Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

Identification of isoxazole-based TRPA1 inhibitors with analgesic effects in vivo

The transient receptor potential ankyrin 1 (TRPA1) channel has been extensively studied as a potential therapeutic target for the treatment of different pain types, with better efficacy and safety profiles compared to current therapies. Because TRPA1 is implicated in different pathophysiological processes, selective antagonists of this channel could provide therapeutic benefits beyond pain relief. In this study, we report the design and synthesis of a novel series of carboxamide derivatives incorporating an isoxazole moiety, which were evaluated for their ability to inhibit TRPA1-mediated signalling. Among these, we identified the TRPA1 antagonists 12 and 13 displaying nanomolar potency in vitro and significant analgesic effects against the TRPA1 agonist, allyl isothiocyanate and in the formalin test in mice. Docking analyses were also conducted to explore the binding modes of the most representative compounds with the proposed pharmacological target.

Discovery and characterization of novel FAK inhibitors for breast cancer therapy via hybrid virtual screening, biological evaluation and molecular dynamics simulations

Focal adhesion kinase (FAK) is a critical drug target implicated in various disease pathways, including hematological malignancies and breast cancer. Therefore, identifying FAK inhibitors with novel scaffolds could offer new opportunities for developing effective therapeutic compounds. Herein, we disclosed the discovery of a new backbone inhibitor of FAK using an "internal" database, employing a structure-based high-transparency permeability virtual screening (HTVS) and a DeepDock algorithm based on geometric deep learning. Subsequently, molecular docking was conducted at different precisions to identify 10 compounds for further evaluation of biological activity. Ultimately, compound 4, a pyrimidin-4-amine derivative, demonstrated inhibitory activity against FAK and breast cancer cells, further supporting its potential as a FAK inhibitor. Moreover, molecular dynamics simulations were carried out to gain more detailed insights into the binding mechanism between compound 4 and FAK to guide subsequent structural optimization.

Light-mediated activation/deactivation control and in vitro ADME-Tox profiling of a donepezil-like Dual AChE/MAO-B Inhibitor

The possibility to control the effects of drugs in time and space represents an ideal condition for developing safer and more personalized therapies against different disorders. In this context, photopharmacology has paved the way for the use of light in the modulation of drugs activity. Our interest is directed to photo-switchable molecules, capable of interconverting between two different isoforms upon light irradiation. We recently reported 1, a donepezil-like compound based on 2-benzylidenindan-1-one structure, as a dual AChE and MAO-B inhibitor, which can be converted into the E- (active form) and Z- (about tenfold less active form) diastereoisomers by irradiating with UV-vis light. Aiming at identifying compounds with remarkable activity in physiological conditions, we herein report a fine characterization of 1 in PBS solutions. First, we evaluated its ability to act as a photoswitch comparing PBS solution with organic solvents (e.g. methanol), demonstrating that a wavelength in the UV range (330 nm) can convert the E- into the Z-diastereoisomer, while the use of a visible light (400 nm) allows the interconversion from Z to E in both media. Along with its photoinducible behavior, we investigated the passive diffusion across cellular membrane with PAMPA experiments, plasma and microsomal stability, and binding to plasma proteins. Interestingly, the results of such studies suggested that 1 could persist in the blood circulation for a long time, which is desirable for application in photopharmacological therapies. Cytotoxicity studies highlighted the potential of our prototypic compound as a lead photodrug against neurodegenerative disorders, deserving to advance in molecular optimization studies and further in vitro and in vivo characterization.

Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations

We present Moldrug, a computational tool for accelerating the hit-to-lead phase in structure-based drug design. Moldrug explores the chemical space using structural modifications suggested by the CReM library and by optimizing an adaptable fitness function with a genetic algorithm. Moldrug is complemented by Moldrug-Dashboard, a cross-platform and user-friendly graphical interface tailored for the analysis of Moldrug simulations. To illustrate Moldrug, we designed new potential inhibitors targeting the main protease (MPro) of SARS-CoV-2 by optimizing a consensus fitness function that balances binding affinity, drug-likeness, and synthetic accessibility. The designed molecules exhibited high chemical diversity. A subset of the designed molecules were ranked using MM/GBSA and alchemical binding free energy calculations, revealing predicted affinities as low as - 10 kcal mol - 1 . Moldrug is distributed as a Python package under the Apache 2.0 license. It offers pre-configured multi-parameter fitness functions for molecular design, while being highly adaptable for integrating functionalities from external software. Documentation and tutorials are available at https://moldrug.rtfd.io .

Structural Determinants of Buprenorphine Partial Agonism at the μ-Opioid Receptor

The μ-opioid receptor (μOR) is a class A G Protein-Coupled Receptor (GPCR) targeted by natural and synthetic ligands to provide analgesia to patients with pain of various etiologies. Available opioid medications present several unwanted side effects, stressing the need for safer pain therapies. Despite the attractive proposal that biasing μOR signaling toward G protein pathways would lead to fewer side effects, recent studies indicate that low-efficacy opioid drugs, such as buprenorphine, may represent a safer alternative. In the present work, we combine molecular docking, microsecond-time scale molecular dynamics (MD) simulations, and metadynamics to investigate the conformational dynamics of the μOR bound to morphine or buprenorphine. Our objective was to determine structural aspects associated with the unique pharmacological effects caused by the latter, taking morphine as a reference. MD simulations identified a salt bridge with D1493.32 as crucial for stabilizing both ligands into the μOR orthosteric site, with this interaction being weaker in buprenorphine. The morphinan-scaffold of both ligands shared contacts with transmembrane (TM) helix residues of the receptor, including TM3, TM5, TM6, and TM7. Conversely, while morphine showed stronger interactions with a few TM3 residues, additional chemical groups of buprenorphine showed stronger interactions with TM2, extracellular loop 2 (ECL2), and TM7 residues. We also observed distinct TM arrangements induced by these ligands, with buprenorphine causing an extracellular outward movement of TM7 and morphine provoking intracellular inward movements of TM5 and TM7 of the receptor. In addition, we found that buprenorphine tends to explore deeper regions in the μOR orthosteric site, further supported by funnel-metadynamics, resulting in diverse side chain orientations of W2956.48. Metadynamics also unveiled distinct intermediate states for morphine and buprenorphine, with the latter accessing a secondary binding site associated with partial μOR agonists. Our results indicate that the weakened salt bridge of buprenorphine with D1493.32, along with the strong TM7 interaction through its cyclopropyl group, may explain its low efficacy and consequent partial μOR agonism. Furthermore, ECL2 interactions may contribute to explaining the biased agonism of buprenorphine, a common feature shared with other opioid modulators with similar functional effects. Our study sheds light on the complex pharmacology of buprenorphine, identifying structural aspects associated with its partial and biased μOR agonism. These results can provide valuable information for the design of new effective and safer opioid drugs.

Vina-CUDA: An Efficient Program with in-Depth Utilization of GPU to Accelerate Molecular Docking

As a mainstream technology in modern drug discovery, molecular docking methodologies enable precise and efficient identification of lead compounds within large chemical repositories to improve drug development efficiency and reduce costs. The exponential growth of chemical databases has substantially expanded drug discovery resources while improving the identification rates of true positives in lead compounds. However, this rapid expansion poses significant challenges for existing docking tools to efficiently screen lead compounds from these massive chemical libraries. In this study, we proposed Vina-CUDA, which leverages GPU hardware features to optimize and accelerate the core algorithm of the popular tool AutoDock Vina at three aspects, computational capability, memory access, and resource utilization, significantly improving docking efficiency. A hybrid parallel optimization strategy integrating task and computational parallelism was implemented, accompanied by systematic code and data structure optimization, to maximize GPU resource utilization and enhance computational efficiency. Building upon this, we developed its derivatives, QuickVina2-CUDA and QuickVina-W-CUDA, as well as a user-friendly multi-GPU docking framework to utilize multi-GPU resources to accelerate large-scale virtual screening tasks. The performance and docking accuracy of Vina-CUDA and its derivatives were evaluated under five chemical databases. Results showed that, compared to baseline programs, Vina-CUDA with RILC-BFGS optimization algorithm achieved average and maximum accelerations of 3.71× and 6.89× across five databases, while QuickVina2-CUDA and QuickVina-W-CUDA achieved average speedups of 6.19× and 1.46×, respectively, without compromising docking accuracy. Furthermore, Vina-CUDA and its derivatives demonstrated comparable performance to baseline docking programs in docking, scoring, and ranking power, with excellent scalability and portability.

