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.

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.

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.

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.

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.

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.

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.

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.

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.

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.