Implementation of the CHARMM Force Field in GROMACS: Analysis of Protein Stability Effects from Correction Maps, Virtual Interaction Sites, and Water Models

Effect of ethylene oxide groups on calcite wettability reversal by nonionic surfactants: An experimental and molecular dynamics simulation investigation

HYPOTHESIS

Ethoxylated nonionic surfactants are promising candidates for enhanced oil recovery (EOR) from oil-wet carbonate reservoirs due to their ability to reverse the mineral wettability. The wettability-reversal efficiency increases with the number of the ethoxy (EO) groups in the surfactant molecule.

METHODOLOGY

Contact angle measurements, scanning electron microscopy (SEM) and molecular dynamics (MD) simulations were combined to investigate the wettability reversal of an oil-wet calcite by three ethoxylated nonionic surfactants with 1, 4 and 8 EO groups, respectively, to directly probe the role of the EO groups and to uncover the molecular mechanism responsible for the wettability reversal.

FINDINGS

Both experiments and simulations consistently show a clear correlation between the number of EO groups and the wettability reversal efficiency of the surfactants, whereby the higher number of EO groups results in greater degree of wettability reversal. This is due to 1) the more hydrophilic surfactant headgroup weakening the carboxylate interactions with the surface by expanding the surface-adjacent water layer, and 2) the physically larger surfactant molecule attracting the carboxylates more strongly, thus aiding in their removal from the surface.

Identification of potential inhibitors for drug resistance in acute lymphoblastic leukemia through differentially expressed gene analysis and in silico screening

Acute lymphoblastic leukemia (ALL) is a disease of lymphocyte origin predominantly diagnosed in children. While its 5-year survival rate is high, resistance to chemotherapy drugs is still an obstacle. Our aim is to determine differentially expressed genes (DEGs) related to Asparaginase, Daunorubicin, Prednisolone, and Vincristine resistance and identify potential inhibitors via docking. Three datasets were accessed from the Gene Expression Omnibus database; GSE635, GSE19143, and GSE22529. The microarray data was analyzed using R4.2.0 and Bioconductor packages, and pathway and protein-protein interaction analysis were performed. We identified 1294 upregulated DEGs, with 12 genes consistently upregulated in all four resistant groups. KEGG analysis revealed an association with the PI3K-Akt pathway. Among DEGs, 33 hub genes including MDM2 and USP7 were pinpointed. Within common genes, CLDN9 and HS3ST3A1 were subjected to molecular docking against 3556 molecules. Following ADMET analysis, three drugs emerged as potential inhibitors: Flunarizine, Talniflumate, and Eltrombopag. Molecular dynamics analysis for HS3ST3A1 indicated all candidates had the potential to overcome drug resistance, Eltrombopag displaying particularly promising results. This study promotes a further understanding of drug resistance in ALL, introducing novel genes for consideration in diagnostic screening. It also presents potential inhibitor candidates to tackle drug resistance through repurposing.

Computational strategies in Klebsiella pneumoniae vaccine design: navigating the landscape of in silico insights

The emergence of multidrug-resistant Klebsiella pneumoniae poses a grave threat to global public health, necessitating urgent strategies for vaccine development. In this context, computational tools have emerged as indispensable assets, offering unprecedented insights into klebsiellal biology and facilitating the design of effective vaccines. Here, a review of the application of computational methods in the development of K. pneumoniae vaccines is presented, elucidating the transformative impact of in silico approaches. Through a systematic exploration of bioinformatics, structural biology, and immunoinformatics techniques, the complex landscape of K. pneumoniae pathogenesis and antigenicity was unravelled. Key insights into virulence factors, antigen discovery, and immune response mechanisms are discussed, highlighting the pivotal role of computational tools in accelerating vaccine development efforts. Advancements in epitope prediction, antigen selection, and vaccine design optimisation are examined, highlighting the potential of in silico approaches to update vaccine development pipelines. Furthermore, challenges and future directions in leveraging computational tools to combat K. pneumoniae are discussed, emphasizing the importance of multidisciplinary collaboration and data integration. This review provides a comprehensive overview of the current state of computational contributions to K. pneumoniae vaccine development, offering insights into innovative strategies for addressing this urgent global health challenge.

Driving forces and large scale affinity calculations for piperine/γ-cyclodxetrin complexes: Mechanistic insights from umbrella sampling simulation and DFT calculations

Piperine (PiP), a bioactive molecule, exhibits numerous health benefits and is frequently employed as a co-delivery agent with various phytomedicines (e.g., curcumin) to enhance their bioavailability. This is attributed to PiP's inhibitory activity against drug-metabolizing proteins, notably CYP3A4. Nevertheless, PiP encounters solubility challenges addressed in this study using cyclodextrins (CDs). Specifically, γ-CD and its derivatives, Hydroxypropyl-γ-CD (HP-γ-CD), and Octakis (6-O-sulfo)-γ-CD (Octakis-S-γ-CD), were employed to form supramolecular complexes with PiP. The conformational space of the complexes was assessed through 1 μs molecular dynamics simulations and umbrella sampling. Additionally, quantum mechanical calculations using wB97X-D dispersion-corrected DFT functional and 6-311 + G(d,p) basis set were conducted on the complexes to examine the thermodynamics and kinetic stability. Results indicated that Octakis-S-γ-CD exhibits superior host capabilities for PiP, with the most favorable complexation energy (-457.05 kJ/mol), followed by HP-γ-CD (-249.16 kJ/mol). Furthermore, two conformations of the Octakis-S-γ-CD/PiP complex were explored to elucidate the optimal binding orientation of PiP within the binding pocket of Octakis-S-γ-CD. Supramolecular chemistry relies significantly on non-covalent interactions. Therefore, our investigation extensively explores the critical atoms involved in these interactions, elucidating the influence of substituted groups on the stability of inclusion complexes. This comprehensive analysis contributes to emphasizing the γ-CD derivatives with improved host capacity.

Larvicidal activity of β-Citral: An In-vitro and In-silico study to understand its potential against mosquito

Tropical and subtropical regions face millions of deaths from mosquito-borne illnesses yearly. Insecticides prevent transmission but pose health risks like dermatitis and allergies. The primary objective was to mitigate the recurring dependence on synthetic insecticides, thereby curbing the development of mosquito resistance. Leaves of Cymbopogon flexuosus (lemongrass) was collected from Mayurbhanj, India, processed, then extracted by steam distillation for essential oils & analyzed spectroscopically. Larvicidal assays were performed across varying concentrations, revealing the significant mortality induced by the Cymbopogon flexuosus extract against Anopheles stephensi larvae. 3D structure was modelled by using G protein-coupled receptors (GPCR) sequence and structural stability was also validated. After docking the binding free energy was determined from GPCR protein with β-citral complex. Molecular dynamics (MD) study was conducted on the docked pose that displayed an optimal interactome profile. The larvicidal assay at the 12th and 24th hour revealed the highest LC50 (lethal concentration) of 23.493 ppm and 19.664 ppm . β-Citral has a high binding affinity and an identifiable binding site, which suggests that it may play a larvicidal role in regulating the receptor's function by creating stable complexes with it. β-Citral from lemongrass oils has potential larvicidal activity and effective against GPCR family 1 of mosquito and highly effective repellents against mosquito-borne diseases.

Marine natural compounds as potential CBP bromodomain inhibitors for treating cancer: an in-silico approach using molecular docking, ADMET, molecular dynamics simulations and MM-PBSA binding free energy calculations

The cAMP-responsive element binding protein (CREB) binding protein (CBP), a bromodomain-containing protein, engages with multiple transcription factors and enhances the activation of many genes. CBP bromodomain acts as an epigenetic reader and plays an important role in the CBP-chromatin interaction which makes it an important drug target for treating many diseases. Though inhibiting CBP bromodomain was reported to have great potential in cancer therapeutics, approved CBP bromodomain inhibitor is yet to come. We utilized various in silico approaches like molecular docking, ADMET, molecular dynamics (MD) simulations, MM-PBSA calculations, and in silico PASS predictions to identify potential CBP bromodomain inhibitors from marine natural compounds as they have been identified as having distinctive chemical structures and greater anticancer activities. To develop a marine natural compound library for this investigation, Lipinski's rule of five was used. Sequential investigations utilizing molecular docking, ADMET studies, 100 ns MD simulations, and MM-PBSA calculations revealed that three marine compounds-ascididemin, neoamphimedine, and stelletin A-demonstrated superior binding affinity compared to the standard inhibitor, 69 A. These compounds also exhibited suitable drug-like properties, a favorable safety profile, and formed stable protein-ligand complexes. The in-silico PASS tool predicted that these compounds have significant potential for anticancer activity. Among them, ascididemin demonstrated the highest binding affinity in both molecular docking and MM-PBSA calculations, as well as a better stability profile in MD simulations. Hence, ascididemin can be a potential inhibitor of CBP bromodomain. However, in vitro and in vivo validation is required for further confirmation of these findings.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00258-5.

