All-atom empirical potential for molecular modeling and dynamics studies of proteins

Rational design of monomeric IL37 variants guided by stability and dynamical analyses of IL37 dimers

IL37 plays important roles in the regulation of innate immunity and its oligomeric status is critical to these roles. In its monomeric state, IL37 can effectively inhibit the inflammatory response of IL18 by binding to IL18Rα, a capacity lost in its dimeric form, underlining the pivotal role of the oligomeric status of IL37 in its anti-inflammatory action. Until now, two IL37 dimer structures have been deposited in PDB, reflecting a substantial difference in their dimer interfaces. Given this discrepancy, we analyzed the PDB structures of the IL37 dimer (PDB IDs: 6ncu, 5hn1) along with a AF2-multimer prediction by molecular dynamics (MD) simulations. Results showed that the 5hn1 and AF2-predicted dimers have the same interface and stably maintained their conformations throughout simulations, while the recent IL37 dimer (PDB ID: 6ncu) with a different interface did not, proposing a possible issue with the recent IL37 dimer structure (6ncu). Next, focusing on the stable dimer structures, we have identified five critical positions of V71/Y85/I86/E89/S114, three new positions compared to the literature, that would reduce dimer stability without affecting the monomer structure. Two quintuple mutants were tested by MD simulations and showed partial or complete dissociation of the dimer. Overall, the insights gained from this study reinforce the validity of the 5hn1 and AF2 multimer structures, while also advancing our understanding of the IL37 dimer interface through the generation of monomer-locked IL37 variants.

Catalytic mechanism, computational design, and crystal structure of a highly specific and efficient benzoylecgonine hydrolase

Enzyme therapy for cocaine detoxification should break down both cocaine and its primary toxic metabolite, benzoylecgonine (BZE), which is also the main form of cocaine contaminant in the environment. An ideal BZE-metabolizing enzyme (BZEase) is expected to be highly efficient and selective in BZE hydrolysis. Here, BZEase4 was engineered from bacterial cocaine esterase (CocE) by our reactant state-based enzyme design theories (RED), which has a 34,977-fold improved substrate discrimination between BZE and the neurotransmitter acetylcholine (ACh), compared with wild-type CocE. Under the physiological concentrations of BZE and ACh, the reaction velocity of BZEase4 against BZE is 2.25 × 106-fold higher than it against ACh, suggesting BZEase4 has extremely high substrate selectivity for BZE over ACh to minimize the potential cholinergic side-effects. This study provides additional evidence supporting the further development of BZEase4 toward a promising therapeutic for cocaine overdose, a potentially effective and eco-friendly enzymatic method for BZE degradation in the environment.

Phosphopeptide binding to the N-SH2 domain of tyrosine phosphatase SHP2 correlates with the unzipping of its central β-sheet

SHP2 is a tyrosine phosphatase that plays a regulatory role in multiple intracellular signaling cascades and is known to be oncogenic in certain contexts. In the absence of effectors, SHP2 adopts an autoinhibited conformation with its N-SH2 domain blocking the active site. Given the key role of N-SH2 in regulating SHP2, this domain has been extensively studied, often by X-ray crystallography. Using a combination of structural analyses and molecular dynamics (MD) simulations we show that the crystallographic environment can significantly influence the structure of the isolated N-SH2 domain, resulting in misleading interpretations. As an orthogonal method to X-ray crystallography, we use a combination of NMR spectroscopy and MD simulations to accurately determine the conformation of apo N-SH2 in solution. In contrast to earlier reports based on crystallographic data, our results indicate that apo N-SH2 in solution primarily adopts a conformation with a fully zipped central β-sheet, and that partial unzipping of this β-sheet is promoted by binding of either phosphopeptides or even phosphate/sulfate ions.

