Computer-aided Drug Design Research Articles

Glycine-Conjugated α-Mangostins as Potential Estrogen Receptor Alpha (ERα) Antagonists through Pharmacophore Modeling, Docking Analysis, and Molecular Dynamics Simulations

Natural compounds have demonstrated good biological activity when combined with certain amino acids. For example, a glycine-conjugated glycyrrhetinic acid exhibits heightened efficiency against MCF7 cancer cells. Consequently, a molecular modeling analysis is conducted to construct glycine-conjugated α-mangostins and investigate their potential. According to pharmacophore modeling using the ligand-based drug design technique, only two glycine-conjugated α-mangostins conform to the pharmacophore features. The docking simulation results show that the Am1Gly conjugate can interact with the estrogen receptor-α (ERα) with a binding energy of −10.91 kcal/mol. This interaction is further supported by molecular dynamics simulations performed over a 200 ns timeframe. Based on molecular dynamics modeling using the MMPBSA method, the binding affinity of Am1Gly (ΔGTotal = −48.79 kcal/mol) is determined. The results of this analysis indicate that Am1Gly might function as an antagonist to estrogen receptors.

The Binding Capacity of the Lead Phytochemical Molecule to Cancer Cell Target Proteins and its Potential Anticancer Properties with Respect to Standard Drugs

Molecular docking is an important tool for connecting molecular biological structure to computer-aided drug design. The purpose of this blind docking experiment is to compare the binding energies of these three drugs and to predict the most likely binding poses of a ligand with a known three dimensional structure of a protein. To substantiate our previous in vitro study findings, an in silico model was chosen to compare the binding properties of the three drugs. The work is entirely bioinformatics in nature. Blind docking was accomplished with the help of free software/(s). A comparison was made among the three drgus, used primarily in cancer treatment, namely, anastrazole, capecitabine and quercetin. In our in vitro study, these three drugs were extremely effective. CB dock-2 to blind dock, SwissTargetPrediction to choose the targets, PubChem and Protein Data Base to obtain 3D structures of ligand and target respectively were used. Absorption, Distribution, Metabolism, and Excretion (ADME) from SwissADME, drug likeness from MolSoft L.L.C. software, and Liponski’s rule was used to check the “Rule of five (RO5)”. The crystallographic structures have resolution values ranging from 1 Å to 3 Å. Lipinski’s rule of five, Swiss ADME and drug likeness were used to compare the three drugs. For all of the targets studied, quecetin appeared to have the highest AutoDock vina scores. To summarize the current findings and publicly available data, quercetin is chemoprotective and radioprotective to healthy/ normal cells and it can be used during cancer treatment.

Machine learning-based QSAR and LB-PaCS-MD guided design of SARS-CoV-2 main protease inhibitors

The global outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus had led to profound respiratory health implications. This study focused on designing organoselenium-based inhibitors targeting the SARS-CoV-2 main protease (Mpro). The ligand-binding pathway sampling method based on parallel cascade selection molecular dynamics (LB-PaCS-MD) simulations was employed to elucidate plausible paths and conformations of ebselen, a synthetic organoselenium drug, within the Mpro catalytic site. Ebselen effectively engaged the active site, adopting proximity to H41 and interacting through the benzoisoselenazole ring in a π-π T-shaped arrangement, with an additional π-sulfur interaction with C145. In addition, the ligand-based drug design using the QSAR with GFA-MLR, RF, and ANN models were employed for biological activity prediction. The QSAR-ANN model showed robust statistical performance, with an r2training exceeding 0.98 and an RMSEtest of 0.21, indicating its suitability for predicting biological activities. Integration the ANN model with the LB-PaCS-MD insights enabled the rational design of novel compounds anchored in the ebselen core structure, identifying promising candidates with favorable predicted IC50 values. The designed compounds exhibited suitable drug-like characteristics and adopted an active conformation similar to ebselen, inhibiting Mpro function. These findings represent a synergistic approach merging ligand and structure-based drug design; with the potential to guide experimental synthesis and enzyme assay testing.

