Molecular Energy Calculations Research Articles

Deciphering Mutational Impacts on c-Src-HK2 Interaction in Colorectal Cancer Progression, and Identification of Potential Phytocompounds Inhibitors: A Molecular Simulation and Free Energy Calculation Approach.

Colorectal cancer (CRC) stands as the third most widespread cancer worldwide in both men and women, witnessing a concerning rise, especially in younger demographics. Abnormal activation of the Non-Receptor Tyrosine Kinase c-Src has been linked to the advancement of several human cancers, including colorectal, breast, lung, and pancreatic ones. The interaction between c-Src and Hexokinase 2 (HK2) triggers enzyme phosphorylation, significantly boosting glycolysis, and ultimately contributing to the development of CRC. The objectives of this study are to examine the influence of newly identified mutations on the interaction between c-Src and the HK2 enzyme and to discover potent phytocompounds capable of disrupting this interaction. In this study, we utilized molecular docking to check the effect of the identified mutation on the binding of c-Src with HK2. Virtual drug screening, MD simulation, and binding free energy were employed to identify potent drugs against the binding interface of c-Src and HK2. Among these mutations, six (W151C, L272P, A296S, A330D, R391H, and P434A) were observed to significantly disrupt the stability of the c-Src structure. Additionally, through molecular docking analysis, we demonstrated that the mutant forms of c-Src exhibited high binding affinities with HK2. The wildtype showed a docking score of -271.80 kcal/mol, while the mutants displayed scores of -280.77 kcal/mol, -369.01 kcal/mol, -324.41 kcal/mol, -362.18 kcal/mol, 266.77 kcal/mol, and -243.28 kcal/mol for W151C, L272P, A296S, A330D, R391H, and P434A respectively. Furthermore, we identified five lead phytocompounds showing strong potential to impede the binding of c-Src with HK2 enzyme, essential for colon cancer progression. These compounds exhibit robust bonding with c-Src with docking scores of -7.37 kcal/mol, -7.26 kcal/mol, -6.88 kcal/mol, -6.81 kcal/mol, and -6.73 kcal/mol. Moreover, these compounds demonstrate dynamic stability, structural compactness, and the lowest residual fluctuation during MD simulation. The binding free energies for the top five hits (-42.44±0.28 kcal/mol, -28.31±0.25 kcal/mol, -34.95±0.44 kcal/mol, -38.92±0.25 kcal/mol, and -30.34±0.27 kcal/mol), further affirm the strong interaction of these drugs with c-Src which might impede the cascade of events that drive the progression of colon cancer. Our findings serve as a promising foundation, paving the way for future discoveries in the fight against colorectal cancer.

Electron Binding Energies of Open-Shell Species from Diagonal Electron-Propagator Self-Energies with Unrestricted Hartree-Fock Spin-Orbitals.

For closed-shell molecules, valence electron binding energies may be calculated accurately and efficiently with ab initio electron-propagator methods that have surpassed their predecessors. Advantageous combinations of accuracy and efficiency range from cubically scaling methods with mean errors of 0.2 eV to quintically scaling methods with mean errors of 0.1 eV or less. The diagonal self-energy approximation in the canonical Hartree-Fock basis is responsible for the enhanced efficiency of several methods. This work examines the predictive capabilities of diagonal self-energy approximations when they are generalized to the canonical spin-orbital basis of unrestricted Hartree-Fock (UHF) theory. Experimental data on atomic electron binding energies and high-level, correlated calculations in a fixed basis for a set of open-shell molecules constitute standards of comparison. A review of the underlying theory and analysis of numerical errors lead to several recommendations for the calculation of electron binding energies: (1) In calculations that employ the diagonal self-energy approximation, Koopmans's identity for UHF must be qualitatively correct. (2) Closed-shell reference states are preferable to open-shell reference states in calculations of molecular ionization energies and electron affinities. (3) For molecular electron binding energies between doublets and triplets, calculations of electron detachment energies are more accurate and efficient than calculations of electron attachment energies. When these recommendations are followed, mean absolute errors increase by approximately 0.05 eV with respect to their counterparts obtained with closed-shell reference orbitals.

