CHARMM: the biomolecular simulation program

Prognostic, biological, and structural implications of FLT3-JMD point mutations in acute myeloid leukemia: an analysis of Alliance studies

The FLT3 gene frequently undergoes mutations in acute myeloid leukemia (AML), with internal tandem duplications (ITD) and tyrosine kinase domain (TKD) point mutations (PMs) being most common. Recently, PMs and deletions in the FLT3 juxtamembrane domain (JMD) have been identified, but their biological and clinical significance remains poorly understood. We analyzed 1660 patients with de novo AML and found FLT3-JMD mutations, mostly PMs, in 2% of the patients. Patients with FLT3-JMD mutations had a higher relapse rate and shorter disease-free survival than those with FLT3-TKD, whereas their relapse rate, disease-free and overall survival were not significantly different from those of FLT3-ITD-positive patients. In vitro experiments showed that FLT3-JMD PMs transformed hematopoietic cells and responded well to type I and II FLT3 inhibitors. Molecular dynamics simulations were used to explore the conformational changes of JMD PMs relative to wild-type FLT3. These mutations exhibited constrained domain motions with wider gate openings, potentially enhancing drug binding. Altered residue interactions and structural changes shed light on their unique functional mechanisms, with increased allosteric pathways suggesting reduced interactions with other residues. We conclude that patients with FLT3-JMD PMs represent uncommon but important subset with distinct molecular and biological features, and may benefit from FLT3 inhibitors.

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Stabilization of a protein by a single halogen-based aromatic amplifier

The utility of halogenation in protein design is investigated by a combination of quantitative atomistic simulations and experiment. Application to insulin is of complementary basic and translational interest. In a singly halogenated aromatic ring, regiospecific inductive effects were predicted to modulate multiple surrounding electrostatic (weakly polar) interactions, thereby amplifying changes in thermodynamic stability. In accordance with the simulations, we demonstrated stabilization of insulin by single halogen atoms at the ortho position of an invariant phenylalanine (2-F-PheB24, 2-Cl-PheB24, and 2-Br-PheB24; ΔΔGu = -0.5 to -1.0 kcal/mol) located at the edge of a protein crevice; corresponding meta and para substitutions had negligible effects. Although receptor-binding affinities were generally decreased (in accordance with packing of the native Phe at the hormone-receptor interface), the ortho-analogs retained biological activity in mammalian cells and in a rat model of diabetes mellitus. Further, the ortho-modified analogs exhibited enhanced resistance to fibrillation above room temperature in two distinct assays of physical stability. Regiospecific halo-aromatic stabilization may thus augment the shelf life of pharmaceutical insulin formulations under real-world conditions. This approach, extending principles of medicinal chemistry, promises to apply to a broad range of therapeutic proteins and vaccines whose biophysical stabilization would enhance accessibility in the developing world.

Fritillaria steroidal alkaloids and their multi-target therapeutic mechanisms: insights from network pharmacology

Medicinal Fritillaria herbs, known for their rich content of steroidal alkaloids, have emerged as promising candidates in the treatment of chronic diseases due to their diverse pharmacological properties. Leveraging advancements in network pharmacology and molecular docking, this study explores the multi-target mechanisms through which these alkaloids exert therapeutic effects. The integration of bioinformatics, systems biology, and pharmacology in drug discovery has provided insights into the molecular interactions and pathways influenced by Fritillaria steroidal alkaloids. This review synthesizes comprehensive literature from 1985 to 2024, revealing the potential of these compounds in addressing respiratory diseases, inflammation, and cancer. The integration of traditional Chinese medicine (TCM) with modern pharmacological techniques underscores the relevance of these compounds in next-generation drug discovery. While initial findings are promising, further empirical validation is necessary to fully harness the therapeutic potential of Fritillaria steroidal alkaloids.

Elemental evaluation, nutritional analysis, GC-MS analysis and ameliorative effects of Artocarpus communis J.R.Forst. & G.Forst. seeds' phytoconstituents on metabolic syndrome via in silico approach

The nutritional as well as beneficial effects of the Artocarpus communis seed on metabolic syndrome complications have not been studied. In this research, the aim was to investigate the nutritional composition and beneficial effects of Artocarpus communis seeds' phytoconstituents on the p53 core, fat mass and obesity-associated (FTO) protein and cytochrome P450 CYP11A1 domains. The elements and phytochemicals in the seed were determined through atomic absorption spectroscopy assay and gas chromatography-mass spectrometry (GC-MS) analysis, respectively. Also, the compounds detected were docked to the p53 core, FTO protein and cytochrome P450 CYP11A1 domains protein. Artocarpus communis seed contains sodium (7.824 ± 0.0134 ppm), magnesium (10.187 ± 0.0239 ppm) and iron (1.924 ± 0.0017), while zinc and cadmium were undetected. Phenolics and flavonoids were the most abundant phytochemicals in the seed. Phytoconstituents, such as pentadecanoic acid, hexadecanoic acid and methyl ester, possessing different therapeutic effects were identified via GC-MS analysis. In A. communis seed, 3-methyl-4-nitro-5-(1-pyrazolyl) pyrazole and phenanthrene were able to bind more peculiarly and specifically to the p53 core, FTO protein and cytochrome P450 CYP11A1 domains. One of the important processes that were hypothesized for the recovery of metabolic syndrome in affected victims is shown by the molecular dynamics analysis, which shows that the binding of these chemicals to the targeted structure stabilized the proteins. Therefore, Artocarpus communis seeds could be a new strategy for the management of metabolic syndrome.Communicated by Ramaswamy H. Sarma.

Esterase-2 mutant-based nanostructured amperometric biosensors for the selective determination of paraoxon (Neurotoxin)

Organophosphate pesticides (OPs) are causing non-selective inhibition in enzymatic bioreceptors, thus the enzymatic-inhibition-based traditional assays are not suitable for their specific detection in food and environmental samples. Accordingly, a selective nanostructured electrochemical biosensing system was designed using six mutants of the esterase-2 (EST2 protein) enzymes from A. acidocaldarius to be exploited as targeting bio-receptors for the specific detection of OPs. Each of the EST2 mutant enzymes was immobilized on disposable screen-printed electrodes modified with Aluminum oxide (Al2O3)/Copper (Cu) nanocomposite. Consequently, chronoamperometric assay was fully optimized, and cross-reactivity study was carried out using paraoxon, malathion and chlorpyrifos. The comparative cross-reactivity study was performed on the six mutant proteins in terms of inhibitory percentage over a wide range of pesticide concentrations. Eventually, a wide dynamic inhibition range was achieved while the limit of detection for the paraoxon toxicity was 0.01 nM and the limit of quantification was 0.05 nM. Finally, paraoxon was selectively determined using the newly developed EST-based biosensor in different spiked food samples.

An in-depth review of AI-powered advancements in cancer drug discovery

The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep learning and advanced data analytics, in accelerating key stages of the drug discovery process: target identification, drug design, clinical trial optimization, and drug response prediction. Cutting-edge tools such as DrugnomeAI and PandaOmics have made substantial contributions to therapeutic target identification, while AI's predictive capabilities are driving personalized treatment strategies. Additionally, advancements like AlphaFold highlight AI's capacity to address intricate challenges in drug development. However, the field faces significant challenges, including the management of large-scale genomic datasets and ethical concerns surrounding AI deployment in healthcare. This review underscores the promise of data-centric AI approaches and emphasizes the necessity of continued innovation and interdisciplinary collaboration. Together, AI and genomics are charting a path toward more precise, efficient, and transformative cancer therapeutics.