Peptide S4 is an entry inhibitor of SARS-CoV-2 infection

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a significant socioeconomic burden, and combating COVID-19 is imperative. Blocking the SARS-CoV-2 RBD–ACE2 interaction is a promising therapeutic approach for viral infections, as SARS-CoV-2 binds to the ACE2 receptors of host cells via the RBD of spike proteins to infiltrate these cells. We used computer-aided drug design technology and cellular experiments to screen for peptide S4 with high affinity and specificity for the human ACE2 receptor through structural analysis of SARS-CoV-2 and ACE2 interactions. Cellular experiments revealed that peptide S4 effectively inhibited SARS-CoV-2 and HCoV-NL63 viruses from infecting host cells and was safe for cells at effective concentrations. Based on these findings, peptide S4 may be a potential pharmaceutical agent for clinical application in the treatment of the ongoing SARS-CoV-2 pandemic.

In silico discovery of potential PPI inhibitors for anti-lung cancer activity by targeting the CCND1-CDK4 complex via the P21 inhibition mechanism.

Non-Small Cell Lung Cancer (NSCLC) is a prevalent and deadly form of lung cancer worldwide with a low 5-year survival rate. Current treatments have limitations, particularly for advanced-stage patients. P21, a protein that inhibits the CCND1-CDK4 complex, plays a crucial role in cell proliferation. Computer-Aided Drug Design (CADD) based on pharmacophores can screen and design PPI inhibitors targeting the CCND1-CDK4 complex. By analyzing known inhibitors, key pharmacophores are identified, and computational methods are used to screen potential PPI inhibitors. Molecular docking, pharmacophore matching, and structure-activity relationship studies optimize the inhibitors. This approach accelerates the discovery of CCND1-CDK4 PPI inhibitors for NSCLC treatment. Molecular dynamics simulations of CCND1-CDK4-P21 and CCND1-CDK4 complexes showed stable behavior, comprehensive sampling, and P21's impact on complex stability and hydrogen bond formation. A pharmacophore model facilitated virtual screening, identifying compounds with favorable binding affinities. Further simulations confirmed the stability and interactions of selected compounds, including 513457. This study demonstrates the potential of CADD in optimizing PPI inhibitors targeting the CCND1-CDK4 complex for NSCLC treatment. Extended simulations and experimental validations are necessary to assess their efficacy and safety.

Discovery of novel CXCR4 inhibitors for the treatment of inflammation by virtual screening and biological evaluation

C-X-C chemokine receptor type 4 (CXCR4) exerts considerable influence on the pathogenesis of inflammatory disorders and offers a potent avenue for drug intervention. This research utilizes a hybrid virtual screening methodology constructed using computer-aided drug design to discover novel CXCR4 inhibitors for the treatment of inflammation. First, a compound library was screened by Lipinski's five rules and adsorption, distribution, metabolism, excretion and toxicity properties. Second, the HypoGen algorithm was used in constructing a 3D-QSAR pharmacophore model and verify it layer by layer, and the obtained optimal pharmacophore 1 (Hypo 1) was used as a 3D query for compound screening. Then, hit compounds were obtained through molecular docking (Libdock and CDOCKER). The toxicity of the compounds to MDA-MB-231 cells was evaluated in vitro, and their binding affinity to the target was evaluated according to how they compete with 12G5 antibody for CXCR4 on the surfaces of the MDA-MB-231 cells. Compound Hit14 showed the strongest binding affinity among the hit compounds and inhibited cell migration and invasion in Matrigel invasion and wound healing assay at a concentration of 100 nM, demonstrating a better effect than AMD3100. Western Blot experiments further showed that Hit14 blocked the CXCR4/CXCL12-mediated phosphorylation of Akt. Meanwhile, cellular thermal displacement assay analysis showed that CXCR4 protein bound to Hit14 had high thermal stability. Finally, through in vivo experiments, we found that Hit14 inhibited mouse ear inflammation and reduced ear swelling and damage. Therefore, Hit14 is a promising drug for the further development of CXCR4 inhibitors for inflammation treatment.

Targeted Screening of Curcumin Derivatives as Pancreatic Lipase Inhibitors Using Computer-Aided Drug Design.

