Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling.

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.

Computer-Aided Drug Design Using the Fragment Molecular Orbital Method: Current Status and Future Applications for SBDD.

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.

DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening.

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.

A glance at structural biology in advancing rice blast fungus research.

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.

Discovery of the selective and nanomolar inhibitor of DPP-4 more potent than sitagliptin by structure-guided rational design.

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.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.

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.

New potent EV-A71 antivirals targeting capsid.

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.

Advances in structure-based drug design targeting membrane protein markers in prostate cancer.

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.

Substrate-Based Ligand Design for Phenazine Biosynthesis Enzyme PhzF.

The phenazine pyocyanin is an important virulence factor of the pathogen Pseudomonas aeruginosa, which is on the WHO list of antibiotic resistant "priority pathogens". In this study the isomerase PhzF, a key bacterial enzyme of the pyocyanin biosynthetic pathway, was investigated as a pathoblocker target. The aim of the pathoblocker strategy is to reduce the virulence of the pathogen without killing it, thus preventing the rapid development of resistance. Based on crystal structures of PhzF, derivatives of the inhibitor 3-hydroxyanthranilic acid were designed. Co-crystal structures of the synthesized derivatives with PhzF revealed spacial limitations of the binding pocket of PhzF in the closed conformation. In contrast, ligands aligned to the open conformation of PhzF provided more room for structural modifications. The intrinsic fluorescence of small 3-hydroxyanthranilic acid derivatives enabled direct affinity determinations using FRET assays. The analysis of structure-activity relationships showed that the carboxylic acid moiety is essential for binding to the target enzyme. The results of this study provide fundamental structural insights that will be useful for the design of PhzF-inhibitors.

Leveraging binding pose metadynamics to optimise target fishing predictions for three diverse ligands and their true targets.

Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50-100, 100-150, and 250-300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50-100, 100-150, 150-200, and 200-250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure-based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts.

Discovery and characterization of small-molecule TGR5 ligands with agonistic activity.

The Takeda G protein-coupled receptor 5 (TGR5) is activated endogenously by primary and secondary bile acids. This receptor is considered a candidate target for addressing inflammatory and metabolic disorders. We have targeted TGR5 with structure-based methods for ligand finding using the recently solved experimental structures, as well as structures obtained from molecular dynamics simulations. Through addressing the orthosteric as well as a putative allosteric site, we identified agonists and positive allosteric modulators. While the predicted binding locations were not in line with their efficacy, our work contributes activating small-molecule ligands that we have thoroughly characterized in vitro.

Caught between a ROCK and a hard place: current challenges in structure-based drug design.

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.

An update on lipophilic efficiency as an important metric in drug design.

INTRODUCTION: Lipophilic efficiency (LipE) and lipophilic metabolic efficiency (LipMetE) are valuable tools that can be utilized as part of a multiparameter optimization process to advance a hit to a clinical quality compound. AREAS COVERED: This review covers recent, effective use cases of LipE and LipMetE that have been published in the literature over the past 5 years. These use cases resulted in the delivery of high-quality molecules that were brought forward to in vivo work and/or to clinical studies. The authors discuss best-practices for using LipE and LipMetE analysis, combined with lipophilicity-focused compound design strategies, to increase the speed and effectiveness of the hit to clinical quality compound optimization process. EXPERT OPINION: It has become well established that increasing LipE and LipMetE within a series of analogs facilitates the improvement of broad selectivity, clearance, solubility, and permeability and, through this optimization, also facilitates the achievement of desired pharmacokinetic properties, efficacy, and tolerability. Within this article, we discuss lipophilic efficiency-focused optimization as a tool to yield high-quality potential clinical candidates. It is suggested that LipE/LipMetE-focused optimization can facilitate and accelerate the drug-discovery process.

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: A Structural Biology Perspective.

G protein-coupled receptors (GPCRs) play a key role in the transduction of extracellular signals to cells and regulation of many biological processes, which makes these membrane proteins one of the most important targets for pharmacological agents. A significant increase in the number of resolved atomic structures of GPCRs has opened the possibility of developing pharmaceuticals targeting these receptors via structure-based drug design (SBDD). SBDD employs information on the structure of receptor-ligand complexes to search for selective ligands without the need for an extensive high-throughput experimental ligand screening and can significantly expand the chemical space for ligand search. In this review, we describe the process of deciphering GPCR structures using X-ray diffraction analysis and cryoelectron microscopy as an important stage in the rational design of drugs targeting this receptor class. Our main goal was to present modern developments and key features of experimental methods used in SBDD of GPCR-targeting agents to a wide range of specialists.

Targeting shelterin proteins for cancer therapy.