Giant gateable thermoelectric conversion by tuning the ion linkage interactions in covalent organic framework membranes

Efficient energy conversion using ions as carriers necessitates membranes that sustain high permselectivity in high salinity conditions, which presents a significant challenge. This study addresses the issue by manipulating the linkages in covalent-organic-framework membranes, altering the distribution of electrostatic potentials and thereby influencing the short-range interactions between ions and membranes. We show that a charge-neutral covalent-organic-framework membrane with β-ketoenamine linkages achieves record permselectivity in high salinity environments. Additionally, the membrane retains its permselectivity under temperature gradients, providing a method for converting low-grade waste heat into electrical energy. Experiments reveal that with a 3 M KCl solution and a 50 K temperature difference, the membrane generates an output power density of 5.70 W m-2. Furthermore, guided by a short-range ionic screening mechanism, the membrane exhibits adaptable permselectivity, allowing reversible and controllable operations by finely adjusting charge polarity and magnitude on the membrane's channel surfaces via ion adsorption. Notably, treatment with K3PO4 solutions significantly enhances permselectivity, resulting in a giant output power density of 20.22 W m-2, a 3.6-fold increase over the untreated membrane, setting a benchmark for converting low-grade heat into electrical energy.

Thermodynamic Details of Pinholin S2168 Activation Revealed Using Alchemical Free Energy Simulations

Pinholin S2168 is a viral integral membrane protein whose function is to form nanoscopic "pinholes" in bacterial cell membranes to induce cell lysis as part of the viral replication cycle. Pinholin can transition from an inactive to an active conformation by exposing a transmembrane domain (TMD1) to the extracellular fluid. Upon activation, several copies of the protein assemble via interactions among a second transmembrane domain (TMD2) to form a single pore, thus hastening cell lysis and viral escape. The following experiments provide conformational descriptors of pinholin in active and inactive states and elucidate the molecular driving forces that control pinholin activity. In the present study, molecular dynamics (MD) simulations have been used to refine experimentally derived conformational descriptors into an atomistically detailed model of irsS2168, an antiholin mutant. To provide additional details about the thermodynamics of pinholin activation and to overcome large intrinsic kinetic barriers to activation, alchemical free energy simulations have been conducted. Alchemical mutations reveal the change in folding free energy upon mutation. The results suggest that alchemical mutations are an effective tool to rationalize experimental observations and predict the effects of site mutations on conformational states for proteins integrated into lipid bilayers. S16F, A17Q, A17Q+G21Q, and A17Q+G21Q+G14Q mutants reveal how changes in hydrophilicity and disruption of the glycine zipper motif influence pinholin's thermodynamic equilibrium, favoring the active conformation. These findings align with experimental observations from DEER spectroscopy, demonstrating that mutations increasing the hydrophilicity of TMD1 promote activation by making TMD1 more likely to exit the membrane and enter the extracellular fluid.

Molecular docking and molecular dynamic simulation studies to identify potential terpenes against Internalin A protein of Listeria monocytogenes

Introduction

Ever since the outbreak of listeriosis and other related illnesses caused by the dreadful pathogen Listeria monocytogenes, the lives of immunocompromised individuals have been at risk.

Objectives and Methods

The main goal of this study is to comprehend the potential of terpenes, a major class of secondary metabolites in inhibiting one of the disease-causing protein Internalin A (InlA) of the pathogen via in silico approaches.

Results

The best binding affinity value of -9.5 kcal/mol was observed for Bipinnatin and Epispongiadiol according to the molecular docking studies. The compounds were further subjected to ADMET and biological activity estimation which confirmed their good pharmacokinetic properties and antibacterial activity.

Discussion

Molecular dynamic simulation for a timescale of 100 ns finally revealed Epispongiadiol to be a promising drug-like compound that could possibly pave the way to the treatment of this disease.

Molecular Mechanism of Ciprofloxacin Translocation Through the Major Diffusion Channels of the ESKAPE Pathogens Klebsiella pneumoniae and Enterobacter cloacae

Experimental studies on the translocation and accumulation of antibiotics in Gram-negative bacteria have revealed details of the properties that allow efficient permeation through bacterial outer membrane porins. Among the major outer membrane diffusion channels, OmpF has been extensively studied to understand the antibiotic translocation process. In a few cases, this knowledge has also helped to improve the efficacy of existing antibacterial molecules. However, the extension of these strategies to enhance the efficacy of other existing and novel drugs require comprehensive molecular insight into the permeation process and an understanding of how antibiotic and channel properties influence the effective permeation rates. Previous studies have investigated how differences in antibiotic charge distribution can influence the observed permeation pathways through the OmpF channel, and have shown that the dynamics of the L3 loop can play a dominant role in the permeation process. Here, we perform all-atom simulations of the OmpF orthologs, OmpE35 from Enterobacter cloacae and OmpK35 from Klebsiella pneumoniae. Unbiased simulations of the porins and biased simulations of the ciprofloxacin permeation processes through these channels provide insight into the differences in the permeation pathway and energetics. In addition, we show that similar to the OmpF channel, antibiotic-induced dynamics of the L3 loop are also operative in the orthologs. However, the sequence and structural differences, influence the extent of the L3 loop fluctuations with OmpK35 showing greater stability in unbiased runs and subdued fluctuations in simulations with ciprofloxacin.

Destabilization of the D2 domain of Thermotoga maritima arginine binding protein induced by guanidinium thiocyanate and its counteraction by stabilizing agents

D2 is a structural and cooperative domain of Thermotoga maritima Arginine Binding Protein, that possesses a remarkable conformational stability, with a denaturation temperature of 102.6°C, at pH 7.4. The addition of potassium thiocyanate causes a significant decrease in the D2 denaturation temperature. The interactions of thiocyanate ions with D2 have been studied by means of isothermal titration calorimetry measurements and molecular dynamics simulations. It emerged that: (a) 20-30 thiocyanate ions interact with the D2 surface and are present in its first solvation shell; (b) each of them makes several contacts with protein groups, both polar and nonpolar ones. The addition of guanidinium thiocyanate causes a marked destabilization of the D2 native state, because both the ions are denaturing agents. However, on adding to the solution containing D2 and guanidinium thiocyanate a stabilizing agent, such as TMAO, sucrose or sodium sulfate, a significant increase in denaturation temperature occurs. The present results confirm that counteraction is a general phenomenon for globular proteins.

Shedding light into the biological activity of aminopterin, via molecular structural, docking, and molecular dynamics analyses

In this study, the structural and anticancer properties of aminopterin, as well as its antiviral characteristics, were elucidated. The preferred conformations of the title molecule were investigated with semiempirical AM1 method, and the obtained the lowest energy conformer was then optimized by using density functional (DFT/B3LYP) method with 6-311++G(d,p) as basis set. The vibrational frequencies of the optimized structure were calculated by the same level of theory and were compared with the experimental values. The vibrational assignments were performed based on the computed potential energy distribution (PED) of the vibrational modes. The molecular electrostatic potential (MEP) and frontier molecular orbitals (HOMO, LUMO) analyses were carried out for the optimized structure and the chemical reactivity has been scrutinized. To enlighten the biological activity of aminopterin as anticancer and anti-COVID-19 agents, aminopterin was docked into DNA, αIIBβ3 and α5β1integrins, human dihydrofolate reductase, main protease (Mpro) of SARS-CoV-2 and SARS-CoV-2/ACE2 complex receptor. The binding mechanisms of aminopterin with the receptors were clarified. The molecular docking results revealed the strong interaction of the aminopterin with DNA (-8.2 kcal/mol), αIIBβ3 and α5β1 integrins (-9.0 and -10.8 kcal/mol, respectively), human dihydrofolate reductase (-9.7 kcal/mol), Mpro of SARS-CoV-2 (-6.7 kcal/mol), and SARS-CoV-2/ACE2 complex receptor (-8.1 kcal/mol). Moreover, after molecular docking calculations, top-scoring ligand-receptor complexes of the aminopterin with SARS-CoV-2 enzymes (6M03 and 6M0J) were subjected to 50 ns all-atom MD simulations to investigate the ligand-receptor interactions in more detail, and to determine the binding free energies accurately. The predicted results indicate that the aminopterin may significantly inhibit SARS-CoV-2 infection. Thus, in this study, as both anticancer and anti-COVID-19 agents, the versatility of the biological activity of aminopterin was shown.Communicated by Ramaswamy H. Sarma.