Curcumin has demonstrated promising preclinical antiobesity effects, but its low bioavailability makes it difficult to exert its full effect at a suitable dose. The objective of this study was to screen curcumin derivatives with enhanced bioavailability and lipid-lowering activity under the guidance of computer-aided drug design (CADD). CAAD was used to perform virtual assays on curcumin derivatives to assess their pharmacokinetic properties and effects on pancreatic lipase activity. Subsequently, 19 curcumin derivatives containing 5 skeletons were synthesized to confirm the above virtual assay. The in vitro pancreatic lipase inhibition assay was employed to determine the half-maximal inhibitory concentration (IC50) of these 19 curcumin derivatives. Based on CADD analysis and in vitro pancreatic lipase inhibition, 2 curcumin derivatives outperformed curcumin in both aspects. Microscale thermophoresis (MST) experiments were employed to assess the binding equilibrium constants (K d) of the aforementioned 2 curcumin derivatives, curcumin, and the positive control drug with pancreatic lipase. Through virtual screening utilizing a chemoinformatics database and molecular docking, 6 derivatives of curcumin demonstrated superior solubility, absorption, and pancreatic lipase inhibitory activity compared to curcumin. The IC50 value for 1,7-bis(4-hydroxyphenyl)heptane-3,5-dione (C4), which displayed the most effective inhibitory effect, was 42.83 μM, while the IC50 value for 1,7-bis(4-hydroxy-3-methoxyphenyl)heptane-3,5-dione (C6) was 98.62 μM. On the other hand, the IC50 value for curcumin was 142.24 μM. The MST experiment results indicated that the K d values of C4, C6, and curcumin were 2.91, 18.20, and 23.53 μM, respectively. The results of the activity assays exhibited a relatively high degree of concordance with the outcomes yielded by CADD screening. Under the guidance of CADD, the targeted screening of curcumin derivatives with excellent properties in this study exhibited high-efficiency and low-cost benefits.

In silico and in vitro study of bioactive compounds from Allium sativum with PTEN: A novel target and promising source for cancer diagnostic potentials

IntroductionCancer remains a significant global health issue, necessitating innovative management strategies. The tumor suppressor protein PTEN, due to its pivotal role in cancer development, is a promising target for therapeutic intervention. Materials and MethodsWe used dried Allium sativum (garlic) seed extract to discover phytocompounds that could inhibit PTEN in cancer. We conducted qualitative tests to determine the phytochemical composition of the extract and employed a DPPH radical scavenging assay to evaluate its antioxidant activity. Computational methods were used to explore the impact of Allium sativum and its constituents on PTEN. This involved predicting and validating 3D structures using the SWISS model and ITASSER, performing docking analysis with natural compounds from the Asinex database, and assessing the drug-likeness and ADMET characteristics of selected compounds using online tools. Molecular dynamics simulations (MDS) were conducted to examine protein–ligand interactions, and the results were analyzed to understand complex stability and fluctuations. ResultsA study on Allium sativum revealed the presence of diverse phytochemicals in its seeds and leaves. The leaves contained alkaloids and saponins, while the methanol extract of the leaves exhibited the highest flavonoid and phenolic contents. This methanol extract demonstrated potent antioxidant activity and significantly inhibited cytotoxicity. Through computer-aided drug design, we identified potential anticancer drugs derived from natural botanicals that target the PTEN protein. The virtual screening process led to the discovery of three lead compounds exhibiting high binding affinities and interactions with PTEN. Molecular docking and MDS indicated the strong binding energy and favorable drug-like properties of these compounds. While the protein exhibited some flexibility, the ligand–protein complex remained stable. ConclusionThis study contributes to the expanding literature on cancer drug discovery and emphasizes the importance of investigating natural plant compounds in the search for novel and effective cancer treatments.

Identifying the lead molecule from Carica papaya using computer-aided drug design technique against streptococcus pneumoniae

Identifying the lead molecule from Carica papaya using computer-aided drug design technique against streptococcus pneumoniae

Targeting small druggable compounds against 3RZE histamine H1 receptor as potential of anti-allergic drug applying molecular modeling approach

BackgroundAllergic disorders, prevalent global health concerns, afflict a substantial portion of the world’s population. These maladies result from an exaggerated immune system response to ordinarily innocuous substances, such as pollen, dust mites, and specific dietary components. Clinical manifestations of this heightened immune response include itching, swelling, and respiratory impairment, often accompanied by releasing mediators like histamine. The pathophysiological mechanisms of allergy disorders are intricate, arising from a complex interplay between genetic and environmental factors. While clinical presentations may vary, all allergy conditions share a common foundation in the dysregulated immune response to allergens.ResultThe current aim of this study was to identify innovative anti-allergic agents capable of inhibiting histamine and effectively mitigating allergic reactions by utilizing the computer-aided drug design approach by discovery studio (DS) 2022 v 23.1.1 package. The overarching aim was identifying potential drug candidates targeting the active site within the histamine H1 receptor complex; therefore, a collection of 4000 small druggable compounds was curated from ZINC, PubChem, and DRUG BANK databases sources. Four compounds appeared as promising candidates after assessing docking scores and binding energies. Notably, Compound ID 34154, recognized as tymazoline, showed the highest affinity for the H1 receptor of 3RZE, suggesting it may be the most promising choice for more research. Further chemoinformatic and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analyses were conducted to assess the drug-like qualities of this chosen molecule. In addition, bioisosteric substitution techniques were employed to enhance tymazoline’s ADMET characteristics.ConclusionTymazoline shows strong binding affinity with 3RZE and verified all the drug-likeness criteria to inhibit the allergic disorders. Furthermore, molecular dynamics (MD) studies corroborated tymazoline’s potential as an anti-allergic agent, demonstrating contact between the ligand and the receptor that is well defined and stable.