Identifying Potent Compounds Using Pairwise Consensus Methods

Molecular docking is a widely used method within the in silico compound screening process of modern drug discovery. The accuracy of this method for predicting high-affinity small-molecule binders for a target protein from a large chemical library can be substantially improved by combining individual docking tools for cross-validation. This traditional consensus strategy typically relies on averaging scores or ranks obtained from molecular docking, which are, however, vulnerable to false positives and thus exploit shortcomings from scoring functions. To overcome this remarkable weakness, we developed here the pairwise consensus score (PCS) algorithm. PCS integrates structural similarity information on ligand-receptor complexes to evaluate predicted conformations and penalize highly dissimilar docked poses. To demonstrate the versatility of PCS, we developed a consensus docking protocol for targeting G protein-coupled receptors (GPCRs) that are among the most important targets for modern drug discovery. In particular, we screened a large compound library for highly potent antagonism ligands to an important GPCR therapeutic target, the neurokinin 1 receptor, and found several compounds targeting the receptor with ten-picomolar activity. Notably, these highly active compounds show a totally different chemical structure from that of previously reported NK1 binders. This opens exciting opportunities to develop drugs with unique alternative pharmacological features and therapeutic value.

The contribution of de novo coding mutations to meningomyelocele

Meningomyelocele (also known as spina bifida) is considered to be a genetically complex disease resulting from a failure of the neural tube to close. Individuals with meningomyelocele display neuromotor disability and frequent hydrocephalus, requiring ventricular shunting. A few genes have been proposed to contribute to disease susceptibility, but beyond that it remains unexplained1. We postulated that de novo mutations under purifying selection contribute to the risk of developing meningomyelocele2. Here we recruited a cohort of 851 meningomyelocele trios who required shunting at birth and 732 control trios, and found that de novo likely gene disruption or damaging missense mutations occurred in approximately 22.3% of subjects, with 28% of such variants estimated to contribute to disease risk. The 187 genes with damaging de novo mutations collectively define networks including actin cytoskeleton and microtubule-based processes, Netrin-1 signalling and chromatin-modifying enzymes. Gene validation demonstrated partial or complete loss of function, impaired signalling and defective closure of the neural tube in Xenopus embryos. Our results indicate that de novo mutations make key contributions to meningomyelocele risk, and highlight critical pathways required for neural tube closure in human embryogenesis.

Insect Cytochrome P450 Database: An Integrated Resource of Genetic Diversity, Evolution and Function

Insects, the most numerous and diverse group of animal species on Earth, have important interactions with humans through providing resources, transmitting diseases and damaging agricultural cultivars. Cytochrome P450 monooxygenases (P450s) are one of the most important protein families in insects implicated in the endogenous metabolism and detoxification of xenobiotics, including allelochemicals, insecticides and environmental pollutants. To better understand the evolution and function of insect P450s and support the development and application of insecticides for pest control, an integrated bioinformatics platform is highly desirable. Here, we present the Insect Cytochrome P450 database (ICPD, http://www.insectp450.net/), which contains 66,477 P450s collected from public databases and predicted from the genomes of 682 insect species using a standardised bioinformatics protocol. Phylogenetic relationships between P450 genes are constructed for each species. The structures of all P450 proteins in the database are predicted using ESMFold, then visualised using WeView. Web services, such as BLAST, homogeneous modelling and molecular docking, are provided for determining the catalytic activities of P450 proteins. The ICPD will facilitate systematic investigations of the evolution and functions of the complete insect P450 complement, and represents a powerful tool for guiding insecticide design and application.

Gemini surfactant stabilized zein nanoparticles: Preparation, characterization, interaction mechanism, and antibacterial activity

To enhance the physicochemical properties of zein nanoparticles, zein complexes with two Gemini surfactants (12-3-12 and 12-4-12) were prepared using the anti-solvent method and investigated the physicochemical properties, formation mechanism and antibacterial activity. Results indicated that the optimal mass ratio between zein and Gemini surfactants was at 1:1, and the incorporation of Gemini surfactants significantly improved the surface properties of zein, reducing its surface hydrophobicity and surface tension, thereby enhancing its dispersion in aqueous media. Fluorescence spectroscopy and molecular docking experiments further elucidated the interaction mechanisms between zein and Gemini surfactant, revealing a spontaneous binding process, mainly driven by hydrophobic and hydrogen interaction, and a strong binding affinity of 12-4-12 with zein. Additionally, the zein/Gemini surfactant complexes exhibited significant antibacterial activity against Staphylococcus aureus, with the zein/12-4-12 complex showing particularly prominent inhibitory effects. Therefore, this research not only provides a theoretical foundation for the construction of Gemini surfactant stabilized zein nanoparticles but also points the way for the subsequent embedding of bacteriostatic agents to achieve synergistic antibacterial effects.

Trimethylamine N-Oxide (TMAO) Acts as Inhibitor of Endothelial Nitric Oxide Synthase (eNOS) and Hampers NO Production and Acetylcholine-Mediated Vasorelaxation in Rat Aortas

Trimethylamine N-oxide (TMAO) is an endogenous osmolyte produced by enzymatic reactions starting in the human gut, where microbiota release trimethylamine (TMA) from foods, and ending in the liver, where TMA is oxidized to TMAO by flavin-containing monooxygenase 3 (FMO3). While physiological concentrations of TMAO help proteins preserve their folding, high levels of this metabolite are harmful and promote oxidative stress, inflammation, and atherosclerosis. In humans, elevated levels of circulating TMAO predispose individuals to cardiovascular diseases and chronic kidney disease and increase mortality risk, especially in the elderly. How TMAO exerts its negative effects has been only partially elucidated. In hypertensive rats, the eNOS substrate L-arginine and Taurisolo®, a nutraceutical endowed with TMAO-reducing activity, act synergistically to reduce arterial blood pressure. Here, we investigate the molecular mechanisms underpinning this synergism and prove that TMAO, the target of Taurisolo®, acts as direct inhibitor of endothelial nitric oxide synthase (eNOS) and competes with L-arginine at its catalytic site, ultimately inhibiting NO production and acetylcholine (Ach)-induced relaxation in murine aortas.

Enhanced Binding Site Identification in Protein-Ligand Complexes with a Combined Blind Docking and Dipolar Electron Paramagnetic Resonance Approach

Understanding protein-drug complex structures is crucial for elucidating therapeutic mechanisms and side effects. Blind docking facilitates site identification but is hindered by computational complexity and imprecise scoring, causing ambiguity. Dipolar electron paramagnetic resonance (EPR) provides spin-spin distances but struggles to determine relative positions within complexes. We present a novel approach combining GPU-accelerated blind docking with EPR distance constraints to enhance binding site detection. Our algorithm uses a single EPR distance distribution to filter and validate docking results. Ligand poses from blind docking are clustered, filtered by expected distances, and refined through focused docking. To illustrate our approach, we investigated human serum albumin binding with porphyrin-based photosensitizers used in photodynamic therapy. Combining docking and EPR, we identified possible binding sites, demonstrating that EPR data significantly reduce possible configurations and provide experimentally validated information. This strategy produces a detailed map of photoligand binding sites, revealing that binding may occur away from standard albumin sites and often involves multiple locations. Furthermore, it overcomes key limitations of fluorescence-based methods, which are prone to misinterpretation in albumin studies due to non one-to-one donor-acceptor relationships. By resolving ambiguities in both blind docking and EPR, our framework provides a versatile platform for investigating EPR-active ligands.