Mutations in LIFR rewire the JAK/STAT signaling pathway: A study unveiling mechanistic details of Stüve-Wiedemann syndrome

Stüve-Wiedemann syndrome (SWS), a rare autosomal recessive disorder, characterized by diminutive size, curvature of the elongated bones, bent fingers, episodes of heightened body temperature, respiratory distress or periods of breath-holding, and challenges with feeding, especially causes fatality in infants. SWS is an outcome of potential missense mutations in the leukemia inhibitory factor receptor gene reflected as numerous amino acid mutations at protein level. Employing in silico tools and techniques like mutational screening with Pred_MutHTP, I-Mutant2.0, PANTHER.db, PolyPhen, to classify mutations as deleterious/destabilizing, in conjunction with experimental data analysis, P136A and S279P emerged as 'effect'-causing mutations. Pre-existing knowledge suggests, SWS progression is effectuated conformationally altered and dysfunctional LIFR, unable to bind to LIF and further form the LIF/LIFR/gp130 signalling complex. To gain functional insights into the effect of the said mutations on the wild type protein, an all-atom, explicit, solvent molecular dynamics simulation was performed following docking approaches. Consequently, referring to the RMSD, RMSF, protein dynamic network analysis, energy landscape plots and domain motion analysis, it was revealed that unbound LIFR_WT was more prone to LIF binding as usual whereas the mutants exhibited considerable domain closure to inhibit LIF binding. We conducted binding affinity analysis via MM/GBSA and dissociation constant estimation after LIFR-LIF docking and found the WT_complex to be more stable and compact as a whole when compared to the flexible mutant complexes thus being associated with SWS. Our study offers a route for understanding molecular level implications upon LIFR mutations which opens an avenue for therapeutic interventions.

Exploring the phase behavior of C8-BTBT-C8 at ambient and high temperatures: insights and challenges from molecular dynamics simulations

C8-BTBT-C8 is one promising candidate for the development of high-performance electronic devices based on thin-film technologies. Its monoclinic polymorph has a well-established role in thin-film growth. Yet, quite little information is available about its dynamics on the molecular scale, and the structures of the mesophases which form at high temperature (about 100 K above ambient temperature). The present study is devoted to the analysis of such phases, with the ultimate goal of developing molecular models. Already at ambient temperature, our molecular dynamics simulations reveal a rich conformational behavior of the alkyl side chains, with gauche conformations as leading structural defects. Heating promotes the formation of a stacking faulted mesophase (380 K), and a smectic phase, at 385 K, upon side chain melting. Although more disordered, this phase bears several analogies with the smectic A phase, experimentally observed at 382.5 K. At higher temperatures, the increase in configurational disorder is brought by molecular diffusion and other phenomena, finally leading to an isotropic molten phase. Our in-depth analysis, complemented by hot-stage polarizing microscopy data, provides interesting insights into this material, highlighting the challenges associated with the modeling of soft semiconducting systems.

Data Mining and in Silico Analysis of Ethiopian Traditional Medicine: Unveiling the Therapeutic Potential of Rumex abyssinicus Jacq

Multicomponent traditional medicine prescriptions are widely used in Ethiopia for disease treatment. However, inconsistencies across practitioners, cultures, and locations have hindered the development of reliable therapeutic medicines. Systematic analysis of traditional medicine data is crucial for identifying consistent and reliable medicinal materials. In this study, we compiled and analyzed a dataset of 505 prescriptions, encompassing 567 medicinal materials used for treating 106 diseases. Using association rule mining, we identified significant associations between diseases and medicinal materials. Notably, wound healing-the most frequently treated condition-was strongly associated with Rumex abyssinicus Jacq., showing a high support value. This association led to further in silico and network analysis of R. abyssinicus Jacq. compounds, revealing 756 therapeutic targets enriched in various KEGG pathways and biological processes. The Random-Walk with Restart (RWR) algorithm applied to the CODA PPI network identified these targets as linked to diseases such as cancer, inflammation, and metabolic, immune, respiratory, and neurological disorders. Many hub target genes from the PPI network were also directly associated with wound healing, supporting the traditional use of R. abyssinicus Jacq. for treating wounds. In conclusion, this study uncovers significant associations between diseases and medicinal materials in Ethiopian traditional medicine, emphasizing the therapeutic potential of R. abyssinicus Jacq. These findings provide a foundation for further research, including in vitro and in vivo studies, to explore and validate the efficacy of traditional and natural product-derived medicines.

Bioinformatic elucidation of conserved epitopes to design a potential vaccine candidate against existing and emerging SARS-CoV-2 variants of concern

The COVID-19 pandemic caused by SARS-CoV-2 poses a significant adverse effects on health and economy globally. Due to mutations in genome, COVID-19 vaccine efficacy decreases. We used immuno-informatics to design a Multi epitope vaccine (MEV) candidate for SARS-CoV-2 variants of concern (VOCs). Hence, we predicted binders/epitopes MHC-I, CD8+, MHC-II, CD4+, and CTLs from spike, membrane and envelope proteins of VOCs. In addition, we assessed the conservation of these binders and epitopes across different VOCs. Subsequently, we designed MEV by combining the predicted CTL and CD4+ epitopes from spike protein, peptide linkers, and an adjuvant. Further, we evaluated the binding of MEV candidate against immune receptors namely HLA class I histocompatibility antigen, HLA class II histocompatibility antigen, and TLR4, achieving binding scores of -1265.3, -1330.7, and -1337.9. Molecular dynamics and normal mode analysis revealed stable docking complexes. Moreover, immune simulation suggested MEV candidate elicits both innate and adaptive immune response. We anticipate that this conserved MEV candidate will provide protection from VOCs and emerging strains.

Identification of Potent ADCK3 Inhibitors through Structure-Based Virtual Screening

ADCK3 is a member of the UbiB family of atypical protein kinases in humans, with homologues in archaea, bacteria, and eukaryotes. In lieu of protein kinase activity, ADCK3 plays a role in the biosynthesis of coenzyme Q10 (CoQ10), and inactivating mutations can cause a CoQ10 deficiency and ataxia. However, the exact functions of ADCK3 are still unclear, and small-molecule inhibitors could be useful as chemical probes to elucidate its molecular mechanisms. In this study, we applied structure-based virtual screening (VS) to discover a novel chemical series of ADCK3 inhibitors. Through extensive structural analysis of the active-site residues, we developed a pharmacophore model and applied it to a large-scale VS. Out of ∼170,000 compounds virtually screened, 800 top-ranking candidate compounds were selected and tested in both ADCK3 and p38 biochemical assays for hit validation. In total, 129 compounds were confirmed as ADCK3 inhibitors, and among them, 114 compounds are selective against p38, which was used as a counter-target. Molecular dynamics (MD) simulations were then conducted to predict the binding modes of the most potent compounds within the ADCK3 active site. Through metadynamics analysis, we successfully detected the key amino acid residues that govern intermolecular interactions. The findings provided in this study can serve as a promising starting point for drug development.

Theoretical Investigations on Free Energy of Binding Cilostazol with Different Cyclodextrins as Complex for Selective PDE3 Inhibition

Cilostazol is a phosphodiesterase III inhibitor characterized by poor solubility. This limitation can be overcome by using a drug carrier capable of delivering the drug to the target site. Cyclodextrins are essential as drug carriers because of their outstanding complexation abilities and their capacity to improve drug bioavailability. This study comprises two stages: The first involves verifying different cyclodextrins and their complexation abilities towards cilostazol. This was accomplished using molecular docking simulations (MDS) and density functional theory (DFT). Both techniques indicate that the largest Sulfobutyl Ether-β-Cyclodextrin forms the most stable complex with cilostazol. Additionally, other important parameters of the complex are described, including binding sites, dominant interactions, and thermodynamic parameters such as complexation enthalpy, Gibbs free energy, and Gibbs free energy of solvation. The second stage involves a binding study between cilostazol and Phosphodiesterse3 (PDE3). This study was conducted using molecular docking simulations, and the most important energetic parameters are detailed. This is the first such report, and we believe that the results of our predictions will pave the way for future drug development efforts using cyclodextrin-cilostazol complexes as potential therapeutics.

An in-silico receptor-pharmacophore based multistep molecular docking and simulation study to evaluate the inhibitory potentials against NS1 of DENV-2

DENV-2 strain is the most fatal and infectious of the five dengue virus serotypes. The non-structural protein NS1 encoded by its genome is the most significant protein required for viral pathogenesis and replication inside the host body. Thus, targeting the NS1 protein and designing an inhibitor to limit its stability and secretion is a propitious attempt in our fight against dengue. Four novel inhibitors are designed to target the conserved cysteine residues (C55, C313, C316, and C329) and glycosylation sites (N130 and N207) of the NS1 protein in an attempt to halt the spread of the dengue infection in the host body altogether. Numerous computer-aided drug designing techniques including molecular docking, molecular dynamics simulation, virtual screening, principal component analysis, and dynamic cross-correlation matrix were employed to determine the structural and functional activity of the NS1-inhibitor complexes. From our analysis, it was evident that the extent of structural and atomic level fluctuations of the ligand-bound protein exhibited a declining trend in contrast to unbound protein which was prominently noticeable through the RMSD, RMSF, Rg, and SASA graphs. The ADMET analysis of the four ligands revealed a promising pharmacokinetics and pharmacodynamic profile, along with good bioavailability and toxicity properties. The proposed drugs when bound to the targeted cavities resulted in stable conformations in comparison to their unbound state, implying they have good affinity promising effective drug action. Thus, they can be tested in vitro and used as potential anti-dengue drugs.Communicated by Ramaswamy H. Sarma.