Cholinergic Inhibition and Antioxidant Potential of Gongronema latifolium Benth Leaf in Neurodegeneration: Experimental and In Silico Study

The use of Gongronema latifolium for the management of various forms of neurological disorders has generated a lot of interest in the need to further investigate its neurotherapeutic constituents. This work, therefore, focused on assessing the inhibitory potential of selected bioactive components derived from G. latifolium against key neurotherapeutic targets and oxidant species associated with neurodegeneration using in vitro analysis and biomolecular modelling. G. latifolium methanol extract (GLME), solvent partition, chromatographic fractions (A-F) of GLME and pregnane compounds (Iloneoside and marsectohexol) derived from fraction-B with the highest activity were investigated for in vitro acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and monoamine oxidase (MAO) inhibition in addition to their in vitro antioxidant activities. The interactions of iloneoside, marsectohexol, and reference drugs with human acetylcholinesterase, butyrylcholinesterase, and β-secretase (BACE-1) were further assessed using molecular docking, binding free energy calculations, cluster analysis, and molecular dynamics simulations. The GLME and fractions inhibited the activities of both acetylcholinesterase and butyrylcholinesterase in a dose-dependent manner. Iloneoside and marsectohexol exhibited in vitro concentration-dependent inhibitory activities against acetylcholinesterase (IC50 = 19.28, 184.9 µM, respectively) and butyrylcholinesterase (IC50 = 30.75, 43.4 µM, respectively). These compounds also possess ferric ion-reducing, hydroxyl, and superoxide radical-scavenging activities. Iloneoside had the highest docking scores of -9.8, -9.9 -9.4 Kcal for AChE, BChE, and BACE1, respectively. The stability of the interaction of the bioactive compounds with the catalytic residues of the protein targets was preserved in a 100 ns molecular dynamics simulation. Iloneoside, a rare pregnane glycoside, was identified as a neurotherapeutic constituent of G. latifolium leaf. Further studies are suggested to investigate the neurotherapeutic potential in animal models.

Elucidating the structure and function of a membrane-active plant protein domain using in silico mutagenesis

The Solanum tuberosum (common potato) plant specific insert (StPSI) is an antimicrobial protein domain that exhibits membrane-disrupting and membrane-fusing activity upon dimerization at acidic pH, activity proposed to involve electrostatic attraction and membrane anchoring mediated by specific positively-charged and conserved tryptophan residues, respectively. This study is the first to employ an in silico mutagenesis approach to clarify the structure-function relationship of a plant specific insert (PSI), where ten rationally-mutated StPSI variants were investigated using all-atom and coarse-grained molecular dynamics. The tryptophan (W) residue at position 18 (W18) of wild-type StPSI was predicted to confer structural flexibility to the dimer and mediate a partial separation of the assembled monomers upon bilayer contact, while residues including W77 and the lysine (K) residue at position 83 (K83) were predicted to stabilize secondary structure and influence association with the model membrane. Mechanisms predicted to influence StPSI-membrane association included the partial separation of assembled monomers on the bilayer surface, formation of a specific salt bridge, and membrane anchoring of hinge 2 residues. The findings suggested that the structure-function relationship of StPSI involved several mechanisms that may each be modulated by specific key residues, insights that may support efforts to develop PSI with tailored membrane association for novel applications in plant biotechnology and crop protection.

Unraveling atomic-scale mechanisms of GDP extraction catalyzed by SOS1 in KRAS-G12 and KRAS-D12 oncogenes

The guanine exchange factor SOS1 plays a pivotal role in the positive feedback regulation of the KRAS signaling pathway. Recently, the regulation of KRAS-SOS1 interactions and KRAS downstream effector proteins has emerged as a key focus in the development of therapies targeting KRAS-driven cancers. However, the detailed dynamic mechanisms underlying SOS1-catalyzed GDP extraction and the impact of KRAS mutations remain largely unexplored. In this study, we unveil and describe in atomic detail the primary mechanisms by which SOS1 facilitates GDP extraction from KRAS oncogenes. For GDP-bound wild-type KRAS (KRAS-G12), four critical amino acids (Lys811, Glu812, Lys939, and Glu942) are identified as essential for the catalytic function of SOS1. Notably, the KRAS-G12D mutation (KRAS-D12) significantly accelerates the rate of GDP extraction. The molecular basis of this enhancement are attributed to hydrogen bonding interactions between the mutant residue Asp12 and a positively charged pocket in the intrinsically disordered region (residues 807-818), comprising Ser807, Trp809, Thr810, and Lys811. These findings provide novel insights into SOS1-KRAS interactions and offer a foundation for developing anti-cancer strategies aimed at disrupting these mechanisms.

Apoptotic Potential of Iloneoside from Gongronema latifolium Benth against Prostate Cancer Cells Using In Vitro and In Silico Approach

Prostate cancer is a major cause of cancer-related mortality in men worldwide. The anti-proliferative activity of Gongronema latifolium leaf extracts on some cancer cells has been reported. Herein, we investigated the growth inhibitory effect of the Gongronema latilolium leaf methanol extract and isolated pregnane (iloneoside) against prostate cancer cell lines using the MTT cell proliferation assay, apoptosis quantification, cell cycle analysis using flow cytometry and computational analysis molecular docking, molecular dynamics simulation (MDs), binding free energy computation and cluster analysis. In addition, UPLC-ESI-TOFMS chemical fingerprinting of previously isolated compounds was performed. The extract inhibited the growth of the cell lines with an IC50 of 49.3 µg/ml and 28.4 µg/ml for 24 h and 48 h, respectively, for PC3; and 43.7 µg/ml and 22.3 µg/ml for 24 h and 48 h, respectively, for DU145. Iloneoside demonstrated low inhibitory activities against PC3 and DU145 (IC50 > 80 μM). Apoptotic quantification and cell cycle analysis further showed that iloneoside induced apoptosis in a few cells at a dose of 200 uM. The ensemble-based molecular docking of the iloneoside to BCL-XL and BCL-2 proteins, and docking to MCL-1, BCL-A1 and BFL-1 proteins, respectively, presented binding energies of -7.22 ± 0.5, -8.12 ± 0.55, -7.1, -7.2 and -6.3 kcal/mol, while the MM/PBSA binding free energy was -25.72 ± 7.22 and -27.76 ± 11.32 kcal/mol for BCL-XL and BCL-2 proteins. Furthermore, iloneoside was stable during the 100 ns MDs analysis, while the clustering of the MDs trajectories showed that the interactions were strongly preserved. Iloneoside, in part, or in synergy with other constituents, may be responsible for the antiproliferative activities of the leaf, subject to further investigation.

Insights into the low-temperature adaptation of an enzyme as studied through ancestral sequence reconstruction

For billions of years, enzymes have evolved in response to the changing environments in which their host organisms lived. Various lines of evidence suggest the earliest primitive organisms inhabited high-temperature environments and possessed enzymes adapted to such conditions. Consequently, extant mesophilic and psychrophilic enzymes are believed to have adapted to lower temperatures during the evolutionary process. Herein, we analyzed this low-temperature adaptation using ancestral sequence reconstruction. Previously, we generated the phylogenetic tree of 3-isopropylmalate dehydrogenases (IPMDHs) and reconstructed the sequence of the last bacterial common ancestor. The corresponding ancestral enzyme displayed high thermostability and catalytic activity at elevated temperatures but moderate activity at low temperatures (Furukawa et al., Sci. Rep., 2020;10:15493). Here, to identify amino acid residues that are responsible for the low-temperature adaptation, we reconstructed and characterized all 11 evolutionary intermediates that sequentially connect the last bacterial common ancestor with extant mesophilic IPMDH from Escherichia coli. A remarkable change in catalytic properties, from those suited for high reaction temperatures to those adapted for low temperatures, occurred between two consecutive evolutionary intermediates. Using a combination of sequence comparisons between ancestral proteins and site-directed mutagenesis analyses, three key amino acid substitutions were identified that enhance low-temperature catalytic activity. Intriguingly, amino acid substitutions that had the most significant impact on activity at low temperatures displayed no discernable effect on thermostability. However, these substitutions markedly reduced the activation energy for catalysis, thereby improving low-temperature activity. The results were further investigated by molecular dynamics simulations of the predicted structures of the ancestral enzymes. Our findings exemplify how ancestral sequence reconstruction can identify residues crucial for adaptation to low temperatures.

