Estimation Of Binding Affinities Research Articles

Design, Synthesis, and Molecular Docking of Quinazolines Bearing Caffeoyl Moiety for Targeting of PGK1/PKM2/STAT3 Signaling Pathway in the Human Breast Cancer.

PGK1 and PKM2 are glycolytic enzymes, and their expression is upregulated in cancer cells. STAT3 is a transcription factor implicated in breast cancer progression and chemoresistance. Researchers worldwide continue to explore how targeting genes might lead to more effective anti-breast cancer therapies. The present study aims to synthesize quinazolines containing caffeoyl moiety for developing innovative anticancer agents against the human breast cancer cell line (MCF-7). A new quinazoline 2 was synthesized by reacting caffeic acid with 5-amino-phenylpyrazole carboxylate 1 in the presence of PCl3. Compound 2 reacted with NH2NH2.H2O to produce compound 3 through cyclo-condensation. Apoptosis and necrosis as well as inhibition activity compounds 2 and 3 against PGK1, and PKM2 were evaluated. The effect of compounds 2 and 3 on the levels of GSH, GR, SOD, GPx, CAT, MDA, Bax, Bcl-2, caspase-3, P53 and VEGF levels as well as PGK1, PKM2 and STAT3 gene expression were estimated in MCF-7 tumor cells. The viability of MCF-7 cells was reduced to 22.42% and 45.86% after incubation with compounds 2 and 3 for 48 hours, respectively. The IC50 values for compounds 2 and 3 are 62.05 μg/mL and 16.73 μg/mL. Furthermore, compound 3 exhibited more significant apoptosis and necrosis than compound 2. IC50 values of compound 2 against PGK1, and PKM2 at interval concentration equals 1.04, and 0.32 μM/mL, respectively, after 210 minutes of incubation. Moreover, compound 3 were revealed strong inhibition of PGK1, and PKM2 with IC50 values equals 0.55 and 0.21 μg/mL, respectively after 210 minutes of incubation. Our results proved that the incubation of compounds 2 and 3 with MCF-7 cells increased the levels of apoptotic proteins, elevated MDA, Bax, caspase-3 and P53 levels, depleted GSH, GR, SOD, GPx, CAT, Bcl-2 levels and downregulated the levels of STAT3, PGK1, and PKM2 gene expression significantly. Our In-silico results proved that compound 2 showed a stronger estimated binding affinity with a ΔG of -7.2, -7.5, and - 7.9 kcal/mol., respectively towards PGK1, PKM2 and STAT3 proteins. Also, compound 3 exhibits a strong binding affinity with ΔG of -7.9, -8.5, and - 8.7 kcal/mol., towards PGK1, PKM2 and STAT3 proteins. The results show that compounds 2 and 3 induce apoptotic activity by blocking the PGK1- PKM2-STAT3 signaling pathway. The present investigation opens exciting possibilities for developing innovative new anticancer quinazolines bearing caffeoyl moiety.

Probing marine macroalgal phlorotannins as an antibacterial candidate against Salmonella typhi: Molecular docking and dynamics simulation approach

The increasing prevalence of drug-resistant bacterial strains, including multidrug-resistant Salmonella typhi, has raised significant concerns about the effectiveness of traditional antimicrobial treatments. To address this issue, the study employed computational techniques to evaluate the potential of natural phlorotannins derived from marine algae as alternative antibacterial agents against S. typhi. A total of 104 phlorotannins were retrieved from PubChem and subjected to molecular docking with three specific target proteins of S. typhi. Among the compounds tested, Compound-10 a derivative of dieckol exhibited the highest affinity for the Omptin family outer membrane protease [pgtE] protein and for the cell invasion protein sipB protein. The docking results revealed strong interactions between the phlorotannins and the target proteins, indicating their potential as antimicrobial agents. Additionally, pharmacokinetic studies using the QikProp module demonstrated that the top-ranked compounds showed favourable drug-like properties with good druggable efficiency, moderate gut-blood barrier transport, and high human oral absorption. Most compounds passed the rule of three and five drug-likeness criteria, indicating their potential as drug candidates. Furthermore, MM-GBSA analysis provided relative binding affinity estimations, ranking the compounds based on their calculated binding energies. These results align reasonably well with experimental binding affinities, reinforcing the potential of the identified compounds as potent binders to their target proteins. This study highlights the promising antibacterial potential of marine phlorotannins against S. typhi. Further in vitro studies are warranted to validate these compounds' efficacy and anti-microbial activity, ultimately paving the way for developing new therapeutic strategies to combat drug-resistant Salmonella infections.