Molecules targeting a novel homotrimer cavity of Spike protein attenuate replication of SARS-CoV-2

The SARS-CoV-2 Spike glycoprotein (S) utilizes a unique trimeric conformation to interact with the ACE2 receptor on host cells, making it a prime target for inhibitors that block viral entry. We have previously identified a novel proteinaceous cavity within the Spike protein homotrimer that could serve as a binding site for small molecules. However, it is not known whether these molecules would inhibit, stimulate, or have no effect on viral replication. To address this, we employed structural-based screening to identify small molecules that dock into the trimer cavity and assessed their impact on viral replication. Our findings show that a cohort of identified small molecules binding to the Spike trimer cavity effectively reduces the replication of various SARS-CoV-2 variants. These molecules exhibited inhibitory effects on B.1 (European original, D614G, EDB2) and B.1.617.2 (δ) variants, while showing moderate activity against the B.1.1.7 (α) variant. We further categorized these molecules into distinct groups based on their structural similarities. Our experiments demonstrated a dose-dependent viral replication inhibitory activity of these compounds, with some, like BCC0040453 exhibiting no adverse effects on cell viability even at high concentrations. Further investigation revealed that pre-incubating virions with compounds like BCC0031216 at different temperatures significantly inhibited viral replication, suggesting their specificity towards the S protein. Overall, our study highlights the inhibitory impact of a diverse set of chemical molecules on the biological activity of the Spike protein. These findings provide valuable insights into the role of the trimer cavity in the viral replication cycle and aid drug discovery programs aimed at targeting the coronavirus family.

Changes of structure properties and potential allergenicity of ovalbumin under high hydrostatic pressures

Egg proteins, notably ovalbumin (OVA), contribute to a prevalent form of food allergy, particularly in children. This study aims to investigate the impact of high hydrostatic pressure (HHP) treatment at varying levels (300, 400, 500, and 600 MPa) on the molecular structure and allergenicity of OVA. The structure of HHP-treated OVA was assessed through fluorescence spectroscopy, circular dichroism spectroscopy, and molecular dynamics (MD) simulation. HHP treatment (600 MPa) altered OVA structures, such as α-helix content decreased from 28.07 % to 19.47 %, and exogenous fluorescence intensity increased by 8.8 times compared to that of the native OVA. The free sulfhydryl groups and zeta potential value were also increased with HHP treatment (600 MPa). ELISA analysis and MD simulation unveiled a noteworthy reduction in the allergenicity of OVA when subjected to 600 MPa for 10 min. Overall, this study suggests that the conformational changes in HHP-treated OVA contribute to its altered allergenicity.

Surface Hydrophobicity Strongly Influences Adsorption and Conformation of Amyloid Beta Derived Peptides

The formation of amyloid fibrils is a common feature of many protein systems. It has implications in both health, as amyloid fibrils are implicated in over 30 degenerative diseases, and in the biological functions of proteins. Surfaces have long been known to affect the formation of fibrils but the specific effect depends on the details of both the surface and protein. Fully understanding the role of surfaces in fibrillization requires microscopic information on protein conformation on surfaces. In this paper replica exchange molecular dynamics simulation is used to investigate the model fibril forming protein, Aβ(10-40) (a 31-residue segment of the amyloid-beta protein) on surfaces of different hydrophobicity. Similar to other proteins Aβ(10-40) is found to adsorb strongly onto hydrophobic surfaces. It also adopts significantly different sets of conformations on hydrophobic and polar surfaces, as well as in bulk solution. On hydrophobic surfaces, it adopts partially helical structures, with the helices overlapping with beta-strand regions in the mature fibril. These may be helical intermediates on the fibril formation pathway, suggesting a mechanism for the enhanced fibril formation seen on hydrophobic surfaces.

Investigating Medin Cleavage Accessibility in MfgE8: Conformational Insights Derived from Molecular Dynamics Simulations and AlphaFold2 Models

Recent studies have indicated that the human amyloidogenic protein medin is associated with a range of vascular diseases, including aortic aneurysms, vascular dementia, and Alzheimer's disease. Medin accumulates in the vasculature with age, leading to endothelial dysfunction through oxidative and nitrative stress and inducing pro-inflammatory activation. Medin is a cleavage product from the C2 domain of MfgE8. The exact mechanism of medin production from MfgE8 is unknown, with crystal structures of homologous C2 domains suggesting that the cleavage sites are buried, requiring a conformational transition for medin production. Molecular dynamics simulations can explore a wide range of conformations, from small-scale bond rotations to large-scale changes like protein folding or ligand binding. This study employed a combination of full-atom and coarse-grained molecular dynamics simulations, along with CONCOORD- and AlphaFold2-generated models, to investigate MfgE8 conformations and their implications for medin cleavage site accessibility. The simulations revealed that MfgE8 tends to adopt a compact conformation with the RGD motif, important for cell attachment within the N-terminal domain, and the medin region in the C-terminal domain close in proximity. Formation of this compact structure is facilitated by interdomain electrostatic interactions that promote stability and in turn decrease the solvent-accessible surface area of the medin region and particularly the C-terminal medin cleavage site. This data enhances current knowledge on medin generation to propose that alterations in local environmental conditions, possibly through changes in glycosylation or other post-translational modifications are required to induce MfgE8 to unfold partially or fully: this would result in enhanced accessibility of the cleavage sites and therefore enable medin generation.

Nanomechanical collective vibration of SARS-CoV-2 spike proteins

The development of effective therapeutics against COVID-19 requires a thorough understanding of the receptor recognition mechanism of the SARS-CoV-2 spike (S) protein. Here the multidomain collective dynamics on the trimer of the spike protein has been analyzed using normal mode analysis (NMA). A common nanomechanical profile was identified in the spike proteins of SARS-CoV-2 and its variants. The profile involves collective vibrations of the receptor-binding domain (RBD) and the N-terminal domain (NTD), which may mediate the physical interaction process. Quantitative analysis of the collective modes suggests a nanomechanical property involving large-scale conformational changes, which explains the difference in receptor binding affinity among different variants. These results support the use of intrinsic global dynamics as a valuable perspective for studying the allosteric and functional mechanisms of the S protein. This approach also provides a low-cost theoretical toolkit for screening potential pathogenic mutations and drug targets.

Identifying Oncogenic Missense Single Nucleotide Polymorphisms in Human SAT1 Gene Using Computational Algorithms and Molecular Dynamics Tools

BACKGROUND

The human SAT1 gene encodes spermidine/spermine N1-acetyltransferase 1 (SSAT1), a regulatory biological catalyst of polyamine catabolism. Numerous essential biological processes, such as cellular proliferation, differentiation, and survival, depend on polyamines like spermidine and spermine. Thus, SSAT1 is involved in key cellular activities such as proliferation and survival of cells and mediates various diseases including cancer. A plethora of studies established the involvement of missense single nucleotide polymorphisms (SNPs) in numerous pathological conditions due to their ability to adversely affect the structure and subsequent function of the protein.

AIMS

To date, an in silico study to identify the pathogenic missense SNPs of the human SAT1 gene has not been accomplished yet. This study aimed to filter the missense SNPs that were functionally detrimental and pathogenic.

METHODS AND RESULTS

The rs757435207 (I21N) was ascertained to be the most deleterious and pathogenic by all algorithmic tools. Stability and evolutionary conservation analysis tools also stated that I21N variant decreased the stability and was located in the highly conserved residue. Molecular dynamics simulation revealed that I21N caused substantial alterations in the conformational stability and dynamics of the SSAT1 protein. Consequently, the I21N variant could disrupt the native functional roles of the SSAT1 enzyme.

CONCLUSION

Therefore, the I21N variant was identified and concluded to be an oncogenic missense variant of the human SAT1 gene. Overall, the findings of this study would be a great directory of future experimental research to develop personalized medicine.