Targeting Glucosylceramide Synthase: Innovative Drug Repurposing Strategies for Lysosomal Diseases

Sphingolipidoses, a subgroup of lysosomal storage diseases (LSDs), are rare and debilitating disorders caused by defects in sphingolipid metabolism. Despite advancements in treatment, therapeutic options remain limited. Miglustat, a glucosylceramide synthase EC 2.4.1.80 (GCS) inhibitor, is one of the few available pharmacological treatments; however, it is associated with significant adverse effects that impact patients' quality of life. Drug repurposing offers a promising strategy to identify new therapeutic agents from approved drugs, expanding treatment options for rare diseases with limited therapeutic alternatives. This study aims to identify potential alternative inhibitors of GCS through a drug-repurposing approach, using computational and experimental methods to assess their therapeutic potential for sphingolipidoses. A library of approved drugs was screened using advanced computational techniques, including molecular docking, molecular dynamics simulations, and metadynamics, to identify potential GCS inhibitors. Promising candidates were selected for further in vitro validation to evaluate their inhibitory activity and potential as therapeutic alternatives to Miglustat. Computational screening identified several potential GCS inhibitors, with Dapagliflozin emerging as the most promising candidate. Experimental validation confirmed its efficacy, revealing a complementary mechanism of action to Miglustat while potentially offering a more favorable side effect profile. This study underscores the utility of computational and experimental methodologies in drug repurposing for rare diseases. The identification of Dapagliflozin as a potential GCS inhibitor provides a foundation for further preclinical and clinical evaluation, supporting its potential application in the treatment of sphingolipidoses.

Predicting and interpreting EPR spectra of POPC lipid bilayers with transmembrane α-helical peptides from all-atom molecular dynamics simulations

This study reports a large-scale all-atom MD simulation of POPC lipid bilayers in the presence of different concentrations of the transmembrane peptide acetyl-K2(LA)12K2-amide ((LA)12) and doped with 5-PC paramagnetic spin probes used in EPR studies. We apply a combined MD-EPR simulation methodology for the prediction of EPR spectra directly and completely from MD trajectories. This approach serves three major purposes. Firstly, comparing predicted EPR spectra with experimental ones, which are highly sensitive to motions, provides an ultimate test bed for the force fields currently employed for modeling lipid bilayer systems with embedded proteins or peptides. Secondly, simulations of EPR spectra directly from the atomistic MD models simplify the interpretation of the EPR line shapes and their changes induced by the presence of peptides in the lipid bilayer. These changes are directly linked to the dynamics and order of spin probes and POPC host molecules. Lastly and importantly, we demonstrate how the MD-EPR methodology can be employed to test the validity and limitations of the widely used approach for the estimation of the order parameter of lipids directly from the EPR experimental line shapes.

In silico λ-dynamics predicts protein binding specificities to modified RNAs

RNA modifications shape gene expression through a variety of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.

Target Identification with Live-Cell Photoaffinity Labeling and Mechanism of Action Elucidation of ARN23765, a Highly Potent CFTR Corrector

Molecular-targeted therapies for the treatment of cystic fibrosis (CF) rely on small-molecule modulators that rescue the activity of the defective CF transmembrane conductance regulator (CFTR) anion channel. ARN23765 is a small molecule with subnanomolar potency in rescuing the function of mutant CFTR in bronchial epithelial cells from CF patients carrying the F508del-CFTR mutation. Considering the multifaceted interactions of CFTR with the plasma membrane and the complexity of the protein network within the cellular compartments, here we report the investigation of ARN23765's molecular mechanism in live cells. We used the photoaffinity labeling (PAL) approach to demonstrate the interaction of ARN23765-derived probes with CFTR in cells. We showed that ARN23765 contributes to F508del-CFTR rescue by stabilizing the membrane-spanning domain-1 and interacting with CFTR at the same site as other type I CFTR correctors. Our study characterizes ARN23765's mode of action and highlights the potential of studying the interactions between CFTR and its correctors in live cells.

Discovering Skin Anti-Aging Potentials of the Most Abundant Flavone Phytochemical Compound Reported in Siam Violet Pearl, a Medicinal Plant from Thailand by In Silico and In Vitro Assessments

Currently, nutraceuticals and functional food/cosmeceutical sectors are seeking natural molecules to develop various types of phytopharmaceutical products. Flavonoids have been reported in antioxidant and many medical/pharmacological activities. Monochoria angustifolia or Siam violet pearl medicinal plant is the newest species of the genus Monochoria C. Presl, which have long been consumed as food and herbal medicines. Though previous work showed that apigenin-7-O-glucoside is the most abundant antioxidant phytochemical found in this medicinal plant, the report on anti-aging activity is still lacking and needs to be filled in. The objective of this work is to explore anti-aging capacities of the most abundant antioxidant phytochemical reported in this plant using both in silico and in vitro assessments. In addition, pharmacokinetic properties were predicted. Interestingly, the results from both in silico and in vitro analysis showed a similar trend that apigenin-7-O-glucoside is a potential anti-aging agent against three enzymes. The pharmacokinetic properties, such as adsorption, distribution, metabolism, excretion and toxicity (ADMET), of this compound are also provided in this work. The current study is also the first report on anti-aging properties of this Thai medicinal plant. However, the safety and efficacy of future developed products from this compound and clinical study should be determined in the future.

Molecular Principles of Proton-Coupled Quinone Reduction in the Membrane-Bound Superoxide Oxidase

Reactive oxygen species (ROS) are physiologically harmful radical species generated as byproducts of aerobic respiration. To detoxify ROS, most cells employ superoxide scavenging enzymes that disproportionate superoxide (O2·-) to oxygen (O2) and hydrogen peroxide (H2O2). In contrast, the membrane-bound superoxide oxidase (SOO) is a minimal 4-helical bundle protein that catalyzes the direct oxidation of O2·- to O2 and drives quinone reduction by mechanistic principles that remain unknown. Here, we combine multiscale hybrid quantum/classical (QM/MM) free energy calculations and microsecond molecular dynamics simulations with functional assays and site-directed mutagenesis experiments to probe the mechanistic principles underlying the charge transfer reactions of the superoxide-driven quinone reduction. We characterize a cluster of charged residues at the periplasmic side of the membrane that functions as a O2·- collecting antenna, initiating electron transfer via two b hemes to the active site for quinone reduction at the cytoplasmic side. Based on multidimensional QM/MM string simulations, we find that a proton-coupled electron transfer (PCET) reaction from the active site heme b and nearby histidine residues (H87, H158) results in quinol (QH2) formation, followed by proton uptake from the cytoplasmic side of the membrane. The functional relevance of the identified residues is supported by site-directed mutagenesis and activity assays, with mutations leading to inhibition of the O2·--driven quinone reduction activity. We suggest that the charge transfer reactions could build up a proton motive force that supports the bacterial energy transduction machinery, while the PCET machinery provides unique design principles of a minimal oxidoreductase.