Characterization of a 4'-O-rhamnosyltransferase and de novo biosynthesis of bioactive steroidal triglycosides from Paris polyphylla

Steroidal saponins in Paris polyphylla featuring complicated sugar chains exhibit notable biological activities, but their sugar-chain biosynthesis is still not fully understood. Here, we identified a 4'-O-rhamnosyltransferase (UGT73DY2) from P. polyphylla, which catalyzes the 4'-O-rhamnosylation of polyphyllins V and VI, producing dioscin and pennogenin 3-O-β-chacotrioside, respectively. UGT73DY2 exhibits strict substrate specificity toward steroidal diglycosides and UDP-rhamnose, and a new steroidal triglycoside can be synthesized through enzyme catalysis. A mutation library was generated based on semi-rational design, identifying three mutants, I358T, A342V, and A132T, which displayed approximately two-fold enhanced enzyme activity. Molecular dynamics simulations revealed that shortened distances between the 4'-OH group of the sugar acceptor and either the crucial residue H20 or the donor UDP-Rha contribute to the enhanced enzyme activity. Moreover, subcellular localization analysis of UGT73DY2 and other biosynthetic enzymes indicated that dioscin biosynthesis predominantly occurs in the endoplasmic reticulum of plant cells. By co-expressing 14 biosynthetic genes in Nicotiana benthamiana, optimizing HMGR subcellular localization and cytochrome P450 gene sets, and engineering UGT73DY2, we successfully established a dioscin biosynthesis system with a yield of 3.12 ± 0.11 μg/g dry weight. This study not only clarifies the 4'-O-rhamnosylation process in steroidal saponin biosynthesis but also presents an alternative approach for the production of steroidal saponins in P. polyphylla through synthetic biology and metabolic engineering.

Discovery of Novel DDR1 Inhibitors through a Hybrid Virtual Screening Pipeline, Biological Evaluation and Molecular Dynamics Simulations

Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy with limited therapeutic options for many patients. Discoidin domain receptor 1 (DDR1), a transmembrane tyrosine kinase receptor, has been implicated in AML progression and represents a promising therapeutic target. In this study, we employed a hybrid virtual screening workflow that integrates deep learning-based binding affinity predictions with molecular docking techniques to identify potential DDR1 inhibitors. A multistage screening process involving PSICHIC, KarmaDock, Vina-GPU, and similarity-based scoring was conducted, leading to the selection of seven candidate compounds. The biological evaluation identified Compound 4 as a novel DDR1 inhibitor, demonstrating significant DDR1 inhibitory activity with an IC50 of 46.16 nM and a 99.86% inhibition rate against Z-138 cells at 10 μM. Molecular dynamics simulations and binding free energy calculations further validated the stability and strong binding interactions of Compound 4 with DDR1. This study highlights the utility of combining deep learning models with traditional molecular docking techniques to accelerate the discovery of potent and selective DDR1 inhibitors. The identified compounds hold promise for further development as targeted therapies for AML.

Uncovering the Fluctuation of Peroxynitrite during Early Embryonic Development Using an Integrative Nanobeacon

Embryonic development is the beginning of life, and various kinds of bioactive molecules are implicated in this crucial process. Especially in its early stage, some important biochemical reactions regulating physiological balance may be resuscitated. Thus, revealing the dynamic changes of bioactive molecules during early embryonic development is crucial to the elucidation of biological phenomena. Peroxynitrite (ONOO-) is a typical signaling molecule in intercellular communication. However, up to now, no work has studied the fluctuation of ONOO- during early embryonic development due to its low content, especially in mammals. Herein, a polymeric nanobeacon that integrates an ONOO--responsive degradable scaffold and a fluorescence amplification module, named IFN, was designed to selectively sense embryonic ONOO- with high sensitivity. By virtue of the specific dye, ONOO- was sensitively detected in the range of 0-4.5 μM with a detection limit of 20.4 nM. From the attractive embryonic results, a sudden increase in ONOO- content after fertilization was observed in a mammalian model, while the level of ONOO- decreased slightly at the four-cell and eight-cell stages, finally reaching an equilibrium throughout the morula and blastocyst stages. This phenomenon is due to the resuscitation of ovotids, the activation of some life events by fertilization, and the subsequent establishment of physiological balance. This work not only suggests that ONOO- plays a positive role in normal embryonic development but also highlights the molecular events occurring at the initial phase of life. Furthermore it opens up new avenues for monitoring chemical changes during mammalian embryonic development.

RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning

RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale with large compound libraries and RNA targets. Machine learning offers a solution but remains underdeveloped for RNA due to limited data and practical evaluations. We introduce a data-driven VS pipeline tailored for RNA, utilizing coarse-grained 3D modeling, synthetic data augmentation, and RNA-specific self-supervision. Our model achieves a 10,000x speedup over docking while ranking active compounds in the top 2.8% on structurally distinct test sets. It is robust to binding site variations and successfully screens unseen RNA riboswitches in a 20,000-compound in-vitro microarray, with a mean enrichment factor of 2.93 at 1%. This marks the first experimentally validated success of structure-based deep learning for RNA VS.

Development of small-molecule fluorescent probes targeting neutrophils via N-formyl peptide receptors

N-Formyl peptide receptors (FPRs) are membrane receptors that are abundantly expressed in innate immune cells, including neutrophils and platelets, demonstrating potential new targets for immune system regulation and the treatment of inflammatory conditions. We report here the development and bio-physical validation of new FPR imaging agents as effective tools to track FPR distribution, localisation and functions, ultimately helping to establish FPR exact roles and functions in pathological and physiological conditions. The new series of probes feature a small molecule-based FPR address system conjugated to suitable fluorophores, resulting in highly specific FPR agents, including a partial agonist endowed with high affinity (i.e. low/sub-nanomolar potency) on FPR-transfected cells and human neutrophils. Preliminary imaging studies via multiphoton microscopy demonstrate that the probes enable the visualisation of FPRs in live cells, thus representing valid bio-imaging tools for the analysis of FPR-mediated signalling, such as the activation of neutrophils in inflammatory events.

Umbelliferone as an effective component of Rhodiola for protecting the cerebral microvascular endothelial barrier in cSVD

Objective

Rhodiola is a common Chinese herb in the treatment of cerebral small vessel disease (cSVD). Umbelliferone, one of the effective components of Rhodiola, can protect the endothelial barrier. But its mechanisms are still unclear. Therefore, this study is aimed to explore mechanisms of umbelliferone of an effective component of Rhodiola in protecting the cerebral microvascular endothelial barrier in cSVD.

Methods

Firstly, ETCM, SwissTargetPrediction and literatures were used to screen components and targets of Rhodiola. GeneCards was used to obtain targets of cSVD. STRING and Cytoscape were utilized for building the PPI and C-T network. Metascape was utilized to construct GO and KEGG enrichment analysis. Then, molecular docking was employed to evaluate the binding ability of the compounds for their respective target molecules. Ultimately, the endothelial cell damage caused by OGD was employed to explore the protective impact of umbelliferone, a bioactive constituent of Rhodiola, on the endothelial barrier. Endothelial cell leakage and migration assays were used to assess the permeability and migration ability of endothelial cells. IF and WB techniques were employed to ascertain the expression of endothelial tight junction protein. The major target proteins and related pathways were validated by WB.