Structure-based virtual screening and drug repurposing studies indicate potential inhibitors of bovine papillomavirus E6 oncoprotein

Bovine papillomavirus type 1 (BPV1) is an oncogenic virus that causes lesions and cancer in infected cattle. Despite being one of the most studied genotypes in the family and occurring in herds worldwide, there are currently no vaccines or drugs for its control. The viral E6 oncoprotein plays a crucial role in infection by this virus, making it a promising target for the development of new therapies. In this regard, we integrated structure-based virtual screening approaches, drug repositioning, and molecular dynamics to identify approved drugs with the potential to inhibit BPV1 E6. Our results reveal that Lumacaftor and MK-3207 are promising candidates for controlling BPV1 infection. The findings of this study may contribute to the development of E6 oncoprotein blockers in an accelerated and cost-effective manner.

A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T Cell Receptors Bound to Neoantigens

T cell receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is crucial for adaptive immune response. The identification of therapeutically relevant TCR-pMHC protein pairs is a bottleneck in the implementation of TCR-based immunotherapies. The ability to computationally design TCRs to target a specific pMHC requires automated integration of next-generation sequencing, protein-protein structure prediction, molecular dynamics, and TCR ranking. A pipeline to evaluate patient-specific, sequence-based TCRs to a target pMHC is presented. Using the three most frequently expressed TCRs from 16 colorectal cancer patients, the protein-protein structure of the TCRs to the target CEA peptide-MHC is predicted using Modeller and ColabFold. TCR-pMHC structures are compared using automated equilibration and successive analysis. ColabFold generated configurations require an ≈2.5× reduction in equilibration time of TCR-pMHC structures compared to Modeller. The structural differences between Modeller and ColabFold are demonstrated by root mean square deviation (≈0.20 nm) between clusters of equilibrated configurations, which impact the number of hydrogen bonds and Lennard-Jones contacts between the TCR and pMHC. TCR ranking criteria that may prioritize TCRs for evaluation of in vitro immunogenicity are identified, and this ranking is validated by comparing to state-of-the-art machine learning-based methods trained to predict the probability of TCR-pMHC binding.

Molecular Mechanism of the β3AR Agonist Activity of a β-Blocker

Development of subtype-selective drugs for G protein-coupled receptors poses a significant challenge due to high similarity between subtypes, as exemplified by the three β-adrenergic receptors (βARs). The β3AR agonists show promise for treating the overactive bladder or preterm birth, but their potential is hindered by off-target activation of β1AR and β2AR. Interestingly, several β-blockers, which are antagonists of the β1ARs and β2ARs, have been reported to exhibit agonist activity at the β3AR. However, the molecular mechanism remains elusive. Understanding the underlying mechanism should facilitate the development of β3AR agonists with improved selectivity and reduced off-target effects. In this work, we determined the structures of human β3AR in complex with the endogenous agonist epinephrine or with a synthetic β3AR agonist carazolol, which is also a high-affinity β-blocker. Structure comparison, mutagenesis studies and molecular dynamics simulations revealed that the differences on the flexibility of D3.32 directly contribute to carazolol's distinct activities as an antagonist for the β2AR and an agonist for the β3AR. The process is also indirectly influenced by the extracellular loops (ECL), especially ECL1. Taken together, these results provide key guidance for development of selective β3AR agonists, paving the way for new therapeutic opportunities.

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

Structural modeling of phosphatidylinositol 3-kinase-γ with novel derivatives of stilbenoids