Synthesis, Anti-Inflammatory, and Molecular Docking Studies of New Heterocyclic Derivatives Comprising Pyrazole, Pyridine, and/or Pyran Moieties

Introduction: Inhibiting cyclooxygenase-2 (COX-2) is a potential strategy in inflammation therapy. Thus, developing COX-2 inhibitors plays a pivotal role in efficient inflammation treatment. This study discloses the synthesis of new heterocyclic compounds incorporating pyridine, pyran, and/or pyrazole moieties as COX-2 inhibitors. Methods: In this study, the Claisen-Schmidt reaction of 1-(5-hydroxy-1,3-diphenyl-1H-pyrazol-4-yl)ethan-1-one 1 and p-methoxybenzaldehyde in ethanol containing aqueous sodium hydroxide (10%) led to the formation of 1-(5-hydroxy-1,3-diphenyl-1H-pyrazol-4-yl)-3-(4-methoxyphenyl)prop-2-en-1-one) 2. The latter compound was allowed to react as a key precursor with various nucleophiles such as ethyl cyanoacetate, malononitrile, cyclohexanone, ethyl acetoacetate, hydrazine, cyano acid hydrazide, hydrazide, and/or thiosemicarbazide to yield new heterocyclic derivatives comprising pyridine, pyran, and/or pyrazole moieties 3-15, according to the Michael addition reaction. The newly synthesized compounds were depicted using spectroscopic techniques such as IR, 1H-NMR, 13C-NMR, and MS. Moreover, their anti-inflammatory efficiency was in vitro evaluated by means of protein denaturation inhibition and cell membrane protection assay. Results: The results of 2-ΔΔct values of COX-2 expression for compounds 6, 11, 12, and 13 were 6.6, 2.9, 25.8, and 10.1, respectively. Therefore, compound 12, followed by 13, 11, and 6, showed potent anti-inflammatory properties by in vitro evaluation. Further, an in silico molecular docking study was performed on the best-docked compounds and reference drug (Diclofenac) to investigate their binding affinities against the active site of the target enzyme. The obtained results from the in silico study aligned with the biological evaluation. Conclusions: The studies open new doors for designing new heterocycles containing pyridine, pyran, and/or pyrazole moieties as potent anti-inflammatory agents.

Self-Guided Molecular Simulation to Enhance Concerted Motion

Self-guided (SG) molecular simulation methods, namely self-guided molecular dynamics (SGMD) and self-guided Langevin dynamics (SGLD), enhance conformational search by promoting low-frequency motion. A simple local time averaging scheme is used to extract low-frequency properties with little overhead in computing costs. For molecular processes to form ordered structures like ligand binding and protein folding, it is believed that concerted motions play crucial roles. To enhance the concerted motion in molecular systems, we propose a spatial averaging scheme to extract the concerted motion of a local region. Applying guiding forces based on spatial averaging, self-guided molecular simulations can enhance concerted motion and reach ordered structures more efficiently. Through simulations of amyloid fibril peptides, we demonstrated that the spatial averaging in self-guided Langevin dynamics results in accelerated β-sheet formation.

Long-Chain Cyclic Arylguanidines as Multifunctional Serotonin Receptor Ligands with Antiproliferative Activity

Recent investigations have shown serotonin's stimulatory effect on several types of cancers and carcinoid tumors. Nowadays there has been a significant increase in interest in 5-HT7 and 5-HT5A receptors in the context of cancer treatment. The possible role of 5-HT6R in the pathogenesis and progression of glioma remains an interesting and relatively unexplored issue. We developed a new group of long-chain 2-aminoquinazoline sulfonamides as new multifunctional serotonin receptor ligands, focused on 5-HT6R. The chosen group was further evaluated for antiproliferative effects on 1321N1 astrocytoma cells, along with U87MG, U-251, and LN-229 glioblastoma cell lines. Certain compounds were subjected to in vitro absorption, distribution, metabolism, excretion, and toxicity (ADMET) testing, for assessing factors such as lipophilicity, plasma protein binding, phospholipid affinity, potential for drug-drug interactions (DDI), membrane permeability (PAMPA), metabolic stability, and hepatotoxicity. Additionally, in vivo testing was performed using the Danio rerio model. The developed group includes the selective 5-HT6R antagonist PP 15, dual ligand for 5-HT1AR/5-HT6R PP 13, and dual ligand for 5-HT5AR/5-HT6R PP 10. The use of multifunctional ligands was associated with high anticancer activity both against selected glioma cell lines and other cancers (IC50 < 25 μM).

CHARMM-GUI EnzyDocker for Protein-Ligand Docking of Multiple Reactive States along a Reaction Coordinate in Enzymes

Enzymes play crucial roles in all biological systems by catalyzing a myriad of chemical reactions. These reactions range from simple one-step processes to intricate multistep cascades. Predicting mechanistically appropriate binding modes along a reaction pathway for substrate, product, and all reaction intermediates and transition states is a daunting task. To address this challenge, special docking programs like EnzyDock have been developed. Yet, running such docking simulations is complicated due to the nature of multistep enzyme processes. This work presents CHARMM-GUI EnzyDocker, a web-based cyberinfrastructure designed to streamline the preparation and running of EnzyDock docking simulations. The development of EnzyDocker has been achieved through integration of existing CHARMM-GUI modules, such as PDB Reader and Manipulator, Ligand Designer, and QM/MM Interfacer. In addition, new functionalities have been developed to facilitate a one-stop preparation of multistate and multiscale docking systems and enable interactive and intuitive ligand modifications and flexible protein residues selections. A simple setup related to multiligand docking is automatized through intuitive user interfaces. EnzyDocker offers support for standard classical docking and QM/MM docking with CHARMM built-in semiempirical engines. Automated consensus restraints for incorporating experimental knowledge into the docking are facilitated via a maximum common substructure algorithm. To illustrate the robustness of EnzyDocker, we conducted docking simulations of three enzyme systems: dihydrofolate reductase, SARS-CoV-2 Mpro, and the diterpene synthase CotB2. In addition, we have created four tutorial videos about these systems, which can be found at https://www.charmm-gui.org/demo/enzydock. EnzyDocker is expected to be a valuable and accessible web-based tool that simplifies and accelerates the setup process for multistate docking for enzymes.

Olea europaea L. cv. Nepal Leaf Extract Alleviates Dyslipidemia and Inflammation Related to Obesity in Rabbits: A Network Pharmacology Approach Revealing Effective Antihyperlipidemic Bioactive Compounds

The current study investigated the effects of Olea europaea L. cv. Nepal (OEN) leaf extract on obesity-related disorders in rabbits. OEN extract, in dose 100 mg/kg body weight, significantly reduced cholesterol (TC), triglycerides (TAG), and inflammatory markers (MCP-1, VCAM-1, IL-6, IL-1β, TNFα, NF-κB) in both preventative and treatment groups. In addition, heart tissue displayed near-normal collagen fiber distribution. LC-HRESIMS analysis tentatively identified 20 metabolites (1-20) in OEN, including lignans, secoiridoids, and triterpenoids. Network pharmacology analysis suggested 8-hydroxy-p-menth-1-en-7-oic-acid (13), 6,7-dihydroxy-2H-1-benzopyran-2-one 6-O-d-glucopyranoside (5), and 3-hydroxy-12-oleanen-28-oic-acid (20) as key antihyperlipidemic compounds, potentially targeting pathways involved in hyperlipidemia regulation. Furthermore, in silico studies revealed that lignans (compounds 1, 2, and 16) from OEN may bind and inhibit HMG-CoA reductase, a crucial enzyme in cholesterol metabolism. These results indicate the potential of using OEN leaf extract as a therapeutic strategy for managing hyperlipidemia and inflammation associated with obesity. More investigation is necessary to confirm and validate these results and explore the extract's full therapeutic potential.

Molecular Details of Polyester Decrystallization via Molecular Simulation

Waste polyesters are a potential feedstock for recycled and upcycled products. These polymers are generally semicrystalline, which presents a challenge for chemical and biological recycling to monomers, and thus the thermodynamic work associated with polyester decrystallization is an important consideration in some depolymerization strategies. Here, we use molecular dynamics simulations to calculate the free energy required to decrystallize a single chain from the crystal surface of five commercially and scientifically important, semiaromatic polyesters (PET, PTT, PBT, PEN, and PEF) in water. Our results indicate the decrystallization work ranges from approximately 15 kcal/mol (PEN) to 8 kcal/mol (PEF) per repeat unit for chains in the middle of a crystal surface. The insight gained into the molecular interactions that form the structural basis of semicrystalline synthetic polyesters can guide the pursuit of more efficient plastic processing, which could include catalyst development, optimizing recycling conditions including pretreatment, enzyme and solvent selections, and design of new materials.