Recent perspective on polymeric Semimetal (Si, Ge and As) and nonmetal (N and P) doped C70-Fullerene system: Comparative electronic, dynamic behavior and chemotherapy docking with ADMET analysis

Fullerene system, based on carbon atoms, was studied and its structural modification was built with consideration of several elements such as Si, N, P, Ge, and As, where doped modified systems were constructed. Synthetic routes survey was considered to acquire the structures' availability in further applications related to structure properties investigation. The computational investigation of these designed systems was described using the most popular approach of density functional theory (DFT). Electronic behavior for all systems was studied especially through density of states (DOS) spectra, molecular electrostatic potential (MEP), and frontier molecular orbitals (FMOs) for best comparison towards stability with less energetic properties, where C-fullerene was predicted as a more stable candidate as its ∆E with value of 1.714 eV. Molecular dynamic simulation (MD) was considered to predict the stability of the atoms in the system during the physical movement at 100 ps. A polymeric model of triple Fullerene rings was linked for each Semimetal atom (Si, Ge, and As) to predict the radial distribution function (RDF) and dynamic behavior for stability conditions. The calculated energy parameters such as potential, kinetic, and non-bond energies mainly described the system movement stability during the simulation time. Molecular docking analysis of the modified doped systems was performed for chemotherapy prediction of the studied systems against breast cancer target protein (5NWH) to evaluate the inhibition strength through active sites binding affinity. The estimated binding affinity of Si-fullerene was found the most favorable result (-12.73 kcal/mol). ADMET properties were estimated for further drug-like prediction through comparative pharmacokinetic factors.

Exploring the anti-inflammatory potential of phytochemicals against STING::TBK1 pathway

ABSTRACT Viral illnesses frequently cause excessive inflammation in the body, which adds to the severity of symptoms and problems. Understanding and regulating this inflammatory response is critical for successful viral infection management and therapy. It is traditional Ayurvedic understanding to reduce inflammation with formulations of Tulsi, Dalchini, Sunthi, and Krishan mirch. Using computer-aided drug discovery methodologies, this study aims to find phytochemicals that interact with TBK1 and are likely to have this anti-inflammatory effect. Molecular docking results revealed that Oleanolic acid, Astiaticoside A, Cepharadione A, Proanthrocyanidin, and Alpha carotene had binding scores of −8.1 kcal/mol, −7.8 kcal/mol, −6.8 kcal/mol, −6.75 kcal/mol, and −6.8 kcal/mol against TBK1, respectively. 20 ns MD simulations of TBK1 in complex with phytochemicals were performed to evaluate complex stability and protein conformational changes. The simulations showed stable complexes with favourable energy profiles, as evidenced by characteristics such as RMSD, RMSF, Rg, and hydrogen bonding. The MM-PBSA analysis produced precise binding affinity estimates. This work emphasises the potential of these phytochemicals and the need for additional in vitro and in vivo validation.

Amalgamated Pharmacoinformatics Study to Investigate the Mechanism of Xiao Jianzhong Tang against Chronic Atrophic Gastritis.

Traditional Chinese medicine (TCM) Xiao Jianzhong Tang (XJZ) has a favorable efficacy in the treatment of chronic atrophic gastritis (CAG). However, its pharmacological mechanism has not been fully explained. The purpose of this study was to find the potential mechanism of XJZ in the treatment of CAG using pharmacocoinformatics approaches. Network pharmacology was used to screen out the key compounds and key targets, MODELLER and GNNRefine were used to repair and refine proteins, Autodock vina was employed to perform molecular docking, Δ Lin_F9XGB was used to score the docking results, and Gromacs was used to perform molecular dynamics simulations (MD). Kaempferol, licochalcone A, and naringenin, were obtained as key compounds, while AKT1, MAPK1, MAPK14, RELA, STAT1, and STAT3 were acquired as key targets. Among docking results, 12 complexes scored greater than five. They were run for 50ns MD. The free binding energy of AKT1-licochalcone A and MAPK1-licochalcone A was less than -15 kcal/mol and AKT1-naringenin and STAT3-licochalcone A was less than -9 kcal/mol. These complexes were crucial in XJZ treating CAG. Our findings suggest that licochalcone A could act on AKT1, MAPK1, and STAT3, and naringenin could act on AKT1 to play the potential therapeutic effect on CAG. The work also provides a powerful approach to interpreting the complex mechanism of TCM through the amalgamation of network pharmacology, deep learning-based protein refinement, molecular docking, machine learning-based binding affinity estimation, MD simulations, and MM-PBSA-based estimation of binding free energy.