Structure and slow dynamics of protein hydration water with cryopreserving DMSO and trehalose upon cooling

We study, through molecular dynamics simulations, three aqueous solutions with one lysozyme protein and three different concentrations of trehalose and dimethyl sulfoxide (DMSO). We analyze the structural and dynamical properties of the protein hydration water upon cooling. We find that trehalose plays a major role in modifying the structure of the network of HBs between water molecules in the hydration layer of the protein. The dynamics of hydration water presents, in addition to the α-relaxation, typical of glass formers, a slower long-time relaxation process, which greatly slows down the dynamics of water, particularly in the systems with trehalose, where it becomes dominant at low temperatures. In all the solutions, we observe, from the behavior of the α-relaxation times, a shift of the Mode Coupling Theory crossover temperature and the fragile-to-strong crossover temperature toward higher values with respect to bulk water. We also observe a strong-to-strong crossover from the temperature behavior of the long-relaxation times. In the aqueous solution with only DMSO, the transition shifts to a lower temperature than in the case with only lysozyme reported in the literature. We observe that the addition of trehalose to the mixture has the opposite effect of restoring the original location of the strong-to-strong crossover. In all the solutions analyzed in this work, the observed temperature of the protein dynamical transition is slightly shifted at lower temperatures than that of the strong-to-strong crossover, but their relative order is the same, showing a correlation between the motion of the protein and that of the hydration water.

In silico and in vivo study of anti-inflammatory activity of Morinda longissima (Rubiaceae) extract and phytochemicals for treatment of inflammation-mediated diseases

ETHNOPHARMACOLOGICAL RELEVANCE

Traditionally, the plant Morinda longissima Y.Z.Ruan (Rubiaceae) is used by ethnic people in Vietnam for the treatment of liver diseases and hepatitis.

AIM OF THE STUDY

The study was designed to assess the efficacy of the 95% ethanolic extract of Morinda longissima roots (MLE) in experimental immune inflammation. The phytochemical variation of root extract and the chemical structures of natural compounds were also investigated using HPLC-DAD-HR-MS analysis.

MATERIALS AND METHODS

Three different doses (100, 200, and 300 mg/kg b.w.) of MLE were chosen to determine anti-inflammatory activity. The mice were given orally extracts and monitored their behavior and mortality for 14 days to evaluate acute toxicity. The volume of the paw and the histopathological evaluation were carried out. The polyphenolic phytoconstituents of MLE extract were identified using LC/MS analysis. The anti-inflammatory efficacy in silico and molecular docking simulations of these natural products were evaluated based on their cyclooxygenase (COX)-1 and 2 inhibitory effects.

RESULTS

This investigation showed the 95% ethanolic extract of Morinda longissima roots was found non-toxic up to 2000 mg/kg dose level in an acute study, neither showed mortality nor treatment-related signs of toxicity in mice. Eight anthraquinones and anthraquinone glycosides of Morinda longissima roots were identified by HPLC-DAD-HR-MS analysis. In the in vivo experiments, MLE was found to possess powerful anti-inflammatory activities in comparison with diclofenac sodium. The highest anti-inflammatory activity of MLE in mice was observed at a dose of 300 mg/kg body weight. The in silico analysis showed that seven out the eight anthraquinones and anthraquinone glycosides possess a selectivity index RCOX-2/COX-1 lower than 1, indicating that these compounds are selective against the COX-2 enzyme in the following the order: rubiadin-3-methyl ether < morindone morindone-6-methyl ether < morindone-5-methyl ether < damnacanthol < rubiadin < damnacanthol-3-O-β-primeveroside. The natural compounds with the best selectivity against the COX-2 enzyme are quercetin (9), rubiadin-3-methyl ether (7), and morindone (4), with RCOX2/COX1 ratios of 0.02, 0.03, and 0.19, respectively. When combined with the COX-2 protein in the MD research, quercetin and rubiadin-3-methyl ether greatly stabilized the backbone proteins and ligands.

CONCLUSION

In conclusion, the anthraquinones and ethanolic extract of Morinda longissima roots may help fight COX-2 inflammation. To develop novel treatments for inflammatory disorders linked to this one, these chemicals should be investigated more in the future.

Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities

Raman spectroscopy is an important tool in the study of vibrational properties and composition of molecules, peptides, and even proteins. Raman spectra can be simulated based on the change of the electronic polarizability with vibrations, which can nowadays be efficiently obtained via machine learning models trained on first-principles data. However, the transferability of the models trained on small molecules to larger structures is unclear, and direct training on large structures is prohibitively expensive. In this work, we first train two machine learning models to predict the polarizabilities of all 20 amino acids. Both models are carefully benchmarked and compared to density functional theory (DFT) calculations, with the neural network method being found to offer better transferability. By combination of machine learning models with classical force field molecular dynamics, Raman spectra of all amino acids are also obtained and investigated, showing good agreement with experiments. The models are further extended to small peptides. We find that adding structures containing peptide bonds to the training set greatly improves predictions, even for peptides not included in training sets.

Diffusion of curcumin in PLGA-based carriers for drug delivery: a molecular dynamics study

CONTEXT

The rapid growth and diversification of drug delivery systems have been significantly supported by advancements in micro- and nano-technologies, alongside the adoption of biodegradable polymeric materials like poly(lactic-co-glycolic acid) (PLGA) as microcarriers. These developments aim to reduce toxicity and enhance target specificity in drug delivery. The use of in silico methods, particularly molecular dynamics (MD) simulations, has emerged as a pivotal tool for predicting the dynamics of species within these systems. This approach aids in investigating drug delivery mechanisms, thereby reducing the costs associated with design and prototyping. In this study, we focus on elucidating the diffusion mechanisms in curcumin-loaded PLGA particles, which are critical for optimizing drug release and efficacy in therapeutic applications.

METHODS

We utilized MD to explore the diffusion behavior of curcumin in PLGA drug delivery systems. The simulations, executed with GROMACS, modeled curcumin molecules in a representative volume element of PLGA chains and water, referencing molecular structures from the Protein Data Bank and employing the CHARMM force field. We generated PLGA chains of varying lengths using the Polymer Modeler tool and arranged them in a bulk-like environment with Packmol. The simulation protocol included steps for energy minimization, T and p equilibration, and calculation of the isotropic diffusion coefficient from the mean square displacement. The Taguchi method was applied to assess the effects of hydration level, PLGA chain length, and density on diffusion.

RESULTS

Our results provide insight into the influence of PLGA chain length, hydration level, and polymer density on the diffusion coefficient of curcumin, offering a mechanistic understanding for the design of efficient drug delivery systems. The sensitivity analysis obtained through the Taguchi method identified hydration level and PLGA density as the most significant input parameters affecting curcumin diffusion, while the effect of PLGA chain length was negligible within the simulated range. We provided a regression equation capable to accurately fit MD results. The regression equation suggests that increases in hydration level and PLGA density result in a decrease in the diffusion coefficient.

Quantifying the Uncertainty of Force Field Selection on Adsorption Predictions in MOFs

Comparisons between simulated and experimental adsorption isotherms in MOFs are fraught with challenges. On the experimental side, there is significant variation between isotherms measured on the same system, with a significant percentage (∼20%) of published data being considered outliers. On the simulation side, force fields are often chosen "off-the-shelf" with little or no validation. The effect of this choice on the reliability of simulated adsorption predictions has not yet been rigorously quantified. In this work, we fill this gap by systematically quantifying the uncertainty arising from force field selection on adsorption isotherm predictions. We choose methane adsorption, where electrostatic interactions are negligible, to independently study the effect of the framework Lennard-Jones parameters on a series of prototypical materials that represent the most widely studied MOF "families". Using this information, we compute an adsorption "consensus isotherm" from simulations, including a quantification of uncertainty, and compare it against a manually curated set of experimental data from the literature. By considering many experimental isotherms measured by different groups and eliminating outliers in the data using statistical analysis, we conduct a rigorous comparison that avoids the pitfalls of the standard approach of comparing simulation predictions to a single experimental data set. Our results show that (1) the uncertainty in simulated isotherms can be as large as 15% and (2) standard force fields can provide reliable predictions for some systems but can fail dramatically for others, highlighting systematic shortcomings in those models. Based on this, we offer recommendations for future simulation studies of adsorption, including high-throughput computational screening of MOFs.

Molecular interactions of the Omicron, Kappa, and Delta SARS-CoV-2 spike proteins with quantum dots of graphene oxide

CONTEXT

The Omicron, Kappa, and Delta variants are different strains of the SARS-CoV-2 virus. Graphene oxide quantum dots (GOQDs) represent a burgeoning class of oxygen-enriched, zero-dimensional materials characterized by their sub-20-nm dimensions. Exhibiting pronounced quantum confinement and edge effects, GOQDs manifest exceptional physical-chemical attributes. This study delves into the potential of graphene oxide quantum dots, elucidating their inherent properties pertinent to the surface structures of SARS-CoV-2, employing an integrated computational approach for the repositioning of inhibitory agents.