OpenMMDL - Simplifying the Complex: Building, Simulating, and Analyzing Protein-Ligand Systems in OpenMM

Molecular dynamics (MD) simulations have become an essential tool for studying the dynamics of biological systems and exploring protein-ligand interactions. OpenMM is a modern, open-source software toolkit designed for MD simulations. Until now, it has lacked a module dedicated to building receptor-ligand systems, which is highly useful for investigating protein-ligand interactions for drug discovery. We therefore introduce OpenMMDL, an open-source toolkit that enables the preparation and simulation of protein-ligand complexes in OpenMM, along with the subsequent analysis of protein-ligand interactions. OpenMMDL consists of three main components: OpenMMDL Setup, a graphical user interface based on Python Flask to prepare protein and simulation settings, OpenMMDL Simulation to perform MD simulations with consecutive trajectory postprocessing, and finally OpenMMDL Analysis to analyze simulation results with respect to ligand binding. OpenMMDL is not only a versatile tool for analyzing protein-ligand interactions and generating ligand binding modes throughout simulations; it also tracks and clusters water molecules, particularly those exhibiting minimal displacement from their previous coordinates, providing insights into solvent dynamics. We applied OpenMMDL to study ligand-receptor interactions across diverse biological systems, including LDN-193189 and LDN-212854 with ALK2 (kinases), nifedipine and amlodipine in Cav1.1 (ion channels), LSD in 5-HT2B (G-protein coupled receptors), letrozole in CYP19A1 (cytochrome P450 oxygenases), flavin mononucleotide binding the FMN-riboswitch (RNAs), ligand C08 bound to TLR8 (toll-like receptor), and PZM21 bound to MOR (opioid receptor), highlighting distinct functionalities of OpenMMDL. OpenMMDL is publicly available at https://github.com/wolberlab/OpenMMDL.

Molecular convergence of neutralizing antibodies in human revealed by repeated rabies vaccination

Rabies vaccines require repeated immunization to robustly elicit neutralizing antibodies that prevent fatal diseases. Here, we analyzed rabies glycoprotein antibody repertoires at both polyclonal and monoclonal levels following repeated vaccination. Booster vaccination dramatically elevated the neutralizing activity of recalled antibodies, primarily targeting an immunodominant site III epitope with hydrophilic and rugged structures. Strikingly, the majority of site III-directed antibodies in the recall response used a convergent VH gene (IGHV3-30), and they exhibited more hydrophilic and shorter paratopes than non-site III antibodies, providing physicochemical advantages for binding to site III. Additionally, several amino acids on heavy chain CDR3 were identified as key sites for acquiring an ultrapotent neutralizing activity through site III binding. Our in-depth analysis of antibody repertoires revealed the molecular signatures of neutralizing antibodies generated by repeated rabies vaccination, possibly as a result of adaptive convergence.

Genomics-Informed Drug Repurposing Strategy Identifies Novel Therapeutic Targets for Metabolic Dysfunction-Associated Steatotic Liver Disease

Identification of drug-repurposing targets with genetic and biological support is an economically and temporally efficient strategy for improving treatment of diseases. We employed a cross-disciplinary approach to identify potential treatments for metabolic dysfunction associated steatotic liver disease (MASLD) using humans as a model organism. We identified 212 putative causal genes associated with MASLD using data from a large multi-ancestry genetic association study, of which 158 (74.5%) are novel. From this set we identified 57 genes that encode for druggable protein targets, and where the effects of increasing genetically predicted gene expression on MASLD risk align with the function of that drug on the protein target. These potential targets were then evaluated for evidence of efficacy using Mendelian randomization, pathway analysis, and protein structural modeling. Using these approaches, we present compelling evidence to suggest activation of FADS1 by icosopent ethyl as well as S1PR2 by fingolimod could be promising therapeutic strategies for MASLD.

Molecular simulations reveal intricate coupling between agonist-bound β-adrenergic receptors and G protein

G protein-coupled receptors (GPCRs) and G proteins transmit signals from hormones and neurotransmitters across cell membranes, initiating downstream signaling and modulating cellular behavior. Using advanced computer modeling and simulation, we identified atomistic-level structural, dynamic, and energetic mechanisms of norepinephrine (NE) and stimulatory G protein (Gs) interactions with β-adrenergic receptors (βARs), crucial GPCRs for heart function regulation and major drug targets. Our analysis revealed distinct binding behaviors of NE within β1AR and β2AR despite identical orthosteric binding pockets. β2AR had an additional binding site, explaining variations in NE binding affinities. Simulations showed significant differences in NE dissociation pathways and receptor interactions with the Gs. β1AR binds Gs more strongly, while β2AR induces greater conformational changes in the α subunit of Gs. Furthermore, GTP and GDP binding to Gs may disrupt coupling between NE and βAR, as well as between βAR and Gs. These findings may aid in designing precise βAR-targeted drugs.

Mechanisms of Peptide Agonist Dissociation and Deactivation of Adhesion G-Protein-Coupled Receptors

Adhesion G protein-coupled receptors (ADGRs) belong to Class B2 of GPCRs and are involved in a wide array of important physiological processes. ADGRs contain a GPCR autoproteolysis-inducing domain that is proximal to the receptor N-terminus and undergoes autoproteolysis during the biosynthesis to generate two fragments: the N-terminal fragment (NTF) and the C-terminal fragment (CTF). Dissociation of NTF reveals a tethered agonist to activate the CTF of ADGRs for G protein signaling. Synthetic peptides that mimic the tethered agonist can also activate ADGRs. However, mechanisms of peptide agonist dissociation and the deactivation of ADGRs remain poorly understood. In this study, we have performed all-atom enhanced sampling simulations using a novel protein-protein interaction Gaussian-accelerated molecular dynamics (PPI-GaMD) method on the ADGRG2-IP15 and ADGRG1-P7 complexes. The PPI-GaMD simulations captured the dissociation of the IP15 and P7 peptide agonists from their target receptors. We were able to identify important low-energy conformations of ADGRG2 and ADGRG1 in the active, intermediate, and inactive states, as well as different states of the peptide agonists IP15 and P7 during dissociation. Therefore, our PPI-GaMD simulations have revealed dynamic mechanisms of peptide agonist dissociation and deactivation of ADGRG1 and ADGRG2, which will facilitate the rational design of peptide regulators of the two receptors and other ADGRs.

Scaling of the GROMACS Molecular Dynamics Code to 65k CPU Cores on an HPC Cluster

We benchmarked the performance of the GROMACS 2024 molecular dynamics (MD) code on a modern high-performance computing (HPC) cluster with AMD CPUs on up to 65,536 CPU cores. We used five different MD systems, ranging in size from about 82,000 to 204 million atoms, and evaluated their performance using two different Message Passing Interface (MPI) libraries, Intel-MPI and Open-MPI. The largest system showed near-perfect strong scaling up to 512 nodes or 65,536 cores, maintaining a parallel efficiency above 0.9 even at the highest level of parallelization. Energy efficiency for a given number of nodes was generally equal to or slightly better than parallel efficiency. We achieved peak performances of 687 ns/d for the 82k atom system, 116 ns/d for the 53M atom system, and about 35 ns/d for the largest 204M atom system. These results demonstrate that highly optimized software running on a state-of-the-art HPC cluster provides sufficient computing power to simulate biomolecular systems at the mesoscale of viruses and organelles, and potentially small cells in the near future.