Comprehensive investigation of network pharmacology, computational modeling, and pharmacokinetic assessment to evaluate the efficacy of flavonoids in rheumatoid arthritis.

Rheumatoid arthritis is a chronic autoimmune disease characterized by inflammation and joint damage, imposing a significant burden on affected individuals worldwide. Flavonoids, a class of natural compounds abundant in various plant-based foods, have shown promising anti-inflammatory and immunomodulatory effects, suggesting their potential as therapeutic agents for RA. In this study, we conducted a comprehensive investigation of identified LCMS compounds utilizing network pharmacology, computational modeling, in silico approaches, and pharmacokinetic assessment to evaluate the efficacy of flavonoids in RA treatment. The study identified 5 flavonoid structures with common targets via LCMS and Integration of network pharmacology approaches enabled a comprehensive evaluation of the pharmacological profile of flavonoids in the context of RA treatment, guiding the selection of promising candidates for further experimental validation and clinical development. The top 10 targets were AKT1, PI3KR1, CDK2, EGFR, CDK6, NOS2, FLT3, ALOX5, CCNB1, and PTPRS via PPI network. The investigation emphasized several pathways, including the AGE-RAGE signaling pathway, resistance to EGFR tyrosine kinase inhibitors, the PI3K-AKT signaling network, and the Rap 1 signaling pathway. In silico studies estimated binding affinities that ranged from - 7.0 to - 10.0 kcal/mol. Schaftoside and Vitexin showed no toxicity in computational approach and found suitable for further investigations. Overall, our study underscores the potential of flavonoids as therapeutic agents for RA and highlights the utility of integrative approaches combining network pharmacology, computational modeling, in silico methods, and pharmacokinetic assessment in drug discovery and development processes.

A time‐efficient computational binding affinity estimation protocol with utilization of limited experimental data: A case study for adenosine receptor

AbstractEstimating binding affinity is a crucial step in the drug discovery process. In computer‐aided drug design, this challenge can be divided into two main tasks: finding the correct binding pose and estimating the binding free energy. In this study, we propose a new binding affinity estimation protocol that utilizes molecular docking with limited experimental data and estimates binding affinity using molecular dynamics simulation. A custom scoring function was employed during docking to identify an improved initial binding pose, and the linear interaction energy method with an optimized coefficient was used for binding affinity estimation. The protocol was validated with an external data set and applied to modafinil and its derivatives to rank their binding affinities to adenosine A2A receptors (ADORA2A) as a case study. This approach could be both time‐efficient and valuable for computational drug discovery, particularly when experimental data is limited.

Structural analysis and shape-based identification of novel inhibitors targeting the Trypanosoma cruzi proteasome

There is an urgent need to develop new, safer, and more effective drugs against Chagas disease (CD) as well as related kinetoplastid diseases. Targeting and inhibiting the Trypanosoma cruzi proteasome has emerged as a promising therapeutic approach in this context. To expand the chemical space for this class of inhibitors, we performed virtual screening campaigns with emphasis on shape-based similarity and ADMET prioritization. We describe the ideation and application of robustly validated shape queries for these campaigns, which furnished 44 compounds for biological evaluation. Five hit compounds demonstrated in vitro antitrypanosomal activity by potential inhibition of T. cruzi proteasome and notable chemical diversities, particularly, LCQFTC11. Structural insights were achieved by homology modeling, sequence/structure alignment, proteasome-species comparison, docking, molecular dynamics, and MMGBSA binding affinity estimations. These methods confirmed key interactions as well as the stability of LCQFTC11 at the β4/β5 subunits' binding site of the T. cruzi proteasome, consistent with known inhibitors. Our results warrant future assay confirmation of our hit as a T. cruzi proteasome inhibitor. Importantly, we also shed light into dynamic details for a proteasome inhibition mechanism that shall be further investigated. We expect to contribute to the development of viable CD drug candidates through such a relevant approach.