Uncovering potential CDK9 inhibitors from natural compound databases through docking-based virtual screening and MD simulations.

Cyclin-dependent kinase 9 (CDK9) plays a significant role in gene regulation and RNA polymerase II transcription under basal and stimulated conditions. The upregulation of transcriptional homeostasis by CDK9 leads to various malignant tumors and therefore acts as a valuable drug target in addressing cancer incidences. Ongoing drug development endeavors targeting CDK9 have yielded numerous clinical candidate molecules currently undergoing investigation as potential CDK9 modulators, though none have yet received Food and Drug Administration (FDA) approval. In this study, we employ in silico approaches including the molecular docking and molecular dynamics simulations for the virtual screening over the natural compounds library to identify novel promising selective CDK9 inhibitors. The compounds derived from the initial virtual screening were subsequently employed for molecular dynamics simulations and binding free energy calculations to study the compound's stability under virtual physiological conditions. The first-generation CDK inhibitor Flavopiridol was used as a reference to compare with our novel hit compound as a CDK9 antagonist. The 500-ns molecular dynamics simulation and binding free energy calculation showed that two natural compounds showed better binding affinity and interaction mode with CDK9 receptors over the reference Flavopiridol. They also showed reasonable figures in the predicted absorption, distribution, metabolism, excretion, and toxicity (ADMET) calculations as well as in computational cytotoxicity predictions. Therefore, we anticipate that the proposed scaffolds could contribute to developing potential and selective CDK9 inhibitors subjected to further validations.

Dynamic Second Harmonic Imaging of Proton Translocation Through Water Needles in Lipid Membranes.

Proton translocation through lipid membranes is a fundamental process in the field of biology. Several theoretical models have been developed and presented over the years to explain the phenomenon, yet the exact mechanism is still not well understood. Here, we show that proton translocation is directly related to membrane potential fluctuations. Using high-throughput wide-field second harmonic (SH) microscopy, we report apparently universal transmembrane potential fluctuations in lipid membrane systems. Molecular simulations and free energy calculations suggest that H+ permeation proceeds predominantly across a thin, membrane-spanning water needle and that the transient transmembrane potential drives H+ ions across the water needle. This mechanism differs from the transport of other cations that require completely open pores for transport and follows naturally from the well-known Grotthuss mechanism for proton transport in bulk water. Furthermore, SH imaging and conductivity measurements reveal that the rate of proton transport depends on the structure of the hydrophobic core of bilayer membranes.

A Benchmark Test of High-Throughput Atomistic Modeling for Octa-Acid Host–Guest Complexes

Years of massive applications of high-throughput atomistic modeling tools such as molecular docking and end-point free energy calculations in the drug industry and academic exploration have made them indispensable parts of hierarchical screening. While the similarities between host–guest and protein–ligand complexes lead to the direct extension of techniques for protein–ligand screening to host–guest systems, the practical performance of these hit identification tools remains unclear in host-–-guest binding. Recent reports on specific host–guest complexes suggest that the experience on the accuracy ladder accumulated from protein–ligand cases could be invalid in host–guest complexes, which makes it an urgent need to perform a systematic benchmark to secure solid numerical supports and guidance of practical setups. Concerning molecular docking, there still lacks a comprehensive benchmark considering popular docking programs. As for end-point reranking, quantitative and rigorous free energy estimation via end-point formulism requires establishing statistically meaningful measurements of uncertainties due to finite sampling, which is neglected or underestimated by a significant portion in almost all main-stream applications. Further, a face-to-face comparison between different screening tools is required for the design of a hierarchical workflow. To fill the above-mentioned critical gaps, in this work, using a dataset containing tens of host–guest complexes involving basket-like macromolecular hosts from the octa acid family, we extensively benchmark seven academic docking protocols and perform post-docking end-point rescoring with twenty protocols. The resulting comprehensive benchmark provides conclusive pictures of the practical value of docking and end-point screening in OA host–guest binding.