METHODS

Following rigorous adjustment tests, a spectrum of divergent bonding conformations emerged, with particular emphasis placed on identifying the conformation exhibiting optimal adjustment scores and interactions. The investigation employed molecular docking simulations integrating affinity energy evaluations, electrostatic potential clouds, molecular dynamics encompassing average square root calculations, and the computation of Gibbs-free energy. These values quantify the strength of interaction between GOQDs and SARS-CoV-2 spike protein variants. The receptor structures were optimized using the CHARM-GUI server employing force field AMBERFF14SB. The algorithm embedded in CHARMM offers an efficient interpolation scheme and automatic step size selection, enhancing the efficiency of the optimization process. The 3D structures of the ligands are constructed and optimized with density functional theory (DFT) method based on the most stable conformer of each binder. Autodock Vina Software (ADV) was utilized, where essential parameters were specified. Electrostatic potential maps (MEPs) provide a visual depiction of molecules' charge distributions and related properties. After this, molecular dynamics simulations employing the CHARM36 force field in Gromacs 2022.2 were conducted to investigate GOs' interactions with surface macromolecules of SARS-CoV-2 in an explicit aqueous environment. Furthermore, our investigation suggests that lower values indicate stronger binding. Notably, GO-E consistently showed the most negative values across interactions with different variants, suggesting a higher affinity compared to other GOQDs (GO-A to GO-D).

Prediction of the most deleterious non-synonymous SNPs in the human IL1B gene: evidence from bioinformatics analyses

BACKGROUND

Polymorphisms in IL1B play a significant role in depression, multiple inflammatory-associated disorders, and susceptibility to infection. Functional non-synonymous SNPs (nsSNPs) result in changes in the encoded amino acids, potentially leading to structural and functional alterations in the mutant proteins. So far, most genetic studies have concentrated on SNPs located in the IL1B promoter region, without addressing nsSNPs and their association with multifactorial diseases. Therefore, this study aimed to explore the impact of deleterious nsSNPs retrieved from the dbSNP database on the structure and functions of the IL1B protein.

RESULTS

Six web servers (SIFT, PolyPhen-2, PROVEAN, SNPs&GO, PHD-SNP, PANTHER) were used to analyze the impact of 222 missense SNPs on the function and structure of IL1B protein. Five novel nsSNPs (E100K, T240I, S53Y, D128Y, and F228S) were found to be deleterious and had a mutational impact on the structure and function of the IL1B protein. The I-mutant v2.0 and MUPro servers predicted that these mutations decreased the stability of the IL1B protein. Additionally, these five mutations were found to be conserved, underscoring their significance in protein structure and function. Three of them (T240I, D128Y, and F228S) were predicted to be cancer-causing nsSNPs. To analyze the behavior of the mutant structures under physiological conditions, we conducted a 50 ns molecular dynamics simulation using the WebGro online tool. Our findings indicate that the mutant values differ from those of the IL1B wild type in terms of RMSD, RMSF, Rg, SASA, and the number of hydrogen bonds.

CONCLUSIONS

This study provides valuable insights into nsSNPs located in the coding regions of IL1B, which lead to direct deleterious effects on the functional and structural aspects of the IL1B protein. Thus, these nsSNPs could be considered significant candidates in the pathogenesis of disorders caused by IL1B dysfunction, contributing to effective drug discovery and the development of precision medications. Thorough research and wet lab experiments are required to verify our findings. Moreover, bioinformatic tools were found valuable in the prediction of deleterious nsSNPs.

Classical molecular dynamics simulation identifies catechingallate as a promising antiviral polyphenol against MPOX palmitoylated surface protein

Cumulative global prevalence of the emergent monkeypox (MPX) infection in the non-endemic countries has been professed as a global public health predicament. Lack of effective MPX-specific treatments sets the baseline for designing the current study. This research work uncovers the effective use of known antiviral polyphenols against MPX viral infection, and recognises their mode of interaction with the target F13 protein, that plays crucial role in formation of enveloped virions. Herein, we have employed state-of-the-art machine learning based AlphaFold2 to predict the three-dimensional structure of F13 followed by molecular docking and all-atoms molecular dynamics (MD) simulations to investigate the differential mode of F13-polyphenol interactions. Our extensive computational approach identifies six potent polyphenols Rutin, Epicatechingallate, Catechingallate, Quercitrin, Isoquecitrin and Hyperoside exhibiting higher binding affinity towards F13, buried inside a positively charged binding groove. Intermolecular contact analysis of the docked and MD simulated complexes divulges three important residues Asp134, Ser137 and Ser321 that are observed to be involved in ligand binding through hydrogen bonds. Our findings suggest that ligand binding induces minor conformational changes in F13 to affect the conformation of the binding site. Concomitantly, essential dynamics of the six-MD simulated complexes reveals Catechin gallate, a known antiviral agent as a promising polyphenol targeting F13 protein, dominated with a dense network of hydrophobic contacts. However, assessment of biological activities of these polyphenols need to be confirmed through in vitro and in vivo assays, which may pave the way for development of new novel antiviral drugs.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

The synergy between the PscC subunits for electron transfer to the P840 special pair in Chlorobaculum tepidum

The primary photochemical reaction of photosynthesis in green sulfur bacteria occurs in the homodimer PscA core proteins by a special chlorophyll pair. The light induced excited state of the special pair producing P840+ is rapidly reduced by electron transfer from one of the two PscC subunits. Molecular dynamics (MD) simulations are combined with bioinformatic tools herein to provide structural and dynamic insight into the complex between the two PscA core proteins and the two PscC subunits. The microscopic dynamic model involves extensive sampling at atomic resolution and at a cumulative time-scale of 22µs and reveals well defined protein-protein interactions. The membrane complex is composed of the two PscA and the two PscC subunits and macroscopic connections are revealed within a putative electron transfer pathway from the PscC subunit to the special pair P840 located within the PscA subunits. Our results provide a structural basis for understanding the electron transport to the homodimer RC of the green sulfur bacteria. The MD based approach can provide the basis to further probe the PscA-PscC complex dynamics and observe electron transfer therein at the quantum level. Furthermore, the transmembrane helices of the different PscC subunits exert distinct dynamics in the complex.

Identification of phytochemical inhibitors targeting phosphate acetyltransferase of Mycoplasma genitalium: insights from virtual screening and molecular dynamics studies

Mycoplasma genitalium (M. genitalium) has evolved as a superbug, and the developing antimicrobial resistance with just a few treatment options available is an imminent concern. Due to the emergence of antibiotic resistance, a new antibiotic class or medications are required to combat this pathogen. The phosphate acetyltransferase (PTA) enzyme can be a suitable drug target which is essential for M. genitalium survival and involves in acetate metabolism. To efficiently find potent inhibitors, structure-based drug design approaches targeting the PTA of M. genitalium have been established. In this study, the three most potent phytochemical inhibitors were predicted from virtual screening and these are sitostanyl ferulate, beta-sitosterol-beta-D-glucoside, and brassinolide, with binding energies of - 9.66, - 9.60, and - 9.48 kcal/mol, respectively. The active site residues Thr-125, Arg-300, Ser-299, Tyr-272, and Lys-273 appear to be critical in binding the three predicted potent inhibitors. The results of the molecular dynamics study indicate that the three predicted phytochemical inhibitors have formed stable bonds with PTA. Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) was utilized for the estimation of binding free energy of PTA-phytochemical complexes. Taken together, the findings of our computational work might aid in the development of possible potential drugs to treat and ameliorate the severity of M. genitalium infection.

Cryptotanshinone-Induced Permeabilization of Model Phospholipid Membranes: A Biophysical Study

The Danshen terpenoid cryptotanshinone (CPT) is gaining enormous interest in light of its various outstanding biological activities. Among those, CPT has been shown to interact with cell membranes and, for instance, to have antibacterial activity. Several works have shown that CPT alone, or in combination with other drugs, can effectively act as an antibiotic against various infectious bacteria. Some authors have related the mechanism underlying this action to CPT-membrane interaction. This work shows that CPT readily partitions into phosphatidylcholine membranes, but there is a limiting capacity of accommodation of ca. 1 mol CPT to 3 mol phospholipid. The addition of CPT to unilamellar liposomes composed of 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) causes membrane permeabilization, as shown by fluorescent probe leakage. This process has been kinetically studied, as well as its modulation by incorporation of phosphatidylethanolamine or phosphatidylglycerol, as a model for pathogenic cell membranes. The thermotropic behavior of 1,2-dimyristoylphosphatidylcholine (DMPC) model membranes is weakly affected by CPT, but the terpenoid causes significant dehydration of the polar region of the bilayer and weak disordering of the acyl chain palisade, as observed in Fourier-transform infrared spectroscopy (FTIR) results. Small-angle X-ray scattering (SAXS) shows that CPT increases DMPC bilayer thickness, which could be due to localization near the phospholipid/water interface. Molecular dynamics (MD) simulations show that the lateral diffusion coefficient of the phospholipid increases with the presence of CPT. CPT extends from the polar head region to the center of the bilayer, being centered between the carbonyl groups and the unsaturated region of the POPC, where there is greater overlap. Interestingly, the free energy profiles of a water molecule crossing the lipid membrane show that the POPC membrane becomes more permeable in the presence of CPT. In summary, our results show that CPT perturbs the physicochemical properties of the phospholipid membrane and compromises its barrier function, which could be of relevance to explain part of its antimicrobial or anticancer activities.