Effects of a Polarizable Force Field on Membrane Dynamics: Surface Viscosity, Lipid Diffusion, and Peptide Induced Pore Formation

The effects of the newly developed CHARMM polarizable lipid force field (FF), Drude2023, on selected lipid dynamical properties are compared with the additive CHARMM36 (C36), and an extension of C36, termed C36/LJ-PME, which includes long-range Lennard-Jones (LJ) interactions. Polarizability and long-range LJ interactions increase the membrane surface viscosity, which decreases the translational diffusion constants. Simulated diffusion constants for dipalmitoylphosphatidylcholine (DPPC) and dioleoylphosphatidylcholine (DOPC) extrapolated to infinite system size agree well with experiment for Drude2023, but overestimate experiment by 60% (on average) and a factor of 2.5 for C36/LJ-PME and C36, respectively. The relaxation time of lipid wobble is described about equally well by C36/LJ-PME and Drude2023, as consistent with the hexadecane viscosity for the FF, and both are more accurate than C36. Hence, physical improvements in the FF, which slowed down these dynamic processes led to better agreement with experiment. Lastly, bilayers containing 10 influenza fusion peptides and high fractions of lysolipids (which are known to accelerate pore formation) were simulated with C36 and Drude2023. Pore formation rates were comparable for the two FF for the bilayers with 80% and 90% lysolipid. However, while no pores formed in 24 μs (including a single 20 μs trajectory) in the 70% lysolipid with C36, 4 of 15 replicates formed pores in less than 1 μs with Drude. While the pathway to poration is qualitatively similar for the additive and polarizable FF for the systems studied, Drude2023 should be considered for quantitative studies of pore formation, and, in some cases, will accelerate the process.

A structural biology compatible file format for atomic force microscopy

Cryogenic electron microscopy (cryo-EM), X-ray crystallography, and nuclear magnetic resonance (NMR) contribute structural data that are interchangeable, cross-verifiable, and visualizable on common platforms, making them powerful tools for our understanding of protein structures. Unfortunately, atomic force microscopy (AFM) has so far failed to interface with these structural biology methods, despite the recent development of localization AFM (LAFM) that allows extracting high-resolution structural information from AFM data. Here, we build on LAFM and develop a pipeline that transforms AFM data into 3D-density files (.afm) that are readable by programs commonly used to visualize, analyze, and interpret structural data. We show that 3D-LAFM densities can serve as force fields to steer molecular dynamics flexible fitting (MDFF) to obtain structural models of previously unresolved states based on AFM observations in close-to-native environment. Besides, the .afm format enables direct 3D or 2D visualization and analysis of conventional AFM images. We anticipate that the file format will find wide usage and embed AFM in the repertoire of methods routinely used by the structural biology community, allowing AFM researchers to deposit data in repositories in a format that allows comparison and cross-verification with data from other techniques.

Size dependence of the ion pairing preferences investigated by free energy calculations

We calculate pairing free energies for a series of model monovalent ion pairs with equal sizes spanning a large radius range in the aqueous solution. Thermodynamic analysis reveals that the ion pairing strength displays an initial decrease in a nearly linear fashion followed by a significant enhancement at a relatively slower rate as the ion size gradually increases, resulting in the weakest pairing preference for ions with intermediate size. The free energy decomposition illustrates that the ion pairing is jointly governed by a delicate balance of the favorable ion-ion interaction term and the repulsive solvent-induced contribution, with their compensation leading to a small pairing free energy. The dependence of this substantial compensation on the ion size actually dominates the ion pairing strength and the magnitude of the free energy, which accounts for the occurrence of the less association between the intermediate-size ions. Estimation of van der Waals and electrostatic free energies highlights the importance of attractive dispersion forces in determining the shape of the potential of mean force, specifically for large ions with less favorable interaction with water molecules than the water-water interactions. In addition, the effect of varying the cation size on the decreased pairing preference surpasses that of altering the anion size. Our study significantly enhances our understanding of the empirical rule of matching ion size for predicting ion pairing preferences in aqueous solutions.

Unlocking the potential of in silico approach in designing antibodies against SARS-CoV-2

Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, in silico techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, in silico approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed in silico, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of in silico-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.

Atomic-level mechanisms of abnormal activation in NRAS oncogenes from two-dimensional free energy landscapes

The NRAS-mutant subset of melanoma is one of the most aggressive and lethal types associated with poor overall survival. Unfortunately, a low understanding of the NRAS-mutant dynamic behavior has led to the lack of clinically approved therapeutic agents able to directly target NRAS oncogenes. In this work, accurate local structures of NRAS and its mutants have been fully explored through the corresponding free energy surfaces obtained by microsecond scale well-tempered metadynamics simulations. Free energy calculations are crucial to reveal the precise mechanisms of Q61 mutations at the atomic level. Considering specific atom-atom distances d and angles ϕ as appropriate reaction coordinates we have obtained free energy surfaces revealing local and global minima together with their main transition states, unveiling the mechanisms of abnormal NRAS activation from the atomic-level and quantitatively analyzing the corresponding stable states. This will help in advancing our understanding of the basic mechanisms of NRAS mutations, offering new opportunities for the design of potential inhibitors.

Mechanism of dimer selectivity and binding cooperativity of BRAF inhibitors

Aberrant signaling of BRAFV600E is a major cancer driver. Current FDA-approved RAF inhibitors selectively inhibit the monomeric BRAFV600E and suffer from tumor resistance. Recently, dimer-selective and equipotent RAF inhibitors have been developed; however, the mechanism of dimer selectivity is poorly understood. Here, we report extensive molecular dynamics (MD) simulations of the monomeric and dimeric BRAFV600E in the apo form or in complex with one or two dimer-selective (PHI1) or equipotent (LY3009120) inhibitor(s). The simulations uncovered the unprecedented details of the remarkable allostery in BRAFV600E dimerization and inhibitor binding. Specifically, dimerization retrains and shifts the αC helix inward and increases the flexibility of the DFG motif; dimer compatibility is due to the promotion of the αC-in conformation, which is stabilized by a hydrogen bond formation between the inhibitor and the αC Glu501. A more stable hydrogen bond further restrains and shifts the αC helix inward, which incurs a larger entropic penalty that disfavors monomer binding. This mechanism led us to propose an empirical way based on the co-crystal structure to assess the dimer selectivity of a BRAFV600E inhibitor. Simulations also revealed that the positive cooperativity of PHI1 is due to its ability to preorganize the αC and DFG conformation in the opposite protomer, priming it for binding the second inhibitor. The atomically detailed view of the interplay between BRAF dimerization and inhibitor allostery as well as cooperativity has implications for understanding kinase signaling and contributes to the design of protomer selective RAF inhibitors.

A study of alpha-synuclein and poly(N-isopropylacrylamide) complex formation through detailed atomistic simulations

This work presents an investigation of the influence of poly(N-isopropylacrylamide) (PNIPAM) polymer on the structural dynamics of intrinsically disordered alpha-synuclein (α-syn) protein, exploring the formation and intricate features of the resulting α-syn/PNIPAM complexes. Using atomistic molecular dynamics (MD) simulations, our study analyzes the impact of initial configuration, polymer molecular weight, and protein mutations on the α-syn and the α-syn/PNIPAM complex. Atomistic simulations, of a few μs, of the protein/polymer complex reveal crucial insights into molecular interactions within the complex, emphasizing a delicate balance of forces governing its stability and structural evolution. Our findings indicate that PNIPAM polymer engages in significant non-polar interactions with the non-amyloid component (NAC) region of α-syn, which plays a crucial role in fibril formation, under various conditions such as the mutations in the protein structure and polymer chain length. Especially the PNIPAM polymer with a 40mer monomer exhibits a stabilizing effect on the structural properties of the protein, reducing intramolecular interactions that contribute to misfolding. These findings, which delve into protein/polymer interactions, hold promise as potential guidance for therapeutic strategies in various neurodegenerative disorders.

Lifetime of ground conformational state determines the activity of structured RNA

Biomolecules continually sample alternative conformations. Consequently, even the most energetically favored ground conformational state has a finite lifetime. Here, we show that, in addition to the three-dimensional (3D) structure, the lifetime of a ground conformational state determines its biological activity. Using hydrogen-deuterium exchange nuclear magnetic resonance spectroscopy, we found that Zika virus exoribonuclease-resistant RNA (xrRNA) encodes a ground conformational state with a lifetime that is ~105-107 longer than that of canonical base pairs. Mutations that shorten the apparent lifetime of the ground state without affecting its 3D structure decreased exoribonuclease resistance in vitro and impaired virus replication in cells. Additionally, we observed this exceptionally long-lived ground state in xrRNAs from diverse infectious mosquito-borne flaviviruses. These results demonstrate the biological importance of the lifetime of a preorganized ground state and further suggest that elucidating the lifetimes of dominant 3D structures of biomolecules may be crucial for understanding their behaviors and functions.