Thymoquinone, artemisinin, and thymol attenuate proliferation of lung cancer cells as Sphingosine kinase 1 inhibitors

Sphingosine-1-phosphate (S1P) formed via catalytic actions of sphingosine kinase 1 (SphK1) behaves as a pro-survival substance and activates downstream target molecules associated with various pathologies, including initiation, inflammation, and progression of cancer. Here, we aimed to investigate the SphK1 inhibitory potentials of thymoquinone (TQ), Artemisinin (AR), and Thymol (TM) for the therapeutic management of lung cancer. We implemented docking, molecular dynamics (MD) simulations, enzyme inhibition assay, and fluorescence measurement studies to estimate binding affinity and SphK1 inhibitory potential of TQ, AR, and TM. We further investigated the anti-cancer potential of these compounds on non–small cell lung cancer (NSCLC) cell lines (H1299 and A549), followed by estimation of mitochondrial ROS, mitochondrial membrane potential depolarization, and cleavage of DNA by comet assay. Enzyme activity and fluorescence binding studies suggest that TQ, AR, and TM significantly inhibit the activity of SphK1 with IC50 values of 35.52 µM, 42.81 µM, and 53.68 µM, respectively, and have an excellent binding affinity. TQ shows cytotoxic effect and anti-proliferative potentials on H1299 and A549 with an IC50 value of 27.96 µM and 54.43 µM, respectively. Detection of mitochondrial ROS and mitochondrial membrane potential depolarization shows promising TQ-induced oxidative stress on H1299 and A549 cell lines. Comet assay shows promising TQ-induced oxidative DNA damage. In conclusion, TQ, AR, and TM act as potential inhibitors for SphK1, with a strong binding affinity. In addition, the cytotoxicity of TQ is linked to oxidative stress due to mitochondrial ROS generation. Overall, our study suggests that TQ is a promising inhibitor of SphK1 targeting lung cancer therapy.

Biophysical principles predict fitness of SARS-CoV-2 variants

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the identification of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by dissociation constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto an epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low-frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.

Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges

AbstractStructure‐based drug design is a widely applied approach in the discovery of new lead compounds for known therapeutic targets. In most structure‐based drug design applications, the docking procedure is considered the crucial step. Here, a potential ligand is fitted into the binding site, and a scoring function assesses its binding capability. With the rise of modern machine‐learning in drug discovery, novel scoring functions using machine‐learning techniques achieved significant performance gains in virtual screening and ligand optimization tasks on retrospective data. However, real‐world applications of these methods are still limited. Missing success stories in prospective applications are one reason for this. Additionally, the fast‐evolving nature of the field makes it challenging to assess the advantages of each individual method. This review will highlight recent strides toward improved real world applicability of machine‐learning based scoring, enabling a better understanding of the potential benefits and pitfalls of these functions on a project. Furthermore, a systematic way of classifying machine‐learning based scoring that facilitates comparisons will be presented.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions.

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.

In silico strategies for identifying therapeutic candidates against Acinetobacter baumannii: spotlight on the UDP-N-acetylmuramoyl-L-alanine-D-glutamate:meso-diaminopimelate ligase (MurE)