BIOMX-DB: A web application for the BIOFACQUIM natural product database.

Natural product databases are an integral part of chemoinformatics and computer-aided drug design. Despite their pivotal role, a distinct scarcity of projects in Latin America, particularly in Mexico, provides accessible tools of this nature. Herein, we introduce BIOMX-DB, an open and freely accessible web-based database designed to address this gap. BIOMX-DB enhances the features of the existing Mexican natural product database, BIOFACQUIM, by incorporating advanced search, filtering, and download capabilities. The user-friendly interface of BIOMX-DB aims to provide an intuitive experience for researchers. For seamless access, BIOMX-DB is freely available at www.biomx-db.com.

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors.

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papainlike endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

Discovery of Novel Spike Inhibitors against SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the current coronavirus disease pandemic. With the rapid evolution of variant strains, finding effective spike protein inhibitors is a logical and critical priority. Angiotensin-converting enzyme 2 (ACE2) has been identified as the functional receptor for SARS-CoV-2 viral entry, and thus related therapeutic approaches associated with the spike protein-ACE2 interaction show a high degree of feasibility for inhibiting viral infection. Our computer-aided drug design (CADD) method meticulously analyzed more than 260,000 compound records from the United States National Cancer Institute (NCI) database, to identify potential spike inhibitors. The spike protein receptor-binding domain (RBD) was chosen as the target protein for our virtual screening process. In cell-based validation, SARS-CoV-2 pseudovirus carrying a reporter gene was utilized to screen for effective compounds. Ultimately, compounds C2, C8, and C10 demonstrated significant antiviral activity against SARS-CoV-2, with estimated EC50 values of 8.8 μM, 6.7 μM, and 7.6 μM, respectively. Using the above compounds as templates, ten derivatives were generated and robust bioassay results revealed that C8.2 (EC50 = 5.9 μM) exhibited the strongest antiviral efficacy. Compounds C8.2 also displayed inhibitory activity against the Omicron variant, with an EC50 of 9.3 μM. Thus, the CADD method successfully discovered lead compounds binding to the spike protein RBD that are capable of inhibiting viral infection.

Discovery of HDM2010, a highly potent PTPN2 inhibitor for immunotherapy.

e15021 Background: Protein tyrosine phosphatase non-receptor type 2 (PTPN2), a negative regulator of JAK–STAT pathways that plays a crucial role in the immune system, has been identified as a promising target for immunotherapy. Genetic deletion of PTPN2 in either tumor cells or host immune cells promotes the anti-tumor immunity. In addition, PTPN2 inhibitors, alone or in combination with anti-PD1 therapy, significantly reduced tumor growth and enhanced survival in several subcutaneous tumour models. These findings demonstrate that PTPN2 inhibitors may have clinical benefits for immunotherapy-resistant tumors. Herein, the discovery of a series of novel PTPN2 inhibitors and their implications for immunotherapy are reported. Methods: We have successfully developed a series of small molecule inhibitors that specifically target PTPN2/N1. Molecular design was based on a novel backbone and incorporating both structure-based drug design (SBDD) and computational chemistry techniques. To evaluate the efficacy of our inhibitors, PTPN2 phosphatase activity was monitored using the small-molecule substrate DiFMUP in a fluorescence assay format in vitro. Tumor cells were treated with IFNγ and the cell viability was measured. Pharmacokinetic studies were conducted in mice, with serial blood samples analyzed by liquid chromatography−mass spectrometry (LC−MS)/MS. Pharmacokinetic parameters were calculated by noncompartmental analysis. Results: Based on the co-crystal structure of this target reported and multiple previous research projects, we have designed a multitude of novel compounds with the employment of computer-aided drug design (CADD) and scaffold hopping. Through a comprehensive evaluation of the in vitro and in vivo parameters, we have identified a series of compounds displaying potent PTPN2 inhibition (IC50 < 10 nM). Moreover, the compounds displayed remarkable efficacy in killing tumor cells to IFNγ treatment (IC50 < 100 nM). Artificial intelligence was utilized to conduct assessment and prediction of ADME properties, improving the drug-ability including permeability and physicochemical properties. Currently, a series of compounds demonstrating outstanding potency and good pharmacokinetic profiles, with lower hERG blockage (hERG IC50 > 30 µM), were synthesized and characterized. Among these hit compounds, a leading compound with PTPN2 IC50 = 1.1 nM and tumor cells IC50 = 79.5 nM was identified for further optimization and evaluation. Conclusions: We reported our work on discovering HDM2010, in which a series of potent PTPN2 inhibitors with prominent PK and safety profile were synthesized and characterized. Further optimization including in vivo experiments will lead to a preclinical candidate (PCC) selection.