Simulations of pH and thermal effects on SARS-CoV-2 spike glycoprotein

We performed triplicate and long-time all-atom molecular dynamics simulations to investigate the structures and dynamics of the SARS-CoV-2 spike glycoprotein (S-protein) for a broad range of pH = 1 through 11 and temperatures of 3°C through 75°C. This study elucidates the complex interplay between pH and thermal effects on S-protein structures, with implications for its behavior under diverse conditions, and identifies the RBD as a primary region of the structural deviations. We found: 1) Structural deviations in the S-protein backbone at pH = 1 are 210% greater than those at pH = 7 at 75°C, with most of the deviations appearing in the receptor-binding domain (RBD). Smaller structural changes are observed at pH = 3 and 11. 2) The pH and thermal conditions impact on the protein structures: substantial acidic and basic conditions expand the protein's solvent exposure, while high heat contracts. This effect is primarily pH-driven at extreme acidity and thermo-driven at moderate pH. 3) The Gibbs free energy landscape reveals that pH as the main driver of structural changes. 4) The parametrized methods enable the predictions of the S-protein properties at any reasonable pH and thermal conditions without explicit MD simulations.

Improving the Activity and Selectivity of a Scorpion-Derived Peptide, A3a, against Acinetobacter baumannii through Rational Design

The rise in antimicrobial resistance has led to an increased desire to understand how antimicrobial peptides (AMPs) can be better engineered to kill antibiotic-resistant bacteria. Previously, we showed that C-terminal amidation of a peptide, identified in scorpion Androctonus amoreuxi venom, increased its activity against both Gram-positive and -negative bacteria. Here, we incorporate all-atom molecular dynamics (MD) simulations in a rational design strategy to create analogues of A3a with greater therapeutic potential. We discover two novel AMPs which achieve greater potency against, and selectivity toward, Acinetobacter baumannii ATCC 19606 but via two distinct mechanisms and which are effective in Galleria mellonella models of A. baumannii burn wound infection. While CD spectroscopy indicates A3a adopts an α-helix conformation in the presence of models of the Gram-negative bacterial plasma membrane, MD simulations reveal it adopts a hairpin conformation during initial binding. Three different strategies, designed to stabilize this hairpin conformation, produce substantially different outcomes. Deletion of Ile6 and Ile10 restricts conformational flexibility, characteristic of A3a, during membrane binding, prevents adoption of the α-helix conformation in the steady state, and abrogates the antibacterial activity. In contrast, substitution of arginine 7 to lysine (A3a[R7K]) or isoleucine 14 to tryptophan (A3a[I14W]) does not consistently affect peptide conformations. Both of these new analogues are rapidly bactericidal toward A. baumannii ATCC 19606 but A3a[R7K] also causes rapid permeabilization and while the antibacterial potency and selectivity are increased for both peptides, this is greatest for A3a[I14W]. Integration of atomistic MD simulations into a multidisciplinary approach to understanding antimicrobial peptide mechanism of action is a valuable tool for interpreting the effects of rational design strategies.

Potential Ligands to Resistin Against Prostate Cancer, Evaluated by Molecular Docking and In Vitro Assays to Develop an Anticancer Drug

Overweight and obesity are risk factors that increase the white adipose tissue (WAT) that promotes the release of adipokines (adiponectin, leptin, and resistin). The increase of the resistin levels contributes to different cellular processes; regulation in the metabolism, inflammation process, and particularly in messengers in some cancer types. Resistin promotes cell proliferation and migration. Therefore, resistin is proposed as a multipotential therapeutic target to treat different diseases, and in this study, we focus on the regulation of resistin and its effect on prostate cancer. This study proposes compounds selective to resistin, these were selected and evaluated by molecular docking and in vitro assays, to develop a new drug against the resistin´s functions related to interaction with their potential receptors (Δ-DCN, TLR4, and CAP-1). These molecules with pharmacological characteristics are capable of interacting in the regions of resistin to hinder/block the interaction between resistin and their possible receptors (Δ-DCN, TLR4, and CAP-1). We determined two compounds that showed to be selective against resistin by in vitro assays. In this way, this study proposes compounds that were developed to be selective against resistin, and it could decrease the effect known of resistin by their receptors related to the proliferation of cancer.

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories

Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. However, the analysis of MD simulation trajectories has not been data-driven but rather dependent on the user's prior knowledge of the systems, thus limiting the scope and utility of the MD simulations. Recently, we pioneered using BNM for analyzing the MD trajectories of protein complexes. The resulting BN models yield novel fully data-driven insights into the functional importance of the amino acid residues that modulate proteins' function. In this report, we describe the BaNDyT software package that implements the BNM specifically attuned to the MD simulation trajectories data. We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software's uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. This provides a powerful and versatile mechanism for users to control the workflow. As an application example, we have utilized this methodology and associated software to study how membrane proteins, specifically the G protein-coupled receptors, selectively couple to G proteins. The software can be used for analyzing MD trajectories of any protein as well as polymeric materials.

Computational Insights into Membrane Disruption by Cell-Penetrating Peptides

Cell-penetrating peptides (CPPs) can translocate into cells without inducing cytotoxicity. The internalization process implies several steps at different time scales ranging from microseconds to minutes. We combine adaptive Steered Molecular Dynamics (aSMD) with conventional Molecular Dynamics (cMD) to observe nonequilibrium and equilibrium states to study the early mechanisms of peptide-bilayer interaction leading to CPPs internalization. We define three membrane compositions representing bilayer sections, neutral lipids (i.e., upper leaflet), neutral lipids with cholesterol (i.e., hydrophobic core), and neutral/negatively charged lipids with cholesterol (i.e., lower leaflet) to study the energy barriers and disruption mechanisms of Arg9, MAP, and TP2, representing cationic, amphiphilic, and hydrophobic CPPs, respectively. Cholesterol and negatively charged lipids increase the energetic barriers for the peptide-bilayer crossing. TP2 interacts with the bilayer by hydrophobic insertion, while Arg9 disrupts the bilayer by forming transient or stable pores. MAP has shown both behaviors. Collectively, these findings underscore the significance of innovative computational approaches in studying membrane-disruptive peptides and, more specifically, in harnessing their potential for cell penetration.

SHINE: Deterministic Many-to-Many clustering of Molecular Pathways

State-of-the-art molecular dynamics (MD) simulation methods can generate diverse ensembles of pathways for complex biological processes. Analyzing these pathways using statistical mechanics tools demands identifying key states that contribute to both the dynamic and equilibrium properties of the system. This task becomes especially challenging when analyzing multiple MD simulations simultaneously, a common scenario in enhanced sampling techniques like the weighted ensemble strategy. Here, we present a new module of the MDANCE package designed to streamline the analysis of pathway ensembles. This module integrates n-ary similarity, cheminformatics-inspired tools, and hierarchical clustering to improve analysis efficiency. We present the theoretical foundation behind this approach, termed Sampling Hierarchical Intrinsic N-ary Ensembles (SHINE), and demonstrate its application to simulations of alanine dipeptide and adenylate kinase.