The opportunistic bacterium Acinetobacter baumannii, which belongs to ESKAPE group of pathogenic bacteria, is leading cause of infections associated with gram-negative bacteria. Acinetobacter baumannii causes severe diseases, such as VAP (ventilator-associated pneumonia), meningitis, and UTI (urinary tract infections) among the nosocomial infections contracted in hospitals. The high infection rate and growing resistance to the vast array of antibiotics makes it paramount to look for new therapeutic strategies against this pathogen. The most promising therapeutic targets are the proteins involved in the synthesis of peptidoglycan which is chief component of bacterial cell wall, MurE is one of those enzymes and is responsible for the addition of one unit of meso-diaminopimelic acid (meso-A2pm) to the nucleotide precursor, UDPMurNAc-L-Ala-D-Glu, and aids in the formation of crosslinker pentapeptide chain. The three-dimensional structure of MurE was modelled using homology modelling technique and then vHTS was performed using this model against Approved Drug Library on DrugRep server using AutoDock Vina. Out of 500 drug molecules, two were selected based on estimated binding affinity, interaction pattern, interacting residues, etc. The selected drug molecules are DB12887 (Tazemetostat) and DB13879 (Glecaprevir). Then, MD simulations were performed on native MurE and its complexes with ligands to examine their dynamical behaviour, stability, integrity, compactness, and folding properties. The protein-ligand complexes were then subjected to binding free energy calculations using the MM/PBSA-based binding free energy analysis and the values are −109.788 ± 8.03 and −152.753 ± 11.98 kcal for MurE-DB12887 and MurE-DB13879 complex, respectively. All the analysis performed on MD trajectories for the complexes of these ligands with protein provided plenty of dependable evidences to consider these molecules for inhibition of MurE enzyme from A. baumannii. Communicated by Ramaswamy H. Sarma

A purely physics-based computational binding affinity estimation from restrained umbrella sampling simulations

A purely physics-based computational binding affinity estimation from restrained umbrella sampling simulations

Affinity Capillary Electrophoresis as a Tool To Characterize Molecularly Imprinted Nanogels in Solution.

In this work, an innovative and accurate affinity capillary electrophoresis (ACE) method was set up to monitor the complexation of aqueous MIP nanogels (NGs) with model cancer-related antigens. Using α2,6'- and α2,3'-sialyllactose as oversimplified cancer biomarker-mimicking templates, NGs were synthesized and characterized in terms of size, polydispersity, and overall charge. A stability study was also carried out in order to select the best storage conditions and to ensure product quality. After optimization of capillary electrophoresis conditions, injection of MIP NGs resulted in a single, sharp, and efficient peak. The mobility shift approach was applied to quantitatively estimate binding affinity, in this case resulting in an association constant of K ≈ 106 M-1. The optimized polymers further displayed a pronounced discrimination between the two sialylated sugars. The newly developed ACE protocol has the potential to become a very effective method for nonconstrained affinity screening of NG in solution, especially during the NG development phase and/or for a final accurate quantitation of the observed binding.

Probing the formation of a hetero-dimeric membrane transport complex with dual in vitro and in silico mutagenesis.

The reversible association of transmembrane helices is a fundamental mechanism in how living cells convey information and respond to physiological events. The cardiac calcium transport regulator phospholamban (PLN) is an example of a single-span transmembrane protein that populates a variety of reversible and competing oligomeric states. PLN primarily forms monomers and pentamers in the membrane, where the PLN pentamer is a storage form and the PLN monomer forms a hetero-dimeric inhibitory complex with SERCA. The binding affinity and free-energy of formation of the SERCA-PLN complex in a membrane have not been determined. As is the case for most transmembrane protein interactions, measuring these quantities experimentally is extremely challenging. In this study, we estimated binding affinities by employing in silico alchemical free-energy calculations for all PLN transmembrane alanine substitutions in a membrane bilayer. The binding affinities were calculated separately for the SERCA-PLN complex, a PLN monomer, and a PLN pentamer and compared to in vitro functional measurements of SERCA regulation by the PLN alanine substitutions. Initially, the changes in SERCA inhibition by PLN alanine substitutions were compared to the changes in free energy for the SERCA-PLN complex formed from the PLN monomer. However, the functional data for the PLN alanine substitutions were better explained by the formation of the SERCA-PLN complex directly from the PLN pentamer. This finding points to an inhibitory mechanism favoring conformational selection of SERCA and the interaction of a PLN pentamer with SERCA for 'delivery' of a PLN monomer to the inhibitory site. The implications of these findings suggest that the energetics of helix exchange between homo- and hetero-oligomeric signaling complexes is favored over an intermediate involving a free monomeric helix in the membrane bilayer.

Potent inhibitors targeting cyclin-dependent kinase 9 discovered via virtual high-throughput screening and absolute binding free energy calculations.