Identification of Novel Thiazolidinedione Based Pancreatic Lipase Inhibitors for the Management of Obesity Using In Silico Approach

AbstractAccumulation of excessive fat in the body is an indication of obesity, which is becoming an alarming health concern around the world. Pancreatic lipase has been found to have a promising role in the digestion of dietary fats thereby inhibition of pancreatic lipase can be a therapeutic strategy for the management of obesity. Literature showed the anti‐pancreatic lipase potential of thiazolidinediones thereby the current study is designed to identify substituted thiazolidinedione‐based molecules as pancreatic lipase inhibitors. A 3D QSAR model with R2 value of 0.7283 and Stability of 0.843 was generated to determine the nature of substitution necessary for the inhibitory activity. Based on the generated 3D QSAR, the activity of 352 novel‐designed molecules was predicted and further screened through pharmacokinetic parameters. These compounds were docked into the binding site of the pancreatic lipase which led to the identification of 14 hit molecules with dock score between −9.523 to −7.604 kcal/mol. The molecular simulation studies and free energy calculation of the hit compounds i. e. 87 and 80 revealed good stability and binding in the active site of pancreatic lipase. The current study provides 14 hit molecules, which can be synthesized and further evaluated to explore their anti‐pancreatic lipase inhibitory activity.

Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre-melting of Nanoclusters.

Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure-activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre-melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre-melting of shell atoms, there exists a non-linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad-atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes.

Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre‐melting of Nanoclusters

AbstractDue to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure–activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre‐melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre‐melting of shell atoms, there exists a non‐linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad‐atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes.

Gaussians for Electronic and Rovibrational Quantum Dynamics.

The assumptions underpinning the adiabatic Born-Oppenheimer (BO) approximation are broken for molecules interacting with attosecond laser pulses, which generate complicated coupled electronic-nuclear wave packets that generally will have components of electronic and dissociation continua as well as bound-state contributions. The conceptually most straightforward way to overcome this challenge is to treat the electronic and nuclear degrees of freedom on equal quantum-mechanical footing by not invoking the BO approximation at all. Explicitly correlated Gaussian (ECG) basis functions have proved successful for non-BO calculations of stationary molecular states and energies, reproducing rovibrational absorption spectra with very high accuracy. In this Article, we present a proof-of-principle study of the ability of fully flexible ECGs (FFECGs) to capture the intricate electronic and rovibrational dynamics generated by short, high-intensity laser pulses. By fitting linear combinations of FFECGs to accurate wave function histories obtained on a large real-space grid for a regularized 2D model of the hydrogen atom and for the 2D Morse potential, we demonstrate that FFECGs provide a very compact description of laser-driven electronic and rovibrational dynamics.

Design, synthesis, biological assessment and molecular modeling studies of novel imidazothiazole-thiazolidinone hybrids as potential anticancer and anti-inflammatory agents