Immunoinformatic strategy for developing multi-epitope subunit vaccine against Helicobacter pylori

Helicobacter pylori is a gram-negative bacterium that persistently infects the human stomach, leading to peptic ulcers, gastritis, and an increased risk of gastric cancer. The extremophilic characteristics of this bacterium make it resistant to current drug treatments, and there are no licensed vaccines available against H. pylori. Computational approaches offer a viable alternative for designing antigenic, stable, and safe vaccines to control infections caused by this pathogen. In this study, we employed an immunoinformatic strategy to design a set of candidate multi-epitope subunit vaccines by combining the most potent B and T cell epitopes from three targeted antigenic proteins (BabA, CagA, and VacA). Out of the 12 hypothetical vaccines generated, two (HP_VaX_V1 and HP_VaX_V2) were found to be strongly immunogenic, non-allergenic, and structurally stable. The proposed vaccine candidates were evaluated based on population coverage, molecular docking, immune simulations, codon adaptation, secondary mRNA structure, and in silico cloning. The vaccine candidates exhibited antigenic scores of 1.19 and 1.01, with 93.5% and 90.4% of the most rama-favored regions, respectively. HP_VaX_V1 and HP_VaX_V2 exhibited the strongest binding affinity towards TLR-7 and TLR-8, as determined by molecular docking simulations (ΔG = -20.3 and -20.9, respectively). Afterward, multi-scale normal mode analysis simulation revealed the structural flexibility and stability of vaccine candidates. Additionally, immune simulations showed elevated levels of cell-mediated immunity, while repeated exposure simulations indicated rapid antigen clearance. Finally, in silico cloning was performed using the expression vector pET28a (+) with optimized restriction sites to develop a viable strategy for large-scale production of the chosen vaccine constructs. These analyses suggest that the proposed vaccines may elicit potent immune responses against H. pylori, but laboratory validation is needed to verify their safety and immunogenicity.

In Slico Screening and In Vitro Identification of Hyperuricemia-Inhibiting Peptides from Trachurus japonicus

Hyperuricemia arises from imbalanced uric acid metabolism, contributing to gout and related chronic diseases. When traditional drugs are used to treat hyperuricemia, side effects are inevitable, which promotes the exploration of new bioactive compounds. Protein hydrolysates and peptides are gradually showing potential in the treatment of hyperuricemia. This study investigated the uric acid inhibitory activity of peptides extracted from Trachurus japonicus using in silico and in vitro methods. We employed in silico virtual enzymolysis and experimental validation to identify bioactive peptides from Trachurus japonicus proteins. Four peptides (DF, AGF, QPSF, and AGDDAPR) were comprehensively screened by molecular docking and database analysis. After solid-phase synthesis, the inhibitory effects of these peptides on hyperuricemia were further verified in vitro and at the cellular level. The results showed that all four peptides have good hyperuricemia-inhibiting activities. Molecular docking and molecular dynamics revealed that peptides DF and AGDDAPR affect the production of uric acid by binding to the active sites of urate transporter 1 (URAT1), glucose transporter 9 (GLUT9), and xanthine oxidase (XOD), while peptides QPSF and AGF mainly influence the XOD active site, confirming that it is feasible to rapidly screen hyperuricemia-inhibiting peptides by molecular docking.

Pharmacophore modeling in drug design

A successful and expanded area of computational drug design is pharmacophore modeling. A pharmacophore is a description of the structural features of a compound that are essential to its biological activity. The rational design of new drugs has made extensive use of the pharmacophore concept. By schematically illustrating the essential components of molecular recognition, Pharmacophores can be used to represent and identify molecules in two or three dimensions. Besides target identification, the pharmacophore concept is also helpful for side effects, off-target, and absorption, distribution, and toxicity modeling. Moreover, to enhance virtual screening, pharmacophores, and molecular docking simulations are frequently coupled. A completely new area of drug design has been made possible by the development of machine learning techniques and pharmacophore mapping algorithms, wherein an ineffective molecule with the right modifications may have the potential to function as an inhibitor. This approach has been stimulated by its predictive abilities to assess the possibility that a set of compounds will be active against protein targets of interest. With alignment to the standard pharmacophore model, active compounds of the protein target can be developed. The pharmacophore modeling/screening technique is used to identify possible proteins of interest and seek out/suggest novel therapeutic uses for the drug.

Atomistic Mechanism of Lipid Membrane Binding for Blood Coagulation Factor VIII with Molecular Dynamics Simulations on a Microsecond Time Scale

During the blood coagulation cascade, coagulation factor VIII (FVIII) is activated by thrombin to form activated factor VIII (FVIIIa). FVIIIa associates with platelet surfaces at the site of vascular damage to form an intrinsic tenase complex with activated factor IX. A working model for FVIII membrane binding involves the association of positively charged FVIII residues with negatively charged lipid headgroups and the burial of hydrophobic residues into the membrane interior. Currently, the atomic details of the FVIII lipid binding interactions and membrane orientation are lacking. This study reports residue-specific FVIII C domain interactions with 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) and 1,2-dioleoyl-sn-glycero-3-phospho-l-serine (DOPS) in atomistic detail. Contact maps between residues in the C domains with different lipid moieties support prior structural data describing how the C domains associate with membranes through electrostatic and hydrophobic interactions. Solvent-accessible surface area analysis quantified the extent to which residues in the C1 and C2 domains bury into the membrane. Calculations of the potential energy between the C domains and DOPC and DOPS revealed an FVIII membrane-binding orientation that agrees with previous experimental data. This study expands our knowledge of the structural basis of FVIII membrane association, which may be critical for the development of next-generation FVIII replacement constructs with improved activity.

Synthesis of 1-Hydroxy(and 1-Alkoxy, 1-Acyloxy)-1H-indoles and evaluations of their suppressive activities against tumor growth through inhibiting lactate dehydrogenase A

Inhibition of lactate dehydrogenase (LDH) has emerged as a promising cancer therapy strategy due to its essential role in the metabolic transformation of cancer cells. In this study, 53 derivatives of 1-hydroxy(and 1-alkoxy, 1-acyloxy)indoles were designed, synthesized, and biologically evaluated. Several multi-substituted 1-hydroxy(and 1-alkoxy, 1-acyloxy)indole compounds exhibited inhibitory activity against the LDH-A isoform (LDHA). We confirmed that the C(3)-substituent provided additional significant hydrogen bonding and hydrophobic interactions, which enhanced the LDHA inhibitory activity with high selectivity. Our results revealed that methyl 4-bromo-3-[(n-hexyloxy)methyl]-1-hydroxy-1H-indole-2-carboxylate (1g), selectively inhibited LDHA (IC50 = 25 ± 1.12 nM) without affecting the LDH-B isoform (LDHB). The compound exhibited potent cytotoxic activity in several cancer cell lines, including DLD-1 colorectal cancer cells (GI50 = 27 ± 1.2 μM). Compound 1g significantly inhibited cancer cell growth by activating apoptotic pathways in a xenograft cancer model, without causing weight loss or liver and kidney damage. Therefore, compound 1g may serve as a highly specific and promising candidate for the development of LDHA inhibitors for cancer therapy.

Direct cytosol delivery of mRNA by micron-sized co-assembly with designer oligopeptides

Inefficient endosomal escape has been regarded as the main bottleneck for intracellular nucleic acid delivery. While most research efforts have been spent on designing various nano-sized particles, we took a different path here, investigating micron-sized carriers for direct cytosol entry. Using the spontaneous co-assembly of mRNA and the designer 27 amino acid oligopeptide named pepMAX2, micron-sized co-assemblies were obtained with various sizes by altering the concentration of NaCl salt and time for pre-incubation. Surprisingly, transfection was much more effective using micron-sized than nano-sized co-assemblies, and the efficiency surpasses that of a widely used lipid-based commercial reagent. The study was complemented by computational simulations, inhibitor studies and live-cell confocal imaging to shed light on the role of electrostatic interaction on assembly and the mechanism of uptake and intracellular trafficking. These micron-sized co-assemblies directly enter the cytosol and then release mRNA, bypassing conventional pathways and thus avoiding the lysosomal degradation. This simple approach involving short oligopeptides and salt addition to create optimal micron-sized co-assembly with mRNA should open new avenues to overcome endosomal barriers for intracellular delivery of nucleic acid therapeutics.