Results

Six effective components and 106 potential targets were identified and 1885 targets of cSVD were obtained. Nine key targets were selected. GO and KEGG enrichment analysis suggested that effects of Rhodiola in cSVD were associated with PI3K-Akt, Ras, Rap1 and MAPK signal pathways. Molecular docking results showed good binding ability between 28 pairs of key proteins and compounds. Umbelliferone of an effective component of Rhodiola can protect tight junction proteins and improve the permeability and migration ability of endothelial cells damaged by OGD through MMP9, MMP2, CCND1, PTGS2 and PI3K-Akt, Ras, Rap1 signaling pathways.

Conclusion

Our study systematically clarified mechanisms of Rhodiola in treating cSVD by network pharmacology and molecular docking, characterized by its multi-component, multi-target and multi-pathway effects. This finding was validated through in vitro tests, which demonstrated that umbelliferone of an effective component in Rhodiola can protect the brain microvascular endothelial barrier. It provided valuable ideas and references for additional research.

Current Approaches and Strategies Applied in First-in-class Drug Discovery

First-in-class drug discovery (FICDD) offers novel therapies, new biological targets and mechanisms of action (MOAs) toward targeting various diseases and provides opportunities to understand unexplored biology and to target unmet diseases. Current screening approaches followed in FICDD for discovery of hit and lead molecules can be broadly categorized and discussed under phenotypic drug discovery (PDD) and target-based drug discovery (TBDD). Each category has been further classified and described with suitable examples from the literature outlining the current trends in screening approaches applied in small molecule drug discovery (SMDD). Similarly, recent applications of functional genomics, structural biology, artificial intelligence (AI), machine learning (ML), and other such advanced approaches in FICDD have also been highlighted in the article. Further, some of the current medicinal chemistry strategies applied during discovery of hits and optimization studies such as hit-to-lead (HTL) and lead optimization (LO) have been simultaneously overviewed in this article.

A Hybrid Energy-Based and AI-Based Screening Approach for the Discovery of Novel Inhibitors of AXL

AXL, part of the TAM receptor tyrosine kinase family, plays a significant role in the growth and survival of various tissues and tumors, making it a critical target for cancer therapy. This study introduces a novel high-throughput virtual screening (HTVS) methodology that merges an AI-enhanced graph neural network, PLANET, with a geometric deep learning algorithm, DeepDock. Using this approach, we identified potent AXL inhibitors from our database. Notably, compound 9, with an IC50 of 9.378 nM, showed excellent inhibitory activity, suggesting its potential as a candidate for further research. We also performed molecular dynamics simulations to explore the interactions between compound 9 and AXL, providing insights for future enhancements. This hybrid screening method proves effective in finding promising AXL inhibitors, and advancing the development of new cancer therapies.

Docking guidance with experimental ligand structural density improves docking pose prediction and virtual screening performance

Recent advances in structural biology have led to the publication of a wealth of high-resolution x-ray crystallography (XRC) and cryo-EM macromolecule structures, including many complexes with small molecules of interest for drug design. While it is common to incorporate information from the atomic coordinates of these complexes into docking (e.g., pharmacophore models or scaffold hopping), there are limited methods to directly leverage the underlying density information. This is desirable because it does not rely on the determination of relevant coordinates, which may require expert intervention, but instead interprets all density as indicative of regions to which a ligand may be bound. To do so, we have developed CryoXKit, a tool to incorporate experimental densities from either cryo-EM or XRC as a biasing potential on heavy atoms during docking. Using this structural density guidance with AutoDock-GPU, we found significant improvements in re-docking and cross-docking, important pose prediction tasks, compared with the unmodified AutoDock4 force field. Failures in cross-docking tasks are additionally reflective of changes in the positioning of pharmacophores in the site, suggesting it is a fundamental limitation of transferring information between complexes. We additionally found, against a set of targets selected from the LIT-PCBA dataset, that rescoring of these improved poses leads to better discriminatory power in virtual screenings for selected targets. Overall, CryoXKit provides a user-friendly method for improving docking performance with experimental data while requiring no a priori pharmacophore definition and at virtually no computational expense. Map-modification code available at: https://github.com/forlilab/CryoXKit.

ARF4-mediated intracellular transport as a broad-spectrum antiviral target

Host factors that are involved in modulating cellular vesicular trafficking of virus progeny could be potential antiviral drug targets. ADP-ribosylation factors (ARFs) are GTPases that regulate intracellular vesicular transport upon GTP binding. Here we demonstrate that genetic depletion of ARF4 suppresses viral infection by multiple pathogenic RNA viruses including Zika virus (ZIKV), influenza A virus (IAV) and SARS-CoV-2. Viral infection leads to ARF4 activation and virus production is rescued upon complementation with active ARF4, but not with inactive mutants. Mechanistically, ARF4 deletion disrupts translocation of virus progeny into the Golgi complex and redirects them for lysosomal degradation, thereby blocking virus release. More importantly, peptides targeting ARF4 show therapeutic efficacy against ZIKV and IAV challenge in mice by inhibiting ARF4 activation. Our findings highlight the role of ARF4 during viral infection and its potential as a broad-spectrum antiviral target for further development.

DrugDiff: small molecule diffusion model with flexible guidance towards molecular properties

With the cost/yield-ratio of drug development becoming increasingly unfavourable, recent work has explored machine learning to accelerate early stages of the development process. Given the current success of deep generative models across domains, we here investigated their application to the property-based proposal of new small molecules for drug development. Specifically, we trained a latent diffusion model-DrugDiff-paired with predictor guidance to generate novel compounds with a variety of desired molecular properties. The architecture was designed to be highly flexible and easily adaptable to future scenarios. Our experiments showed successful generation of unique, diverse and novel small molecules with targeted properties. The code is available at https://github.com/MarieOestreich/DrugDiff . SCIENTIFIC CONTRIBUTION: This work expands the use of generative modelling in the field of drug development from previously introduced models for proteins and RNA to the here presented application to small molecules. With small molecules making up the majority of drugs, but simultaneously being difficult to model due to their elaborate chemical rules, this work tackles a new level of difficulty in comparison to sequence-based molecule generation as is the case for proteins and RNA. Additionally, the demonstrated framework is highly flexible, allowing easy addition or removal of considered molecular properties without the need to retrain the model, making it highly adaptable to diverse research settings and it shows compelling performance for a wide variety of targeted molecular properties.

In Silico Identification of Potential Clovibactin-like Antibiotics Binding to Unique Cell Wall Precursors in Diverse Gram-Positive Bacterial Strains

The rise in multidrug-resistant bacteria highlights the critical need for novel antibiotics. This study explores clovibactin-like compounds as potential therapeutic agents targeting lipid II, a crucial component in bacterial cell wall synthesis, using in silico techniques. A total of 2624 clovibactin analogs were sourced from the PubChem database and screened using ProTox 3.0 software based on their ADME-Tox properties, prioritizing candidates with favorable pharmacokinetic profiles and minimal toxicity. Molecular docking protocols were then employed to assess the binding interactions of the selected compounds with lipid II. Our analysis identified Compound 22 as a particularly promising candidate, exhibiting strong binding affinity, stable complex formation, and high selectivity for the target. Binding energy analysis, conducted via molecular dynamics simulations, revealed a highly negative value of -25.50 kcal/mol for Compound 22, surpassing that of clovibactin and underscoring its potential efficacy. In addition, Compound 22 was prioritized due to its exceptional binding affinity to lipid II and its favorable ADME-Tox properties, suggesting a lower likelihood of adverse effects. These characteristics position Compound 22 as a promising candidate for further pharmacological development. While our computational results are encouraging, experimental validation is essential to confirm the efficacy and safety of these compounds. This study not only advances our understanding of clovibactin analogs but also contributes to the ongoing efforts to combat antimicrobial resistance through innovative antibiotic development.