Targeting Oral Cancer: In Silico Docking Studies of Phytochemicals on Oncogenic Molecular Markers.

Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. Oral carcinogenesis is a complex, multistep process in which genetic events within signal transduction pathways governing normal cellular physiology are quantitatively or qualitatively altered. There are various molecular targets like Cyclin D and PI3k- alpha Ras Binding Domain receptor protein involved in the pathogenesis of Oral Squamous Cell Carcinoma. The aim of the study is to demonstrate the computer aided drug design to identify a potent natural molecule for targeting cyclin D4 and PI3K RAS binding protein. Target selection (Cyclin D1 and PI3K-alpha Ras Binding Domain receptor) was done and structures were derived from protein data bank. Ligands (Apigenin, Chrysoeriol and Luteolin) selection was done and structure derived. Final docking was performed by Autodock. From the docking results it can be seen that luteolin has the highest binding energy (-5.45) with the Cyclin D receptor molecule followed by Chrysoeriol (-4.99) and Apigenin (-4.96). The binding energies of the ligands against PI3K-alpha Ras Binding Domain receptors were Apigenin (-4.51), Chrysoeriol (-4.6) and Luteolin (-4.56). The study concludes that all the three selected ligands possess high binding energy with both the target proteins involved in carcinogenesis with highest binding energy possessed by Luteolin against the Cyclin D receptor and by Chrysoeriol against PI3K-RAS binding protein. Thus their activity can be utilized to derive potential Anti-cancer therapeutic drugs.

Discovery of oral chemotherapeutic reversal agents for treating multidrug resistance cancer

The major limitation of cancer treatment is multidrug resistance (MDR), which leads to the inactivation of chemotherapeutic drugs and greater than 90% mortality. To solve this ordeal, we applied ligand-based drug design and bioiosteric replacement strategy from an indazole to a pyrazole ring to discover compounds 27 and 43 with good potential for reversing drug resistance in combination with paclitaxel, and their reversal fold values were 53.2 and 51.0 at 5 μM, respectively, against an MDR cancer cell line (KBvin). Based on the PK profile results, we selected compound 43 with a longer half-life for mechanistic and animal experiments. Combination treatment with compound 43 and paclitaxel-induced apoptosis and enhanced subG1 by decreasing mitochondrial membrane potential in KBvin cells. In addition, 43 also inhibited P-gp function by interfering with ATPase activity. Meanwhile, cotreatment with compound 43 and paclitaxel significantly suppressed tumor growth (TGI = 55.5%) at a dose of 200 mg/kg (PO) in a xenograft model and showed no obvious liver or kidney toxicity by H&E staining. Overall, compound 43 may serve as a safe and effective oral resistance reversal chemotherapeutic agent.

ADME Studies of Phytochemicals in Stachys pilifera Benth plant species

Medicinal chemistry is a constantly changing and developing field. It is a successful and prosperous branch due to the new technologies, the changing complexities of medicine, and the expansion of medicinal chemistry knowledge. In drug design, many aspects need to be checked. The first step in drug development is discovering when new insights and technologies become available. Discovery is possible to find new treatments for diseases. This can begin with thousands of different computational methods, including under Computer-aided drug design (CADD). It provides a no. of tools and techniques that assist the various stages of drug design which can minimize the cost of drug research and development time. For this particular project, we use the SwissADME web tool it is a free web tool, easily assessable and convenient. It can characterize any chemical compound within a min. it allows you to compute physiochemical descriptors also to predict ADME parameters, pharmacokinetic properties, drug like nature of our compound, and medicinal chemistry friendliness of many small molecules to support drug discovery. For ADME analysis we chose a plant in which the isolation of its chemical compound is already done. i.e. stachys pilifera benth plant species. We did the ADME study of their chemical compounds using SwissADME and concluded its drug-like nature, its bioavailability, its synthetic accessibility, and many medicinal properties. We can say that Swiss ADME is the Asset for medicinal chemistry.