Novel barostat implementation for molecular dynamics

We propose a novel implementation of the extended-dynamics equations for isothermal-isobaric ensemble in molecular dynamics, as the Martyna-Tobias-Klein thermostat and barostat. This method is suitable for systems with constraints and the Verlet-family integrators. Instead of iterations or the Trotter-expansion-based methods, both velocities and box sizes (scaling of bond lengths) are predicted. The algorithm begins with force calculation, requiring neither quarter nor half time steps, and necessitating iterations only inside SHAKE. Several tests demonstrate that the quality is comparable to other implementations. It is found that the formula relating the extended barostat mass to the characteristic time of volume fluctuations is inaccurate for condensed systems, which has consequences for the parameter setup. Emphasis is also put on the verification of the precise isothermal-isobaric ensemble and finite-size effects.

The functional and structural characterisation of the bZIP transcription factors from Myristica fragrans Houtt. associated to plant disease-resistant defence: An insight from transcriptomics and computational modelling

The bZIP transcription factors play crucial roles in various aspects of plant biology, including development, defence mechanisms, senescence, and responses to both biotic and abiotic environmental stresses. Myristica fragrans Houtt. transcriptome analysis has identified 15 bZIP transcription factors, each exhibiting major conserved domains and motifs such as BRLZ, MFMR, and DOG1. Functional characterisation of these identified MfbZIP factors indicates their predominant localisation within the nucleus. Phylogenetic analysis reveals that MfbZIP factors cluster into three subgroups alongside annotated bZIP sequences from Magnolia sinica and Arabidopsis thaliana. Moreover, gene ontology (GO) analysis highlights several key functions of MfbZIP, including involvement in defence responses, abscisic acid-induced signalling pathways, and DNA-binding transcription factor activity. Further investigation through KEGG pathway analysis reveals that the amino acid sequences of MfbZIP contain binding motifs for proteins such as TGA, implicated in plant hormone signal transduction pathways associated with disease resistance. To confirm the disease-defence-related activity of the TGA binding protein within MfbZIP, we employed amino acid sequences for 3-D ab initio modelling. Subsequently, we analysed TGA-NPR1 interactions using docking and molecular dynamics simulation analysis. These analyses shed light on the functional and structural aspects of TGA, demonstrating its stable association with NPR1 protein and its significance in the expression of PR1 protein, thus playing a pivotal role in defence responses against pathogens.

Computational analysis of non-synonymous single nucleotide polymorphism in the bovine PKLR geneComputational analysis of bovine PKLR gene

Pyruvate kinase (PKLR) is a potential candidate gene for milk production traits in cows. The main aim of this work is to investigate the potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the PKLR gene by using several computational tools. In silico tools including SIFT, Polyphen-2, SNAP2 and Panther indicated only 18 nsSNPs out of 170 were considered deleterious. The analysis of proteins' stability change due to amino acid substitution performed by the use of the I-mutant, MUpro, CUPSTAT, SDM and Dynamut confirmed that 9 nsSNPs decreased protein stability. ConSurf analysis predicted that all 18 nsSNPs were evolutionary moderately or highly conserved. Two different domains of PKLR protein were revealed by the InterPro tool with 12 nsSNPs positioned in the Pyruvate Kinase barrel domain and 6 nsSNP present in the Pyruvate Kinase C Terminal. The PKLR 3D model was predicted by MODELLER software and validated via Ramachandran plot and Prosa which indicated a good quality model. The analysis of energy minimizations for the native and mutated structures was performed by SWISS PDB viewer with GROMOS 96 program and showed that 3 structural and 4 functional residues had total energy higher than the native model. These findings indicate that these mutant structures (rs441424814, rs449326723, rs476805413, rs472263384, rs474320860, rs475521477, rs441633284) were less stable than the native model. Molecular Dynamics simulations were performed to confirm the impact of nsSNPs on the protein structure and function. The present study provides useful information about functional SNPs that have an impact on PKLR protein in cattle.Communicated by Ramaswamy H. Sarma.

Investigating the effect of SUMO fusion on solubility and stability of amylase-catalytic domain from Pyrococcus abyssi

Alpha amylase belonging to starch hydrolyzing enzymes has significant contributions to different industrial processes. The enzyme production through recombinant DNA technology faces certain challenges related to their expression, solubility and purification, which can be overcome through fusion tags. This study explored the influence of SUMO, a protein tag reported to enhance the solubility and stability of target proteins when fused to the N-terminal of the catalytic domain of amylase from Pyrococcus abyssi (PaAD). The insoluble expression of PaAD in E. coli was overcome when the enzyme was expressed in a fusion state (S-PaAD) and culture was cultivated at 18 °C. Moreover, the activity of S-PaAD increased by 1.5-fold as compared to that of PaAD. The ligand binding and enzyme activity assays against different substrates demonstrated that it was more active against 1 % glycogen and amylopectin. The analysis of the hydrolysates through HPLC demonstrated that the enzyme activity is mainly amylolytic, producing longer oligosaccharides as the major end product. The secondary structure analyses by temperature ramping in CD spectroscopy and MD simulation demonstrated the enzymes in the free, as well as fusion state, were stable at 90 °C. The soluble production, thermostability and broad substrate specificity make this enzyme a promising choice for various foods, feed, textiles, detergents, pharmaceuticals, and many industrial applications.

Investigating the Antiviral Properties of Nyctanthes arbor-tristis Linn against the Ebola, SARS-CoV-2, Nipah, and Chikungunya Viruses: A Computational Simulation Study

Background: The hunt for naturally occurring antiviral compounds to combat viral infection was expedited when COVID-19 and Ebola spread rapidly. Phytochemicals from Nyctanthes arbor-tristis Linn were evaluated as significant inhibitors of these viruses. Methods: Computational tools and techniques were used to assess the binding pattern of phytochemicals from Nyctanthes arbor-tristis Linn to Ebola virus VP35, SARS-CoV-2 protease, Nipah virus glycoprotein, and chikungunya virus. Results: Virtual screening and AutoDock analysis revealed that arborside-C, beta amyrin, and beta-sitosterol exhibited a substantial binding affinity for specific viral targets. The arborside-C and beta-sitosterol molecules were shown to have binding energies of -8.65 and -9.11 kcal/mol, respectively, when interacting with the major protease. Simultaneously, the medication remdesivir exhibited a control value of -6.18 kcal/mol. The measured affinity of phytochemicals for the other investigated targets was -7.52 for beta-amyrin against Ebola and -6.33 kcal/mol for nicotiflorin against Nipah virus targets. Additional molecular dynamics simulation (MDS) conducted on the molecules with significant antiviral potential, specifically the beta-amyrin-VP35 complex showing a stable RMSD pattern, yielded encouraging outcomes. Conclusions: Arborside-C, beta-sitosterol, beta-amyrin, and nicotiflorin could be established as excellent natural antiviral compounds derived from Nyctanthes arbor-tristis Linn. The virus-suppressing phytochemicals in this plant make it a compelling target for both in vitro and in vivo research in the future.

Single Ferritin Nanocages Expressing SARS-CoV-2 Spike Variants to Receptor and Antibodies

SARS-CoV-2 virus variants of concern (VOCs) have rapidly changed their transmissibility and pathogenicity primarily through mutations in the structural proteins. Herein, we present molecular details with dynamics of the ferritin nanocages stitched with synthetic chimeras displaying the Spike receptor binding domains (RBDs). Our findings demonstrated the potential usage of ferritin-based vaccines that may effectively inhibit viral entry by blocking the Spike-ACE2 network and may induce cross-protective antibody responses. Taking the nanocage constructs into consideration, we evaluated the effects of variants on the docked interface of the SARS-CoV-2 Spike RBD with the ACE2 (angiotensin-converting enzyme 2) host cell receptor and neutralizing antibodies (Abs). Investigating the VOCs revealed that most of the mutations reported a possibly reduced structural stability within the Spike RBD domain. Point mutations have moderate or no effect for VVH-72, CR3022, and S309 Abs when bound with the Spike RBD, whereas a significant effect was observed for B38, CB6, and m396 over the surface of the H-ferritin nanocage. In addition to providing useful therapeutic approaches against COVID-19 (coronavirus disease 2019), these structural details can also be used to fight future coronavirus outbreaks.