A new series of imidazothiazole derivatives bearing thiazolidinone moiety (4a-g and 5a-d) were designed, synthesized and evaluated for potential epidermal growth factor receptor (EGFR) kinase inhibition, anticancer and anti-inflammatory activity, cardiomyopathy toxicity and hepatotoxicity. Compound 4c inhibited EGFR kinase at a concentration of 18.35 ± 1.25 µM, whereas standard drug erlotinib showed IC50 value of 06.12 ± 0.92 µM. The molecular docking, dynamics simulation and MM-GBSA binding energy calculations revealed strong interaction of compound 4c with binding site of EGFR. The synthesized compounds were evaluated for their anticancer activity by MTT assay against three human cancer cell lines A549 (Lung), MCF-7 (Breast), HCT116 (Colon), one normal human embryonic kidney cell line HEK293 and also for their EGFR kinase inhibitory activity. Few compounds of the series (4a, 4b, 4c) showed promising growth inhibition against all the tested cancer cell lines and against EGFR kinase. Among these, compound 4c was found to be most active and displayed IC50 value of 10.74 ± 0.40, 18.73 ± 0.88 against cancer cell lines A549 and MCF7 respectively whereas it showed an IC50 value of 96.38 ± 1.79 against HEK293 cell line indicating lesser cytotoxicity for healthy cell. Compounds 4a, 4b and 4c were also examined for their apoptosis inducing potential through AO/EB dual staining assay and it was observed that their antiproliferative activity against A549 cells is mediated via induction of apoptosis. Cardiomyopathy studies showed normal cardiomyocytes with no marked sign of pyknotic nucleus of compounds 4b and 4c. Hepatotoxicity studies of compounds 4b and 4c also showed normal architecture of hepatocytes. Compounds 4a-g and 5a-d were also evaluated for their in-vitro anti-inflammatory activity by protein albumin denaturation assay. Among the tested compounds 4a-d and 5a-b showed promising activity and were selected for in-vivo inflammatory activity against carrageenan rat paw edema test. Among these compounds, 4b was found to be most active in the series showing 84.94% inhibition, whereas the standard drug diclofenac sodium showed 84.57% inhibition. Compound 4b also showed low ulcerogenic potential and lipid peroxidation. Thus, compounds 4c and 4b could be a promising lead compounds for developing anticancer and anti-inflammatory agents with low toxicity and selectivity.

Targeting human progesterone receptor (PR), through pharmacophore-based screening and molecular simulation revealed potent inhibitors against breast cancer

Breast cancer, the prevailing malignant tumor among women, is linked to progesterone and its receptor (PR) in both tumorigenesis and treatment responsiveness. Despite thorough investigation, the precise molecular mechanisms of progesterone in breast cancer remain unclear. The human progesterone receptor (PR) serves as an essential therapeutic target for breast cancer treatment, warranting the rapid design of small molecule therapeutics that can effectively inhibit HPR. By employing cutting-edge computational techniques like molecular screening, simulation, and free energy calculation, the process of identifying potential lead molecules from natural products has been significantly expedited. In this study, we employed pharmacophore-based virtual screening and molecular simulations to identify natural product-based inhibitors of human progesterone receptor (PR) in breast cancer treatment. High-throughput molecular screening of traditional Chinese medicine (TCM) and zinc databases was performed, leading to the identification of potential lead compounds. The analysis of binding modes for the top five compounds from both database provides valuable structural insights into the inhibition of HPR for breast cancer treatment. The top five hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as HPR inhibitors.

Specifically targeting antimicrobial peptides for inhibition of Candidatus Liberibacter asiaticus.

Huanglongbing (citrus greening) is a plant disease putatively caused by the unculturable Gram-negative bacterium Candidatus Liberibacter asiaticus (CLas), and it has caused severe damage to citrus plantations worldwide. There are no definitive treatments for this disease, and conventional disease control techniques have shown limited efficacy. This work presents an in silico evaluation of using specifically targeting anti-microbial peptides (STAMPs) consisting of a targeting segment and an antimicrobial segment to inhibit citrus greening by inhibiting the BamA protein of CLas, which is an outer membrane protein crucial for bacterial viability. Initially, a set of peptides with a high affinity toward BamA protein were screened and evaluated via molecular docking and molecular dynamics simulations and were verified in vitro via bio-layer interferometry (BLI). In silico studies and BLI experiments indicated that two peptides, HASP2 and HASP3, showed stable binding to BamA. Protein structures for STAMPs were created by fusing known anti-microbial peptides (AMPs) with the selected short peptides. The binding of STAMPs to BamA was assessed using molecular docking and binding energy calculations. The attachment of high-affinity short peptides significantly reduced the free energy of binding for AMPs, suggesting that it would make it easier for the STAMPs to bind to BamA. Efficacy testing in vitro using a closely related CLas surrogate bacterium showed that STAMPs had greater inhibitory activity than AMP alone. In silico and in vitro results indicate that the STAMPs can inhibit CLas surrogate Rhizobium grahamii more effectively compared to AMPs, suggesting that STAMPs can achieve better inhibition of CLas, potentially via enhancing the site specificity of AMPs.