Innovative analytical methodology for skin anti-aging compounds discovery from plant extracts: Integration of High-Performance Thin-Layer Chromatography-in vitro spectrophotometry bioassays with multivariate modeling and molecular docking

Skin aging, characterized by reduced elasticity, wrinkles, and changes in pigmentation, presents significant challenges in the cosmetics industry. Identifying compounds that can help mitigate these effects is crucial to developing effective anti-aging treatments and improving skin health. An advanced analytical approach for identifying skin anti-aging compounds within complex natural mixtures must be developed to achieve this. This study introduces a state-of-the-art methodology that combines High-Performance Thin-Layer Chromatography (HPTLC) and in vitro skin anti-aging spectrophotometry bioassays with regression multivariate analysis and molecular docking. The proposed methodology integrates spectrophotometric assays for tyrosinase inhibition (anti-pigmentation), elastase inhibition (anti-wrinkle), and radical scavenging capacity (DPPH•/ ABTS• assays) with analytical signals obtained from HPTLC chromatograms using Partial Least Squares models (PLS). The PLS models for predicting elastase inhibition and antioxidative capacity showed high accuracy with minimal errors. This study introduces an innovative approach combining HPTLC profiling and PLS regression to identify single phenolic compounds/bands responsible for anti-aging effects. In addition, identified bioactives were submitted to molecular docking studies to elucidate the enzyme inhibition mechanisms of elastase and confirm our approach. This integrated, simple, cost-effective, and high-throughput approach represents a significant advancement in the discovery of anti-aging compounds, with promising implications for future skincare and therapeutic applications.

Synthesis of novel deuterated EGFR/ALK dual-target inhibitors and their activity against non-small cell lung cancer

EGFR and ALK are common driver genes in NSCLC, and more patients with these mutations are being identified due to medical advances. Thus, developing dual-target EGFR/ALK inhibitors is crucial. In this study, 10 novel small molecules were designed and synthesized. CCK8 experiments revealed that compound (-)-9a exhibited the best anti-tumor activity, with IC50 values of 1.08 ± 0.07 nM for EGFR and 2.395 ± 0.023 nM for ALK mutant tumor cells. Studies show that compound (-)-9a can inhibit phosphorylated proteins in EGFR, ALK, and BRK signaling pathways and halt the cell cycle, leading to reduced mitochondrial membrane potential and apoptosis in tumor cells. Additionally, (-)-9a not only directly targets tumor cells but also exhibits potential immune-enhancing effects. Furthermore, evaluations conducted in animal models have demonstrated that this drug effectively reduces tumor growth in vivo. In summary, (-)-9a boasts dual-targeting, potent antitumor activity, and immune-enhancing potential, presenting vast potential as a next-gen anticancer drug.

EvaluationMaster: A GUI Tool for Structure-Based Virtual Screening Evaluation Analysis and Decision-Making Support

Structure-based virtual screening (SBVS) plays an indispensable role in the early phases of drug discovery, utilizing computational docking techniques to predict interactions between molecules and biological targets. During the SBVS process, selecting appropriate target structures and screening algorithms is crucial, as these choices significantly shape the outcomes. Typically, such selections require researchers to be proficient with multiple algorithms and familiar with evaluation and analysis processes, complicating their tasks. These algorithms' lack of graphical user interfaces (GUIs) further complicates it. To address these challenges, we introduced EvaluationMaster, the first GUI tool designed specifically to streamline and standardize the evaluation and decision-making processes in SBVS. It supports four docking algorithms' evaluation under multiple target structures and offers a comprehensive platform that manages the entire workflow─including the downloading of molecules, construction of decoy datasets, prediction of protein pockets, batch docking, and extensive data analysis. By automating complex evaluation tasks and providing clear visualizations of analysis results, EvaluationMaster significantly reduces the learning curve for researchers and boosts the efficiency of evaluations, potentially improving SBVS hit rates and accelerating the discovery and development of new therapeutic agents.

Structural bioinformatics for rational drug design

A State of the Art lecture titled "structural bioinformatics technologies for rational drug design: from in silico to in vivo" was presented at the International Society on Thrombosis and Haemostasis (ISTH) Congress in 2024. Drug discovery remains a resource-intensive and complex endeavor, which usually takes over a decade and costs billions to bring a new therapeutic agent to market. However, the landscape of drug discovery has been transformed by the recent advancements in bioinformatics and cheminformatics. Key techniques, including structure- and ligand-based virtual screening, molecular dynamics simulations, and artificial intelligence-driven models are allowing researchers to explore vast chemical spaces, investigate molecular interactions, predict binding affinity, and optimize drug candidates with unprecedented accuracy and efficiency. These computational methods complement experimental techniques by accelerating the identification of viable drug candidates and refining lead compounds. Artificial intelligence models, alongside traditional physics-based simulations, now play an important role in predicting key properties such as binding affinity and toxicity, contributing to more informed decision-making, particularly early in the drug discovery process. Despite these advancements, challenges remain in terms of accuracy, interpretability, and the needed computational power. This review explores the state of the art in computational drug discovery, examining the latest methods and technologies, their transformative impact on the drug development pipeline, and the future directions needed to overcome remaining limitations. Finally, we summarize relevant data and highlight cases where various computational approaches were successfully applied to develop novel inhibitors, as presented during the ISTH 2024 Congress.

Ultra-Large Virtual Screening: Definition, Recent Advances, and Challenges in Drug Design

Virtual screening (VS) in drug design employs computational methodologies to systematically rank molecules from a virtual compound library based on predicted features related to their biological activities or chemical properties. The recent expansion in commercially accessible compound libraries and the advancements in artificial intelligence (AI) and computational power - including enhanced central processing units (CPUs), graphics processing units (GPUs), high-performance computing (HPC), and cloud computing - have significantly expanded our capacity to screen libraries containing over 109 molecules. Herein, we review the concept of ultra-large virtual screening (ULVS), focusing on the various algorithms and methodologies employed for virtual screening at this scale. In this context, we present the software utilized, applications, and results of different approaches, such as brute force docking, reaction-based docking approaches, machine learning (ML) strategies applied to docking or other VS methods, and similarity/pharmacophore search-based techniques. These examples represent a paradigm shift in the drug discovery process, demonstrating not only the feasibility of billion-scale compound screening but also their potential to identify hit candidates and increase the structural diversity of novel compounds with biological activities.

Discovery of the First-in-Class Dual TSPO/Carbonic Anhydrase Modulators with Promising Neurotrophic Activity

In searching for putative new therapeutic strategies to treat neurodegenerative diseases, the mitochondrial 18 kDa translocator protein (TSPO) and cerebral isoforms of carbonic anhydrase (CA) were exploited as potential targets. Based on the structures of a class of highly affine and selective TSPO ligands and a class of CA activators, both developed by us in recent years, a small library of 2-phenylindole-based dual TSPO/CA modulators was developed, able to bind TSPO and activate CA VII in the low micromolar/submicromolar range. The interaction with the two targets was corroborated by computational studies. Biological investigation on human microglia C20 cells identified derivative 3 as a promising lead compound worthy of future optimization due to its (i) lack of cytotoxicity, (ii) ability to stimulate TSPO steroidogenic function and activate CA VII, and (iii) ability to effectively upregulate gene expression of the brain-derived neurotrophic factor.

Artificial Intelligence Transforming Post-Translational Modification Research

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.