As a global health challenge, cancer prompts continuous exploration for innovative therapies that are also based on new targets. One promising avenue is targeting the shelterin protein complex, a safeguard for telomeres crucial in preventing DNA damage. The role of shelterin in modulating ataxia-telangiectasia mutated (ATM) and ataxia-telangiectasia and Rad3-related (ATR) kinases, key players in the DNA damage response (DDR), establishes its significance in cancer cells. Disrupting these defence mechanisms of shelterins, especially in cancer cells, renders telomeres vulnerable, potentially leading to genomic instability and hindering cancer cell survival. In this review, we outline recent approaches exploring shelterins as potential anticancer targets, highlighting the prospect of developing selective molecules to exploit telomere vulnerabilities toward new innovative cancer treatments.

Design, synthesis and biological evaluation of 5-amino-1H-pyrazole-4-carboxamide derivatives as pan-FGFR covalent inhibitors.

The aberrant activation of FGFRs plays a critical role in various cancers, leading to the development of several FGFR inhibitors in clinic. However, the emergence of drug resistance, primarily due to gatekeeper mutations in FGFRs, has limited their clinical efficacy. To address the unmet medical need, a series of 5-amino-1H-pyrazole-4-carboxamide derivatives were designed and synthesized as novel pan-FGFR covalent inhibitors targeting both wild-type and the gatekeeper mutants. The representative compound 10h demonstrated nanomolar activities against FGFR1, FGFR2, FGFR3 and FGFR2 V564F gatekeeper mutant in biochemical assays (IC50 = 46, 41, 99, and 62 nM). Moreover, 10h also strongly suppressed the proliferation of NCI-H520 lung cancer cells, SNU-16 and KATO III gastric cancer cells with IC50 values of 19, 59, and 73 nM, respectively. Further X-ray co-crystal structure revealed that 10h irreversibly binds to FGFR1. The study provides a new promising point for anticancer drug development medicated by FGFRs.

Structure-Based Design of Ultrapotent Tricyclic Ligands for FK506-Binding Proteins.

Access to small, rigid, and sp3-rich molecules is a major limitation in the drug discovery for challenging protein targets. FK506-binding proteins hold high potential as drug targets or enablers of molecular glues but are fastidious in the chemotypes accepted as ligands. We here report an enantioselective synthesis of a highly rigidified pipecolate-mimicking tricyclic scaffold that precisely positions functional groups for interacting with FKBPs. This was enabled by a 14-step gram-scale synthesis featuring anodic oxidation, stereospecific vinylation, and N-acyl iminium cyclization. Structure-based optimization resulted in the discovery of FKBP inhibitors with picomolar biochemical and subnanomolar cellular activity that represent the most potent FKBP ligands known to date.

ADAR Family Proteins: A Structural Review.

This review aims to highlight the structures of ADAR proteins that have been crucial in the discernment of their functions and are relevant to future therapeutic development. ADAR proteins can correct or diversify genetic information, underscoring their pivotal contribution to protein diversity and the sophistication of neuronal networks. ADAR proteins have numerous functions in RNA editing independent roles and through the mechanisms of A-I RNA editing that continue to be revealed. Provided is a detailed examination of the ADAR family members-ADAR1, ADAR2, and ADAR3-each characterized by distinct isoforms that offer both structural diversity and functional variability, significantly affecting RNA editing mechanisms and exhibiting tissue-specific regulatory patterns, highlighting their shared features, such as double-stranded RNA binding domains (dsRBD) and a catalytic deaminase domain (CDD). Moreover, it explores ADARs' extensive roles in immunity, RNA interference, and disease modulation, demonstrating their ambivalent nature in both the advancement and inhibition of diseases. Through this comprehensive analysis, the review seeks to underline the potential of targeting ADAR proteins in therapeutic strategies, urging continued investigation into their biological mechanisms and health implications.

In Silico Search for Drug Candidates Targeting the PAX8-PPARγ Fusion Protein in Thyroid Cancer.

The PAX8/PPARγ rearrangement, producing the PAX8-PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble-docking-based compound screening. Specifically, we investigated the pocket structure that should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand-binding pocket on the PPARγ side of the PPFP is similar to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to the PPFP for 97 drug or drug-like compounds registered in the DrugBank database with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics Poisson-Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

Geometric deep learning methods and applications in 3D structure-based drug design.

3D structure-based drug design (SBDD) is considered a challenging and rational way for innovative drug discovery. Geometric deep learning is a promising approach that solves the accurate model training of 3D SBDD through building neural network models to learn non-Euclidean data, such as 3D molecular graphs and manifold data. Here, we summarize geometric deep learning methods and applications that contain 3D molecular representations, equivariant graph neural networks (EGNNs), and six generative model methods [diffusion model, flow-based model, generative adversarial networks (GANs), variational autoencoder (VAE), autoregressive models, and energy-based models]. Our review provides insights into geometric deep learning methods and advanced applications of 3D SBDD that will be of relevance for the drug discovery community.