Orientation and Membrane Partition Free Energy of PeT-Based Voltage-Sensitive Dyes from Molecular Simulations.

Voltage measurement via small-molecule fluorescent indicators is a valuable approach in deciphering complex dynamics in electrically excitable cells. However, our understanding of various physicochemical properties governing the performance of fluorescent voltage sensors based on the photoinduced electron transfer (PeT) mechanism remains incomplete. Here, through extensive molecular dynamics and free energy calculations, we systematically examine the orientation and membrane partition of three PeT-based voltage-sensing VoltageFluor (VF) dyes in different lipid environment. We show that the symmetry of the molecular scaffold and the net charge of the hydrophilic headgroup of a given VF dye dominate its orientation and membrane partition, respectively. Our work provides a mechanistic understanding of the physical properties contributing to the voltage sensitivity, signal-to-noise ratio, as well as membrane distribution of VF dyes and sheds light onto rational design principles of PeT-based fluorescent probes in general.

Structure-guided engineering and molecular simulations to design a potent monoclonal antibody to target aP2 antigen for adaptive immune response instigation against type 2 diabetes.

Diabetes mellitus (DM) is recognized as one of the oldest chronic diseases and has become a significant public health issue, necessitating innovative therapeutic strategies to enhance patient outcomes. Traditional treatments have provided limited success, highlighting the need for novel approaches in managing this complex disease. In our study, we employed graph signature-based methodologies in conjunction with molecular simulation and free energy calculations. The objective was to engineer the CA33 monoclonal antibody for effective targeting of the aP2 antigen, aiming to elicit a potent immune response. This approach involved screening a mutational landscape comprising 57 mutants to identify modifications that yield significant enhancements in binding efficacy and stability. Analysis of the mutational landscape revealed that only five substitutions resulted in noteworthy improvements. Among these, mutations T94M, A96E, A96Q, and T94W were identified through molecular docking experiments to exhibit higher docking scores compared to the wild-type. Further validation was provided by calculating the dissociation constant (KD), which showed a similar trend in favor of these mutations. Molecular simulation analyses highlighted T94M as the most stable complex, with reduced internal fluctuations upon binding. Principal components analysis (PCA) indicated that both the wild-type and T94M mutant displayed similar patterns of constrained and restricted motion across principal components. The free energy landscape analysis underscored a single metastable state for all complexes, indicating limited structural variability and potential for high therapeutic efficacy against aP2. Total binding free energy (TBE) calculations further supported the superior performance of the T94M mutation, with TBE values demonstrating the enhanced binding affinity of selected mutants over the wild-type. Our findings suggest that the T94M substitution, along with other identified mutations, significantly enhances the therapeutic potential of the CA33 antibody against DM by improving its binding affinity and stability. These results not only contribute to a deeper understanding of antibody-antigen interactions in the context of DM but also provide a valuable framework for the rational design of antibodies aimed at targeting this disease more effectively.

Expression, purification, and characterization of transmembrane protein homogentisate solanesyltransferase

Homogentisate solanesyltransferase (HST) is a crucial enzyme in the plastoquinone biosynthetic pathway and has recently emerged as a promising target for herbicides. In this study, we successfully expressed and purified a stable and highly pure form of seven times transmembrane protein Chlamydomonas reinhardtii HST (CrHST). The final yield of CrHST protein obtained was 12.2 mg per liter of M9 medium. We evaluated the inhibitory effect on CrHST using Des-Morpholinocarbony Cyclopyrimorate (DMC) and found its IC50 value to be 3.63 ± 0.53 μM, indicating significant inhibitory potential. Additionally, we investigated the substrate affinity of CrHST with two substrates, determining the Km values as 22.76 ± 1.70 μM for FPP and 48.54 ± 3.89 μM for HGA. Through sequence alignment analyses and three-dimensional structure predictions, we identified conserved amino acid residues forming the active cavity in the enzyme. The results from molecular docking and binding energy calculations indicate that DMC has a greater binding affinity with HST compared to HGA. These findings represent substantial progress in understanding CrHST’s properties and potential for herbicide development.Key points• First high-yield transmembrane CrHST protein via E. coli system• Preliminarily identified active cavity composition via activity testing• Determined substrate and inhibitor modes via molecular docking