Targeting SARS-CoV-2 Proteins: In Silico Investigation with Polypyridyl-Based Zn(II)-Curcumin Complexes

Herein, we have selected eight Zn(II)-based complexes viz., [Zn(bpy)(acac)Cl] (1), [Zn(phen)(acac)Cl] (2), [Zn(dppz)(acac)Cl] (3), [Zn(dppn)(acac)Cl] (4), [Zn(bpy)(cur)Cl] (5), [Zn(phen)(cur)Cl] (6), [Zn(dppz)(cur)Cl] (7), [Zn(dppn)(cur)Cl] (8), where bpy=2,2'-bipyridine, phen=1,10-phenanthroline, dppz=benzo[i]dipyrido[3,2-a:2',3'-c]phenazine, dppn=naphtho[2,3-i]dipyrido[3,2-a:2',3'-c]phenazine, acac=acetylacetonate, cur=curcumin and performed in silico molecular docking studies with the viral proteins, i. e., spike protein (S), Angiotensin-converting enzyme II Receptor protein (ACE2), nucleocapsid protein (N), main protease protein (Mpro), and RNA-dependent RNA polymerase protein (RdRp) of SARS-CoV-2. The binding energy calculations, visualization of the docking orientation, and analysis of the interactions revealed that these complexes could be potential inhibitors of the viral proteins. Among complexes 1-8, complex 6 showed the strongest binding affinity with S and ACE2 proteins. 4 exerted better binding affinity in the case of the N protein, whereas 8 presented the highest binding affinities with Mpro and RdRp among all the complexes. Overall, the study indicated that Zn(II) complexes have the potential as alternative and viable therapeutic solutions for COVID-19.

Exploring Chemical Spaces in the Billion Range: Is Docking a Computational Alternative to DNA-Encoded Libraries?

The concept of DNA-encoded libraries (DELs) enables the experimental screening of billions of compounds simultaneously, offering an unprecedented boost in the coverage of chemical space. In parallel, however, dramatically increased access to supercomputers and a number of ultrahigh throughput virtual screening (uHTVS) tools have made screening of billion-membered virtual libraries available. Here, we investigate whether current, brute-force, or AI-enabled uHTVS approaches might constitute a computational alternative to DEL screening. While it is tempting to look at uHTVS as a computational analogue of DEL screening, we found specific advantages and limitations of both methodologies that suggest them being complementary rather than competitive.

DSDPFlex: Flexible-Receptor Docking with GPU Acceleration

Molecular docking is an essential tool in structure-based drug discovery, widely utilized to model ligand-protein interactions and enrich potential hits. Among the different docking strategies, semiflexible docking (rigid-receptor and flexible-ligand model) is the most popular, benefiting from its balance of docking accuracy and speed. However, this approach ignores the conformational changes of proteins and hence demands suitable protein conformations as input. When the binding interaction adheres to an induced-fit model, flexible methods such as molecular dynamics simulation can be utilized, but they are computationally demanding. To balance between speed and accuracy, the flexible docking approach is an effective choice, as exemplified by AutoDock Vina and AutoDockFR, which treat selected protein side chains as flexible parts. However, the efficiency of flexible docking methods is yet to be improved for virtual screening usage. In this article, we introduce DSDPFlex, an improved flexible-receptor docking method accelerated by GPU parallelization. Beyond acceleration, optimizations with respect to sampling, scoring, and search space are implemented in DSDPFlex to further improve its capability in flexible tasks. In cross-docking evaluation, DSDPFlex demonstrates superior accuracy compared to AutoDock Vina and is 100 times faster than Vina in flexible-receptor tasks. We also show the advantage of flexible-receptor methods on suboptimal pockets and validate the advantage of DSDPFlex in screening on apo and AlphaFold2-predicted structures. With improvements in both efficiency and accuracy, DSDPFlex is expected to hold potential in future docking-based studies.

Critical assessment of LC3/GABARAP ligands used for degrader development and ligandability of LC3/GABARAP binding pockets

Recent successes in developing small molecule degraders that act through the ubiquitin system have spurred efforts to extend this technology to other mechanisms, including the autophagosomal-lysosomal pathway. Therefore, reports of autophagosome tethering compounds (ATTECs) have received considerable attention from the drug development community. ATTECs are based on the recruitment of targets to LC3/GABARAP, a family of ubiquitin-like proteins that presumably bind to the autophagosome membrane and tether cargo-loaded autophagy receptors into the autophagosome. In this work, we rigorously tested the target engagement of the reported ATTECs to validate the existing LC3/GABARAP ligands. Surprisingly, we were unable to detect interaction with their designated target LC3 using a diversity of biophysical methods. Intrigued by the idea of developing ATTECs, we evaluated the ligandability of LC3/GABARAP by in silico docking and large-scale crystallographic fragment screening. Data based on approximately 1000 crystal structures revealed that most fragments bound to the HP2 but not to the HP1 pocket within the LIR docking site, suggesting a favorable ligandability of HP2. Through this study, we identified diverse validated LC3/GABARAP ligands and fragments as starting points for chemical probe and ATTEC development.

In silico studies for improving target selectivity of anti-malarial dual falcipain inhibitors vis-à-vis human cathepsins

Dual falcipain-2 (FP-2) and falcipain-3 (FP-3) inhibitors, NM12 and NM15, displayed micromolar inhibitions but they exhibit similar binding affinities for the human cathepsins, thus indicating potential toxicity. The current study aims to develop a model to enhance the selectivity of the falcipain inhibitors vis-à-vis human cathepsins using previously identified dual falcipain 2 and 3 inhibitors, NM12 and NM15. To improve the selectivity of NM12 and NM15, analogs with weaker interactions with the conserved residues in the FPs and hCatK were designed while enhancing the unique interactions for the FPs. In silico analysis was carried out in the S2 subsite of both plasmodium and human proteases which is considered the preferred selective site due to the presence of less conserved residues. The Fasta sequence alignment and active/conserved binding site superimposition show that FPs contain acidic polar residues (Asp234 for FP2 and Glu243 for FP3) while hCatK has a neutral hydrophobic residue (Leu209) at the S2 subsite. Therefore, analogs of NM12 and NM15 were designed to enhance affinity and selectivity by improving interactions with these acidic residues while avoiding interactions with hydrophobic residues in hCatK. Newly designed analogs (NM12H and NM15G) show better selectivity as well as binding affinity towards FPs (ΔG of NM12H: -74.49 kcal/mol for FP2, -70.97 kcal/mol for FP3; ΔG of NM15G: -70.09 kcal/mol for FP2, -74.52 kcal/mol for FP3) as compared to NM12 and NM15. Thus, the selectivity and binding affinity against dual falcipains vis-à-vis human cathepsin were improved using molecular dynamic simulations.

Introducing SpaceGA: A Search Tool to Accelerate Large Virtual Screenings of Combinatorial Libraries

The growth of make-on-demand libraries in recent years has provided completely new possibilities for virtual screening for discovering new hit compounds with specific and favorable properties. However, since these libraries now contain billions of compounds, screening them using traditional methods such as molecular docking has become challenging and requires substantial computational resources. Thus, to take real advantage of the new possibilities introduced by the make-on-demand libraries, different methods have been proposed to accelerate the screening process and prioritize molecules for evaluation. Here, we introduce SpaceGA, a genetic algorithm that leverages the rapid similarity search tool SpaceLight (Bellmann, L.; Penner, P.; Rarey, M. Topological similarity search in large combinatorial fragment spaces. J. Chem. Inf. Model. 2021, 61, 238-251). to constrain the optimization process to accessible compounds within desired combinatorial libraries. As shown herein, SpaceGA is able to efficiently identify molecules with desired properties from trillions of synthesizable compounds by enumerating and evaluating only a small fraction of them. On this basis, SpaceGA represents a promising new tool for accelerating and simplifying virtual screens of ultralarge combinatorial databases.