FvOshC Is a Key Global Regulatory Target in Fusarium verticillioides for Fumonisin Biosynthesis and Disease Control.

Fusarium verticillioides has a substantial impact on maize production, commonly leading to maize ear rot and the production of fumonisin, a mycotoxin that poses health risks to both humans and animals. Currently, there is a lack of molecular targets for preventing the disease and controlling the toxin. The biological functions of oxysterol-binding proteins (OSBP) in filamentous fungi remain unclear. In this research, 7 oxysterol-binding protein-related proteins were identified in F. verticillioides, and these proteins were obtained through prokaryotic expression and purification. FvOshC was identified as the specific protein that binds to ergosterol through fluorescence titration. Gene knockout complementation techniques confirmed that FvOSHC plays a positive role, establishing it as a novel global regulatory protein involved in the pathogenicity and FB1 biosynthesis in F. verticillioides. Additionally, the interaction between FvOshC and FvSec14 was identified using yeast two-hybrid techniques. Moreover, computer-aided drug design technology was utilized to identify the receptor molecule Xanthatin based on FvOshC. The inhibitory effect of Xanthatin on the growth of F. verticillioides and the synthesis of FB1 was significantly demonstrated. These findings provide valuable insights that can aid in the management of mycotoxin pollution.

Computer-Aided Design of Peptides for Suppressing Liquid-Liquid Phase Separation

Liquid-liquid phase separation (LLPS) is a fundamental process in cellular biology, critical for the formation of membraneless organelles. Dysregulation of LLPS is linked to various diseases, highlighting the need for targeted therapeutics. Traditional computer-aided drug design has struggled to address LLPS due to the involvement of intrinsically disordered regions (IDRs). In this study, we identified a peptide derived from the low-complexity (LC) region of FUS, with serine-to-glutamic acid mutations (S2E), which significantly suppresses LC phase separation. GPU-based molecular dynamics simulations reveal that the interaction between S2E and LC is essential for their suppressive effect. Inspired by this finding, we proposed a strategy that employs multivalent interactions to design peptides capable of inhibiting phase separation. As a proof of concept, we engineered peptides that combine the LARKS domain from FUS-LC with an E-rich sequence from HSP90, yielding two potent peptides. These peptides effectively inhibit phase separation, as validated by both computer simulation and experimental data. Our findings provide a new avenue for the primary design of peptides aimed at controlling phase separation and protein aggregation, with potential applications in therapeutic development for LLPS-related diseases.

Core proteome mediated subtractive approach for the identification of potential therapeutic drug target against the honeybee pathogen Paenibacillus larvae.

American foulbrood (AFB), caused by the highly virulent, spore-forming bacterium Paenibacillus larvae, poses a significant threat to honey bee brood. The widespread use of antibiotics not only fails to effectively combat the disease but also raises concerns regarding honey safety. The current computational study was attempted to identify a novel therapeutic drug target against P. larvae, a causative agent of American foulbrood disease in honey bee. We investigated effective novel drug targets through a comprehensive in silico pan-proteome and hierarchal subtractive sequence analysis. In total, 14 strains of P.larvae genomes were used to identify core genes. Subsequently, the core proteome was systematically narrowed down to a single protein predicted as the potential drug target. Alphafold software was then employed to predict the 3D structure of the potential drug target. Structural docking was carried out between a library of phytochemicals derived from traditional Chinese flora (n>36,000) and the potential receptor using Autodock tool 1.5.6. Finally, molecular dynamics (MD) simulation study was conducted using GROMACS to assess the stability of the best-docked ligand. Proteome mining led to the identification of Ketoacyl-ACP synthase III as a highly promising therapeutic target, making it a prime candidate for inhibitor screening. The subsequent virtual screening and MD simulation analyses further affirmed the selection of ZINC95910054 as a potent inhibitor, with the lowest binding energy. This finding presents significant promise in the battle against P. larvae. Computer aided drug design provides a novel approach for managing American foulbrood in honey bee populations, potentially mitigating its detrimental effects on both bee colonies and the honey industry.