Generating Potential RET-Specific Inhibitors Using a Novel LSTM Encoder-Decoder Model.

The receptor tyrosine kinase RET (rearranged during transfection) plays a vital role in various cell signaling pathways and is a critical factor in the development of the nervous system. Abnormal activation of the RET kinase can lead to several cancers, including thyroid cancer and non-small-cell lung cancer. However, most RET kinase inhibitors are multi-kinase inhibitors. Therefore, the development of an effective RET-specific inhibitor continues to present a significant challenge. To address this issue, we built a molecular generation model based on fragment-based drug design (FBDD) and a long short-term memory (LSTM) encoder-decoder structure to generate receptor-specific molecules with novel scaffolds. Remarkably, our model was trained with a molecular assembly accuracy of 98.4%. Leveraging the pre-trained model, we rapidly generated a RET-specific-candidate active-molecule library by transfer learning. Virtual screening based on our molecular generation model was performed, combined with molecular dynamics simulation and binding energy calculation, to discover specific RET inhibitors, and five novel molecules were selected. Further analyses indicated that two of these molecules have good binding affinities and synthesizability, exhibiting high selectivity. Overall, this investigation demonstrates the capacity of our model to generate novel receptor-specific molecules and provides a rapid method to discover potential drugs.

Supramolecular self-assembly strategies of natural-based β-lactoglobulin modulating bitter perception of goat milk–derived bioactive peptides

Complete self-assembly and reassembly behavior of bitter peptide-protein necessitates multilevel theories that encompass phenomena ranging from the self-assembly of recombinant complex to atomic trajectories. An extension to the level of mechanism method was put forth, involves limited enzymatic digestion and bottom-up proteomics to dissect inherent heterogeneity within β-LG and β-LG-PPGLPDKY complex and uncover conformational and dynamic alterations occurring in specific local regions of the model protein. Bitter peptide PPGLPDKY spontaneously bound to IIAEKTK, IDALNENK, and YLLFCMENSAEPEQSLACQCLVR regions of β-LG in a 1:1 stoichiometric ratio to mask bitterness perception. Molecular dynamic simulation and free energy calculation provided time-varying atomic trajectories of the recombinant complex and found that a peptide was stabilized in the upper region of the hydrophobic cavity with the binding free energy of -30.56 kJ mol-1 through 4 hydrogen bonds (Glu74, Glu55, Lys69, and Ser116) and hydrophobic interactions (Asn88, Asn90, and Glu112). Current research aims to provide valuable physical insights into the macroscopic self-assembly behavior between proteins and bitter peptides, and the meticulous design of highly acceptable taste characteristics in goat milk products.

Exploring the natural products chemical space to abrogate the F3L-dsRNA interface of monkeypox virus to enhance the immune responses using molecular screening and free energy calculations.