Discovering Dually Active Anti-cancer Compounds with a Hybrid AI-structure-based Approach

Cancer's persistent growth often relies on its ability to maintain telomere length and tolerate the accumulation of DNA damage. This study explores a computational approach to identify compounds that can simultaneously target both G-quadruplex (G4) structures and poly(ADP-ribose) polymerase (PARP)1 enzyme, offering a potential multipronged attack on cancer cells. We employed a hybrid virtual screening (VS) protocol, combining the power of machine learning with traditional structure-based methods. PyRMD, our AI-powered tool, was first used to analyze vast chemical libraries and to identify potential PARP1 inhibitors based on known bioactivity data. Subsequently, a structure-based VS approach selected compounds from these identified inhibitors for their G4 stabilization potential. This two-step process yielded 50 promising candidates, which were then experimentally validated for their ability to inhibit PARP1 and stabilize G4 structures. Ultimately, four lead compounds emerged as promising candidates with the desired dual activity and demonstrated antiproliferative effects against specific cancer cell lines. This study highlights the potential of combining Artificial Intelligence and structure-based methods for the discovery of multitarget anticancer compounds, offering a valuable approach for future drug development efforts.

Accurate prediction of protein-ligand interactions by combining physical energy functions and graph-neural networks

We introduce an advanced model for predicting protein-ligand interactions. Our approach combines the strengths of graph neural networks with physics-based scoring methods. Existing structure-based machine-learning models for protein-ligand binding prediction often fall short in practical virtual screening scenarios, hindered by the intricacies of binding poses, the chemical diversity of drug-like molecules, and the scarcity of crystallographic data for protein-ligand complexes. To overcome the limitations of existing machine learning-based prediction models, we propose a novel approach that fuses three independent neural network models. One classification model is designed to perform binary prediction of a given protein-ligand complex pose. The other two regression models are trained to predict the binding affinity and root-mean-square deviation of a ligand conformation from an input complex structure. We trained the model to account for both deviations in experimental and predicted binding affinities and pose prediction uncertainties. By effectively integrating the outputs of the triplet neural networks with a physics-based scoring function, our model showed a significantly improved performance in hit identification. The benchmark results with three independent decoy sets demonstrate that our model outperformed existing models in forward screening. Our model achieved top 1% enrichment factors of 32.7 and 23.1 with the CASF2016 and DUD-E benchmark sets, respectively. The benchmark results using the LIT-PCBA set further confirmed its higher average enrichment factors, emphasizing the model's efficiency and generalizability. The model's efficiency was further validated by identifying 23 active compounds from 63 candidates in experimental screening for autotaxin inhibitors, demonstrating its practical applicability in hit discovery.Scientific contributionOur work introduces a novel training strategy for a protein-ligand binding affinity prediction model by integrating the outputs of three independent sub-models and utilizing expertly crafted decoy sets. The model showcases exceptional performance across multiple benchmarks. The high enrichment factors in the LIT-PCBA benchmark demonstrate its potential to accelerate hit discovery.

Vina-GPU 2.1: Towards Further Optimizing Docking Speed and Precision of AutoDock Vina and Its Derivatives

AutoDock Vina and its derivatives have established themselves as a prevailing pipeline for virtual screening in contemporary drug discovery. Our Vina-GPU method leverages the parallel computing power of GPUs to accelerate AutoDock Vina, and Vina-GPU 2.0 further enhances the speed of AutoDock Vina and its derivatives. Given the prevalence of large virtual screens in modern drug discovery, the improvement of speed and accuracy in virtual screening has become a longstanding challenge. In this study, we propose Vina-GPU 2.1, aimed at enhancing the docking speed and precision of AutoDock Vina and its derivatives through the integration of novel algorithms to facilitate improved docking and virtual screening outcomes. Building upon the foundations laid by Vina-GPU 2.0, we introduce a novel algorithm, namely Reduced Iteration and Low Complexity BFGS (RILC-BFGS), designed to expedite the most time-consuming operation. Additionally, we implement grid cache optimization to further enhance the docking speed. Furthermore, we employ optimal strategies to individually optimize the structures of ligands, receptors, and binding pockets, thereby enhancing the docking precision. To assess the performance of Vina-GPU 2.1, we conduct extensive virtual screening experiments on three prominent targets, utilizing two fundamental compound libraries and seven docking tools. Our results demonstrate that Vina-GPU 2.1 achieves an average 4.97-fold acceleration in docking speed and an average 342% improvement in EF1% compared to Vina-GPU 2.0.

Antiprotozoal Natural Products from Endophytic Fungi Associated with Cacao and Coffee

BACKGROUND

Collectively, leishmaniasis and Chagas disease cause approximately 8 million cases and more than 40,000 deaths annually, mostly in tropical and subtropical regions. The current drugs used to treat these diseases have limitations and many undesirable side effects; hence, new drugs with better clinical profiles are needed. Fungal endophytes associated with plants are known to produce a wide array of bioactive secondary metabolites, including antiprotozoal compounds. In this study, we analyzed endophytic fungal isolates associated with Theobroma cacao and Coffea arabica crop plants, which yielded extracts with antitrypanosomatid activity.

METHODS

Crude extracts were subjected to bioassay-guided isolation by HPLC, followed by spectrometric and spectroscopic analyses via mass spectrometry (MS) and nuclear magnetic resonance (NMR), Results: Compounds 1-9 were isolated and displayed novel antitrypanosomal and antileishmanial activities ranging from 0.92 to 32 μM. Tandem liquid chromatography-mass spectrometry (LC-MS) analysis of the organic extracts from different strains via the feature-based Global Natural Products Social (GNPS) molecular networking platform allowed us to dereplicate a series of metabolites (10-23) in the extracts. Molecular docking simulations of the active compounds, using the 3-mercaptopyruvate sulfurtransferase protein from L. donovani (Ld3MST) and the cruzipain enzyme from T. cruzi as putative molecular targets, allowed us to suggest possible mechanisms for the action of these compounds.

CONCLUSIONS

The isolation of these antiprotozoal compounds confirms that crop plants like coffee and cacao harbor populations of endophytes with biomedical potential that confer added value to these crops.

Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases

Structure-based virtual screening (SBVS) is a crucial computational approach in drug discovery, but its performance is sensitive to structural variations. Kinases, which are major drug targets, exemplify this challenge due to active site conformational changes caused by different inhibitor types. Most experimentally determined kinase structures have the DFGin state, potentially biasing SBVS towards type I inhibitors and limiting the discovery of diverse scaffolds. We introduce a multi-state modeling (MSM) protocol for AlphaFold2 (AF2) kinase structures using state-specific templates to address these challenges. Our comprehensive benchmarks evaluate predicted model qualities, binding pose prediction accuracy, and hit compound identification through ensemble SBVS. Results demonstrate that MSM models exhibit comparable or improved structural accuracy compared to standard AF2 models, enhancing pose prediction accuracy and effectively capturing kinase-ligand interactions. In virtual screening experiments, our MSM approach consistently outperforms standard AF2 and AF3 modeling, particularly in identifying diverse hit compounds. This study highlights the potential of MSM in broadening kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors, offering a promising solution to the structural bias problem in kinase-targeted drug discovery.

Ursolic acid interaction with transcription factors BRAF, V600E, and V600K: a computational approach towards new potential melanoma treatments

CONTEXT

Melanoma is one of the cancers with the highest mortality rate for its ability to metastasize. Several targets have undergone investigation for the development of drugs against this pathology. One of the main targets is the kinase BRAF (RAF, rapidly accelerated fibrosarcoma). The most common mutation in melanoma is BRAFV600E and has been reported in 50-90% of patients with melanoma. Due to the relevance of the BRAFV600E mutation, inhibitors to this kinase have been developed, vemurafenib-OMe and dabrafenib. Ursolic acid (UA) is a pentacyclic triterpene with a privileged structure, the pentacycle scaffold, which allows to have a broad variety of biological activity; the most studied is its anticancer capacity. In this work, we reported the interaction profile of vemurafenib-OMe, dabrafenib, and UA, to define whether UA has binding capacity to BRAFWT, BRAFV600E, and BRAFV600K. Homology modeling of BRAFWT, V600E, and V600K; molecular docking; and molecular dynamics simulations were carried out and interactions and residues relevant to the binding of the inhibitors were obtained. We found that UA, like the inhibitors, presents hydrogen bond interactions, and hydrophobic interactions of van der Waals, and π-stacking with I463, Q530, C532, and F583. The ΔG of ursolic acid in complex with BRAFV600K (- 63.31 kcal/mol) is comparable to the ΔG of the selective inhibitor dabrafenib (- 63.32 kcal/mol) in complex to BRAFV600K and presents a ΔG like vemurafenib-OMe with BRAFWT and V600E. With this information, ursolic acid could be considered as a lead compound for design cycles and to optimize the binding profile and the selectivity towards mutations for the development of new selective inhibitors for BRAFV600E and V600K to new potential melanoma treatments.