Reference Data Set for Circular Dichroism Spectroscopy Comprised of Validated Intrinsically Disordered Protein Models

Circular dichroism (CD) spectroscopy is an analytical technique that measures the wavelength-dependent differential absorbance of circularly polarized light and is applicable to most biologically important macromolecules, such as proteins, nucleic acids, and carbohydrates. It serves to characterize the secondary structure composition of proteins, including intrinsically disordered proteins, by analyzing their recorded spectra. Several computational tools have been developed to interpret protein CD spectra. These methods have been calibrated and tested mostly on globular proteins with well-defined structures, mainly due to the lack of reliable reference structures for disordered proteins. It is therefore still largely unclear how accurately these computational methods can determine the secondary structure composition of disordered proteins. Here, we provide such a required reference data set consisting of model structural ensembles and matching CD spectra for eight intrinsically disordered proteins. Using this set of data, we have assessed the accuracy of several published CD prediction and secondary structure estimation tools, including our own CD analysis package, SESCA. Our results show that for most of the tested methods, their accuracy for disordered proteins is generally lower than for globular proteins. In contrast, SESCA, which was developed using globular reference proteins, but was designed to be applicable to disordered proteins as well, performs similarly well for both classes of proteins. The new reference data set for disordered proteins should allow for further improvement of all published methods.

Molecular Dynamics Simulations of the Short-Chain Fluorocarbon Surfactant PFHXA and the Anionic Surfactant SDS at the Air/Water Interface

The research and development of alternatives to long-chain fluorocarbon surfactants are desperately needed because they are extremely toxic, difficult to break down, seriously harm the environment, and limit the use of conventional aqueous film-forming foam fire extinguishing agents. In this study, mixed surfactant systems containing the short-chain fluorocarbon surfactant perfluorohexanoic acid (PFHXA) and the hydrocarbon surfactant sodium dodecyl sulfate (SDS) were investigated by molecular dynamics simulation to investigate the microscopic properties at the air/water interface at different molar ratios. Some representative parameters, such as surface tension, degree of order, density distribution, radial distribution function, number of hydrogen bonds, and solvent-accessible surface area, were calculated. Molecular dynamics simulations show that compared with a single type of surfactant, mixtures of surfactants provide superior performance in improving the interfacial properties of the gas-liquid interface. A dense monolayer film is formed by the strong synergistic impact of the two surfactants. Compared to the pure SDS system, the addition of PFHXA caused SDS to be more vertically oriented at the air/water interface with a reduced tilt angle, and a more ordered structure of the mixed surfactants was observed. Hydrogen bonding between SDS headgroups and water molecules is enhanced with the increasing PFHXA. The surface activity is arranged in the following order: PFHXA/SDS = 1:1 > PFHXA/SDS = 3:1 > PFHXA/SDS = 1:3. These results indicate that a degree of synergistic relationship exists between PFHXA and SDS at the air/water interface.

Simulation Study of Polyethylene Terephthalate Hydrolase Adsorption on Self-Assembled Monolayers

Polyethylene terephthalate (PET) hydrolase, discovered in Ideonella sakaiensis (IsPETase), is a promising agent for the biodegradation of PET under mild reaction conditions, yet the thermal stability is poor. The efficient immobilization and orientation of IsPETase on different solid substrates are essential for its application. In this work, the combined parallel tempering Monte Carlo simulation with the all-atom molecular dynamics simulation approach was adopted to reveal the adsorption mechanism, orientation, and conformational changes of IsPETase adsorbed on charged self-assembled monolayers (SAMs), including COOH-SAM and NH2-SAM with different surface charge densities (SCDs). The results show that the protein adsorption orientation was determined not only by attraction interactions but also by repulsion interactions. IsPETase is adsorbed on the COOH-SAM surface with an "end-on" orientation, which favors the exposure of the catalyzed triplet to the solution. In addition, the entrance to the catalytic active center is larger on the COOH-SAM surface with a low SCD. This work reveals the controlled orientation and conformational information on IsPETase on charged surfaces at the atomistic level. This study would certainly promote our understanding of the mechanism of IsPETase adsorption and provide theoretical support for the design of substrates for IsPETase immobilization.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Computational Deciphering of the Role of S100A8 and S100A9 Proteins and Their Changes in the Structure Assembly Influences Their Interaction with TLR4, RAGE, and CD36

S100A8 and S100A9 belong to the calcium-binding, damage associated molecular pattern (DAMP) proteins shown to aggravate the pathogenesis of rheumatoid arthritis (RA) through their interaction with the TLR4, RAGE and CD36 receptors. S100A8 and S100A9 proteins tend to exist in monomeric, homo and heterodimeric forms, which have been implicated in the pathogenesis of RA, via interacting with Pattern Recognition receptors (PRRs). The study aims to assess the influence of changes in the structure and biological assembly of S100A8 and S100A9 proteins as well as their interaction with significant receptors in RA through computational methods and surface plasmon resonance (SPR) analysis. Molecular docking analysis revealed that the S100A9 homodimer and S100A8/A9 heterodimer showed higher binding affinity towards the target receptors. Most S100 proteins showed good binding affinity towards TLR4 compared to other receptors. Based on the 50 ns MD simulations, TLR4, RAGE, and CD36 formed stable complexes with the monomeric and dimeric forms of S100A8 and S100A9 proteins. However, SPR analysis showed that the S100A8/A9 heterodimers formed stable complexes and exhibited high binding affinity towards the receptors. SPR data also indicated that TLR4 and its interactions with S100A8/A9 proteins may play a primary role in the pathogenesis of RA, with additional contributions from CD36 and RAGE interactions. Subsequent in vitro and in vivo investigations are warranted to corroborate the involvement of S100A8/A9 and the expression of TLR4, RAGE, and CD36 in the pathophysiology of RA.

In vitro and in silico analysis for elucidation of antioxidant potential of Djiboutian Avicennia Marina (Forsk.) Vierh. phytochemicals

The present work aims to study the phytochemical composition, the antioxidant capacity of the crude extracts, and the fraction of extract giving the best antioxidant activity of Avicennia marina. The leaves contain high TFC compared to other parts of the plant, whereas fruits have the highest amount of TPC. Fat-soluble pigments are strongly present in the leaves of Avicennia marina i.e. β-carotene, lycopene, chlorophyll a, and chlorophyll b. The crude methanolic flower extracts showed strong DPPH and ABTS radical scavenging activity with IC50 values of 0.30 and 0.33 mg/mL respectively compared to the leaf and stem methanolic extracts for the DPPH and ABTS models with a value IC50 greater than 1 mg/mL. The crude fruit extract shows good activity with the ABTS model, unlike the DPPH model whose IC50 values are 0.95 and 0.38 mg/mL, respectively. Fractionation improved the antioxidant effect of crude flower extract. The ethyl acetate fraction exhibits the best antioxidant activity for both DPPH and ABTS methods with IC50 values of 0.125 and 0.16 mg/mL. The HR-LCMS/MS led to the identification of 13 compounds: 6 flavonoids and 7 iridoid glycoside compounds in the different parts of the plant. A bioinformatics study was performed to evaluate the antioxidant activity of the three major Iridoid glycosides towards the target protein Catalase compound II through free binding energy. Out of these three iridoid glycosides, compound C10 does not represent any toxicity, unlike C8 and C9 which showed an irritancy effect. Furthermore, molecular dynamics shows good stability of the C10-2CAG complex. HighlightsExtraction and fractionation of different part (leaf, stem, flower and fruit) of Avicennia marina.Botanical description and phytochemical analysis of crude extract methanolic. Investigation by HR-LCMS characterization of polyphenols and iridoid glycosides.Evaluation the antioxidant activity of crudes extracts methanolics by two methods in vitro DPPH and ABTS.Antioxidant activity of the fraction of the crude flower extracts presenting the best biological response.Evaluate the contribution of three major compounds 2'-Cinnamoylmussaenosidic acid, 10-O-[E-Cinnamoyl]-geniposidic acid and 10-O-[(E)-p-Coumaroyl]-geniposidic acid in the ethyl acetate fraction on the antioxidant activity through docking and dynamic molecular.Communicated by Ramaswamy H. Sarma.

Integrating physics in deep learning algorithms: a force field as a PyTorch module

MOTIVATION

Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields, however, cannot interface with deep learning algorithms due to their implementation.

RESULTS

We present MadraX, a forcefield implemented as a differentiable PyTorch module, able to interact with deep learning algorithms in an end-to-end fashion.

AVAILABILITY AND IMPLEMENTATION

MadraX documentation, together with tutorials and installation guide, is available at madrax.readthedocs.io.