Due to the crucial regulatory mechanism of cyclin-dependent kinase 9 (CDK9) in mRNA transcription, the development of kinase inhibitors targeting CDK9 holds promise as a potential treatment strategy for cancer. A structure-based virtual screening approach has been employed for the discovery of potential novel CDK9 inhibitors. First, compounds with kinase inhibitor characteristics were identified from the ZINC15 database via virtual high-throughput screening. Next, the predicted binding modes were optimized by molecular dynamics simulations, followed by precise estimation of binding affinities using absolute binding free energy calculations based on the free energy perturbation scheme. The binding mode of molecule 006 underwent an inward-to-outward flipping, and the new binding mode exhibited binding affinity comparable to the small molecule T6Q in the crystal structure (PDB ID: 4BCF), highlighting the essential role of molecular dynamics simulation in capturing a plausible binding pose bridging docking and absolute binding free energy calculations. Finally, structural modifications based on these findings further enhanced the binding affinity with CDK9. The results revealed that enhancing the molecule's rigidity through ring formation, while maintaining the major interactions, reduced the entropy loss during the binding process and, thus, enhanced binding affinities.

PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening

As ultra-large-scale virtual screening becomes critical for early-stage drug discovery, highly efficient screening methods are gaining prominence. Deep-learning-based approaches which directly estimate binding affinities without binding conformation have attracted great...

In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations.

The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods. Each challenge contains two phases, hit-finding and follow-up optimization, each of which is followed by experimental validation of the computational predictions. For the CACHE Challenge #1, the Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 Repeat (WDR) domain was selected as the target for in silico hit-finding and optimization. Mutations in LRRK2 are the most common genetic cause of the familial form of Parkinson's disease. The LRRK2 WDR domain is an understudied drug target with no known molecular inhibitors. Herein we detail the first phase of our winning submission to the CACHE Challenge #1. We developed a framework for the high-throughput structure-based virtual screening of a chemically diverse small molecule space. Hit identification was performed using the large-scale Deep Docking (DD) protocol followed by absolute binding free energy (ABFE) simulations. ABFEs were computed using an automated molecular dynamics (MD)-based thermodynamic integration (TI) approach. 4.1 billion ligands from Enamine REAL were screened with DD followed by ABFEs computed by MD TI for 793 ligands. 76 ligands were prioritized for experimental validation, with 59 compounds successfully synthesized and 5 compounds identified as hits, yielding a 8.5% hit rate. Our results demonstrate the efficacy of the combined DD and ABFE approaches for hit identification for a target with no previously known hits. This approach is widely applicable for the efficient screening of ultra-large chemical libraries as well as rigorous protein-ligand binding affinity estimation leveraging modern computational resources.

Identification of natural antiviral drug candidates against Tilapia Lake Virus: Computational drug design approaches.

Tilapia Lake Virus (TiLV) is a disease that affects tilapia fish, causing a high rate of sudden death at any stage in their life cycle. Unfortunately, there are currently no effective antiviral drugs or vaccines to prevent or control the progression of this disease. Researchers have discovered that the CRM1 protein plays a critical function in the development and spreading of animal viruses. By inhibiting CRM1, the virus's spread in commercial fish farms can be suppressed. With this in mind, this study intended to identify potential antiviral drugs from two different tropical mangrove plants from tropical regions: Heritiera fomes and Ceriops candolleana. To identify promising compounds that target the CRM1 protein, a computer-aided drug discovery approach is employed containing molecular docking, ADME (absorption, distribution, metabolism and excretion) analysis, toxicity assessment as well as molecular dynamics (MD) simulation. To estimate binding affinities of all phytochemicals, molecular docking is used and the top three candidate compounds with the highest docking scores were selected, which are CID107876 (-8.3 Kcal/mol), CID12795736 (-8.2 Kcal/mol), and CID12303662 (-7.9 Kcal/mol). We also evaluated the ADME and toxicity properties of these compounds. Finally, MD simulation was conducted to analyze the stability of the protein-ligand complex structures and confirm the suitability of these compounds. The computational study demonstrated that the phytochemicals found in H. fomes and C. candolleana could potentially serve as important inhibitors of TiLV, offering practical utility. However, further in vivo investigations are necessary to investigate and potentially confirm the effectiveness of these compounds as antiviral drugs against the virus TiLV.