METHODS

The homology modeling calculations were executed on the public servers I-TASSER and ROBETTA, followed by molecular docking calculations using AutoGrid 4.2.6, AutoDockGPU 1.5.3, and AutoDockTools 1.5.6. Molecular dynamics and metadynamics simulations were performed in the Desmond module of the academic version of the Schrödinger-Maestro 2020-4 program, utilizing the OPLS-2005 force field. Ligand-protein interactions were evaluated using Schrödinger-Maestro program, LigPlot + , and PLIP (protein-ligand interaction profiler). Finally, all of the protein figures presented in this article were made in the PyMOL program.

Discovery of N-substituted-2-oxoindolin benzoylhydrazines as c-MET/SMO modulators in EGFRi-resistant non-small cell lung cancer

Non-small cell lung cancer (NSCLC), the leading cause of cancer-related mortality worldwide, poses a formidable challenge due to its heterogeneity and the emergence of resistance to targeted therapies. While initially effective, first- and third-generation EGFR-tyrosine kinase inhibitors (TKIs) often fail to control disease progression, leaving patients with limited treatment options. To address this unmet medical need, we explored the therapeutic potential of multitargeting agents that simultaneously inhibit two key signalling pathways, the mesenchymal-epithelial transition factor (c-MET) and the G protein-coupled receptor Smoothened (SMO), frequently dysregulated in NSCLC. By employing a combination of in silico drug repurposing and structure-based structure-activity relationship (SAR) studies, we identified and developed novel c-MET/SMO-targeting agents with antiproliferative activity against first- as well as third-generation EGFR-TKI-resistant NSCLC cells suggesting a synergistic effect arising from the simultaneous inhibition of c-MET and SMO.

Antioxidant Activity and Hypoallergenicity of Egg Protein Matrices Containing Polyphenols from Citrus Waste

This study reports on the interactions of egg proteins, which represent a major health concern in food allergy, with polyphenols obtained from orange and lemon peels. The antioxidant properties of such citrus peel extracts prior to protein binding were evaluated. The resulting edible, and therefore inherently safe, matrices exhibit reduced IgE binding compared to pure proteins in indirect immunological assays (ELISA) using individual sera from patients allergic to ovalbumin and lysozyme. The reduced allergenicity could arise from the interactions with polyphenols, which alter the structure and functionality of the native proteins. It is hypothesized that the anti-inflammatory and antioxidant properties of the polyphenols, described as inhibitors of the allergic response, could add immunomodulatory features to the hypoallergenic complexes. A docking analysis using lysozyme was conducted to scrutinize the nature of the protein-polyphenol interactions. An in silico study unravelled the complexity of binding modes depending on the isoforms considered. Altogether, the presented results validate the antioxidant properties and reduced allergenicity of polyphenol-fortified proteins. Lastly, this study highlights the upgrading of vegetable wastes as a source of natural antioxidants, thus showing the benefits of a circular economy in agri-food science.

Allosteric modulation of the CXCR4:CXCL12 axis by targeting receptor nanoclustering via the TMV-TMVI domain

CXCR4 is a ubiquitously expressed chemokine receptor that regulates leukocyte trafficking and arrest in both homeostatic and pathological states. It also participates in organogenesis, HIV-1 infection, and tumor development. Despite the potential therapeutic benefit of CXCR4 antagonists, only one, plerixafor (AMD3100), which blocks the ligand-binding site, has reached the clinic. Recent advances in imaging and biophysical techniques have provided a richer understanding of the membrane organization and dynamics of this receptor. Activation of CXCR4 by CXCL12 reduces the number of CXCR4 monomers/dimers at the cell membrane and increases the formation of large nanoclusters, which are largely immobile and are required for correct cell orientation to chemoattractant gradients. Mechanistically, CXCR4 activation involves a structural motif defined by residues in TMV and TMVI. Using this structural motif as a template, we performed in silico molecular modeling followed by in vitro screening of a small compound library to identify negative allosteric modulators of CXCR4 that do not affect CXCL12 binding. We identified AGR1.137, a small molecule that abolishes CXCL12-mediated receptor nanoclustering and dynamics and blocks the ability of cells to sense CXCL12 gradients both in vitro and in vivo while preserving ligand binding and receptor internalization.

Structure-Aided Computational Design of Triazole-Based Targeted Covalent Inhibitors of Cruzipain

Cruzipain (CZP), the major cysteine protease present in T. cruzi, the ethiological agent of Chagas disease, has attracted particular attention as a therapeutic target for the development of targeted covalent inhibitors (TCI). The vast chemical space associated with the enormous molecular diversity feasible to explore by means of modern synthetic approaches allows the design of CZP inhibitors capable of exhibiting not only an efficient enzyme inhibition but also an adequate translation to anti-T. cruzi activity. In this work, a computer-aided design strategy was developed to combinatorially construct and screen large libraries of 1,4-disubstituted 1,2,3-triazole analogues, further identifying a selected set of candidates for advancement towards synthetic and biological activity evaluation stages. In this way, a virtual molecular library comprising more than 75 thousand diverse and synthetically feasible analogues was studied by means of molecular docking and molecular dynamic simulations in the search of potential TCI of CZP, guiding the synthetic efforts towards a subset of 48 candidates. These were synthesized by applying a Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) centered synthetic scheme, resulting in moderate to good yields and leading to the identification of 12 hits selectively inhibiting CZP activity with IC50 in the low micromolar range. Furthermore, four triazole derivatives showed good anti-T. cruzi inhibition when studied at 50 μM; and Ald-6 excelled for its high antitrypanocidal activity and low cytotoxicity, exhibiting complete in vitro biological activity translation from CZP to T. cruzi. Overall, not only Ald-6 merits further advancement to preclinical in vivo studies, but these findings also shed light on a valuable chemical space where molecular diversity might be explored in the search for efficient triazole-based antichagasic agents.

MzDOCK: A free ready-to-use GUI-based pipeline for molecular docking simulations

Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.

Discovery and characterization of novel FGFR1 inhibitors in triple-negative breast cancer via hybrid virtual screening and molecular dynamics simulations

The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schrödinger's Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p-FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound's specific binding interactions with FGFR1.

Insight into molecular basis and dynamics of full-length CRaf kinase in cellular signaling mechanisms

Raf kinases play key roles in signal transduction in cells for regulating proliferation, differentiation, and survival. Despite decades of research into functions and dynamics of Raf kinases with respect to other cytosolic proteins, understanding Raf kinases is limited by the lack of their full-length structures at the atomic resolution. Here, we present the first model of the full-length CRaf kinase obtained from artificial intelligence/machine learning algorithms with a converging ensemble of structures simulated by large-scale temperature replica exchange simulations. Our model is validated by comparing simulated structures with the latest cryo-EM structure detailing close contacts among three key domains and regions of the CRaf. Our simulations identify potentially new epitopes of intramolecule interactions within the CRaf and reveal a dynamical nature of CRaf kinases, in which the three domains can move back and forth relative to each other for regulatory dynamics. The dynamic conformations are then used in a docking algorithm to shed insight into the paradoxical effect caused by vemurafenib in comparison with a paradox breaker PLX7904. We propose a model of Raf-heterodimer/KRas-dimer as a signalosome based on the dynamics of the full-length CRaf.