.36 µM, CC50, MRC-5 > 20 µM, EC50, RD = 0.53 µM, CC50, RD > 40 µM, SI > 63). The binding mode of these compounds in complex with enterovirus capsids was analyzed and showed a series of conserved interactions. Consequently, 6c and its derivatives are promising candidates for the treatment of enterovirus infections.

Asunto(s)

RESUMEN

In consideration of several serious side effects induced by the classical AF-2 involved "lock" mechanism, recently disclosed PPARγ-Ser273 phosphorylation mode of action has become an alternative and mainstream mechanism for currently PPARγ-based drug discovery and development with an improved therapeutic index. In this study, by virtue of structure-based virtual high throughput screening (SB-VHTS), structurally chemical optimization by targeting the inhibition of the PPARγ-Ser273 phosphorylation as well as in vitro biological evaluation, which led to the final identification of a chrysin-based potential hit (YGT-31) as a novel selective PPARγ modulator with potent binding affinity and partial agonism. Further in vivo evaluation demonstrated that YGT-31 possessed potent glucose-lowering and relieved hepatic steatosis effects without involving the TZD-associated side effects. Mechanistically, YGT-31 presented such desired therapeutic index, mainly because it effectively inhibited the CDK5-mediated PPARγ-Ser273 phosphorylation, selectively elevated the level of insulin sensitivity-related Glut4 and adiponectin but decreased the expression of insulin-resistance-associated genes PTP1B and SOCS3 as well as inflammation-linked genes IL-6, IL-1ß and TNFα. Finally, the molecular docking study was also conducted to uncover an interesting hydrogen-bonding network of YGT-31 with PPARγ-Ser273 phosphorylation-related key residues Ser342 and Glu343, which not only gave a clear verification for our targeting modification but also provided a proof of concept for the abovementioned molecular mechanism.

Asunto(s)

RESUMEN

BACKGROUND: Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth. OBJECTIVES/AIMS: This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model. METHODS: This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP. RESULTS: The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis. CONCLUSION: Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

Asunto(s)

RESUMEN

Prostate cancer (PCa) is one of the leading cancers in men and the lack of suitable biomarkers or their modulators results in poor prognosis. Membrane proteins (MPs) have a crucial role in the development and progression of PCa and can be attractive therapeutic targets. However, experimental limitations in targeting MPs hinder effective biomarker and inhibitor discovery. To overcome this barrier, computational methods can yield structural insights and screen large libraries of compounds, accelerating lead identification and optimization. In this review, we examine current breakthroughs in computer-aided drug design (CADD), with emphasis on structure-based approaches targeting the most relevant membrane-bound PCa biomarkers.

Asunto(s)

RESUMEN

BACKGROUND: Human neutrophil elastase (HNE) is an important contributor to lung diseases such as acute lung injury (ALI) or acute respiratory distress syndrome. Therefore, this study aimed to identify natural HNE inhibitors with anti-inflammatory activity through machine learning algorithms, in vitro assays, molecular dynamic simulation, and an in vivo ALI assay. METHODS: Based on the optimized Discovery Studio two-dimensional molecular descriptors, combined with different molecular fingerprints, six machine learning models were established using the Naïve Bayesian (NB) method to identify HNE inhibitors. Subsequently, the optimal model was utilized to screen 6925 drug-like compounds obtained from the Traditional Chinese Medicine Systems Pharmacy Database and Analysis Platform (TCMSP), followed by ADMET analysis. Finally, 10 compounds with reported anti-inflammatory activity were selected to determine their inhibitory activities against HNE in vitro, and the compounds with the best activity were selected for a 100 ns molecular dynamics simulation and its anti-inflammatory effect was evaluated using Poly (I:C)-induced ALI model. RESULTS: The evaluation of the in vitro HNE inhibition efficiency of the 10 selected compounds showed that the flavonoid tricetin had the strongest inhibitory effect on HNE. The molecular dynamics simulation indicated that the binding of tricetin to HNE was relatively stable throughout the simulation. Importantly, in vivo experiments indicated that tricetin treatment substantially improved the Poly (I:C)-induced ALI. CONCLUSION: The proposed NB model was proved valuable for exploring novel HNE inhibitors, and natural tricetin was screened out as a novel HNE inhibitor, which was confirmed by in vitro and in vivo assays for its inhibitory activities.

Asunto(s)

RESUMEN

A key prerequisite for the successful application of protein crystallography in drug discovery is to establish a robust crystallization system for a new drug-target protein fast enough to deliver crystal structures when the first inhibitors have been identified in the hit-finding campaign or, at the latest, in the subsequent hit-to-lead process. The first crucial step towards generating well folded proteins with a high likelihood of crystallizing is the identification of suitable truncation variants of the target protein. In some cases an optimal length variant alone is not sufficient to support crystallization and additional surface mutations need to be introduced to obtain suitable crystals. In this contribution, four case studies are presented in which rationally designed surface modifications were key to establishing crystallization conditions for the target proteins (the protein kinases Aurora-C, IRAK4 and BUB1, and the KRAS-SOS1 complex). The design process which led to well diffracting crystals is described and the crystal packing is analysed to understand retrospectively how the specific surface mutations promoted successful crystallization. The presented design approaches are routinely used in our team to support the establishment of robust crystallization systems which enable structure-guided inhibitor optimization for hit-to-lead and lead-optimization projects in pharmaceutical research.

Asunto(s)