Amid the ongoing monkeypox outbreak, there is an urgent need for the rapid development of effective therapeutic interventions capable of countering the immune evasion mechanisms employed by the monkeypox virus (MPXV). The evasion strategy involves the binding of the F3L protein to dsRNA, resulting in diminished interferon (IFN) production. Consequently, our current research focuses on utilizing virtual drug screening techniques to target the RNA binding domain of the F3L protein. Out of the 954 compounds within the South African natural compound database, only four demonstrated notable docking scores: -6.55, -6.47, -6.37, and -6.35kcal/mol. The dissociation constant (KD) analysis revealed a stronger binding affinity of the top hits 1-4 (-5.34, -5.32, -5.29, and -5.36kcal/mol) with the F3L in the MPXV. All-atom simulations of the top-ranked hits 1 to 4 consistently exhibited stable dynamics, suggesting their potential to interact effectively with interface residues. This was further substantiated through analyses of parameters such as radius of gyration (Rg), Root Mean Square Fluctuation, and hydrogen bonding. Cumulative assessments of binding free energy confirmed the top-performing candidates among all the compounds, with values of -35.90, -52.74, -28.17, and -32.11kcal/mol for top hits 1-4, respectively. These results indicate that compounds top hit 1-4 could hold significant promise for advancing innovative drug therapies, suggesting their suitability for both in vivo and in vitro experiments.

A novel fully human anti-NT-ANGPTL3 antibody from phage display library exhibits potent ApoB, TG, and LDL-C lowering activities in hyperlipidemia mice.

Dyslipidemia is characterized by elevated plasma levels of low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and TG-rich lipoprotein (TGRLs) in circulation, and is closely associated with the incidence and development of cardiovascular disease. Angiopoietin-like protein 3 (ANGPTL3) deficiency has been identified as a cause of familial combined hypolipidemia in humans, which allows it to be an important therapeutic target for reducing plasma lipids. Here, we report the discovery and characterization of a novel fully human antibody F1519-D95aA against N-terminal ANGPTL3 (NT-ANGPTL3), which potently inhibits NT-ANGPTL3 with a KD as low as 9.21 nM. In hyperlipidemic mice, F1519-D95aA shows higher apolipoprotein B (ApoB) and TG-lowering, and similar LDL-C reducing activity as compared to positive control Evinacumab (56.50% vs 26.01% decrease in serum ApoB levels, 30.84% vs 25.28% decrease in serum TG levels, 23.32% vs 22.52% decrease in serum LDLC levels, relative to vehicle group). Molecular docking and binding energy calculations reveal that the F1519-D95aA-ANGPTL3 complex (10 hydrogen bonds, -65.51 kcal/mol) is more stable than the Evinacumab-ANGPTL3 complex (4 hydrogen bonds, -63.76 kcal/mol). Importantly, F1519-D95aA binds to ANGPTL3 with different residues in ANGPTL3 from Evinacumab, suggesting that F1519-D95aA may be useful for the treatment of patients resistant to Evinacumab. In conclusion, F1519-D95aA is a novel fully human anti-NT-ANGPTL3 antibody with potent plasma ApoB, TG, and LDL-C lowering activities, which can potentially serve as a therapeutic agent for hyperlipidemia and relevant cardiovascular diseases.

Binding interactions of EDCs to human estrogen‐related receptor gamma deciphered by multiple molecular dynamics and energy calculations

AbstractDichloro‐diphenyl‐trichloroethane (DDT) analogs, classified as environmental endocrine disrupting compounds (EDCs), have been extensively employed as potent insecticides that can cause endocrine system disruption. However, the precise dynamic structural characteristics and interactions between human estrogen‐related receptor gamma (hERRγ) and DDT analogs are not yet fully understood. In this study, we comprehensively investigate the impact of these EDCs (DDT, dichloro‐diphenyl‐dichloroethane (DDD), 2,2‐bis(4‐chlorophenyl)ethanol (DDOH), O,P′‐DDT (2,4′‐DDT), 4,4′‐dichlorobenzophenone (DCBP), and 4‐hydrotamoxifen (4‐OHT) on the structural changes of hERRγ and their interaction mechanisms by employing multiple molecular dynamics (MD) simulations coupled with MM‐PBSA and SIE approaches. The consequence manifested that overall structures of these six complexes did not transform markedly, but these compounds can affect the local hERRγ structure, leading to essential changes in interactions with pivotal residues nearby L268, V313, L309, Y326, and F435. And van der Waals interactions are the key to how these EDCs interact with hERRγ. These outcomes contribute to our comprehension the risks of DDT analogues to human health.