Phosphatidylinositol 3-kinases (PI3K) form a family of lipid kinases that catalyze the phosphorylation of 3-hydroxyl group of the inositol ring of phosphatidylinositol and its derivatives. It is implicated in inflammatory disorders and cancer thus making it an attractive drug target. Crystal structure of human PI3Kγ was taken and structure was completed using MODELLER and validated using PROCHECK. Stilbenoid molecules, piceatannol and resveratrol, were docked to kinase domain of PI3Kγ using AutoDock Vina and docked complexes were subjected to molecular dynamic simulations using Desmond suite of programmes. Based on the structural analysis of these complexes, modified derivatives of the native molecules were designed, docked and molecular dynamic simulations were performed. Kinase domain has a bi-lobar structure with ATP binding site lying in the cleft connecting the two lobes that are primarily composed of 12 α-helices and 8 β-strands. Piceatannol and resveratrol bind at the ATP binding site, with one its rings in a position primarily occupied by adenine of ATP making a hydrogen bond with backbone of Val882. Molecules also make interactions with Lys833 and several isoleucine residues. Interactions with Ser806 appear to be crucial for the loop conformation and compactness. Derivative molecules of stilbenoids also occupy the ATP binding cleft and the chemical modifications result in hydrogen bonded interactions to Glu880, and ionic interactions to Lys833 and Lys808 thereby enhancing their potencies in comparison to native molecules. Biophysical parameters and quality of interactions of stilbenoid derivatives augurs well for development of potent and specific inhibitory molecules against PI3Kγ enzyme.

Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions

Silica nanoparticles (SNPs), one of the most widely researched materials in modern science, are now commonly exploited in surface coatings, biomedicine, catalysis, and engineering of novel self-assembling materials. Theoretical approaches are invaluable to enhancing fundamental understanding of SNP properties and behavior. Tremendous research attention is dedicated to modeling silica structure, the silica-water interface, and functionalization of silica surfaces for tailored applications. In this review, the range of theoretical methodologies are discussed that have been employed to model bare silica and functionalized silica. The evolution of silica modeling approaches is detailed, including classical, quantum mechanical, and hybrid methods and highlight in particular the last decade of theoretical simulation advances. It is started with discussing investigations of bare silica systems, focusing on the fundamental interactions at the silica-water interface, following with a comprehensively review of the modeling studies that examine the interaction of silica with functional ligands, peptides, ions, surfactants, polymers, and carbonaceous species. The review is concluded with the perspective on existing challenges in the field and promising future directions that will further enhance the utility and importance of the theoretical approaches in guiding the rational design of SNPs for applications in engineering and biomedicine.

Force Field Limitations of All-Atom Continuous Constant pH Molecular Dynamics

All-atom constant pH molecular dynamics simulations offer a powerful tool for understanding pH-mediated and proton-coupled biological processes. As the protonation equilibria of protein side chains are shifted by electrostatic interactions and desolvation energies, pKa values calculated from the constant pH simulations may be sensitive to the underlying protein force field and water model. Here we investigated the force field dependence of the all-atom particle mesh Ewald (PME) continuous constant pH (PME-CpHMD) simulations of a mini-protein BBL. The replica-exchange titration simulations based on the Amber ff19sb and ff14sb force fields with the respective water models showed significantly overestimated pKa downshifts for a buried histidine (His166) and for two glutamic acids (Glu141 and Glu161) that are involved in salt-bridge interactions. These errors (due to undersolvation of neutral histidines and overstabilization of salt bridges) are consistent with the previously reported pKa's based on the CHARMM c22/CMAP force field, albeit in larger magnitudes. The pKa calculations also demonstrated that ff19sb with OPC water is significantly more accurate than ff14sb with TIP3P water, and the salt-bridge related pKa downshifts can be partially alleviated by the atom-pair specific Lennard-Jones corrections (NBFIX). Together, these data suggest that the accuracies of the protonation equilibria of proteins from constant pH simulations can significantly benefit from improvements of force fields.

Diffusion and Viscosity in Mixed Protein Solutions

The viscosity and diffusion properties of crowded protein systems were investigated with molecular dynamics simulations of SH3 mixtures with different crowders, and results were compared with experimental data. The simulations accurately reproduced experimental trends across a wide range of protein concentrations, including highly crowded environments up to 300 g/L. Notably, viscosity increased with crowding but varied little between different crowder types, while diffusion rates were significantly reduced depending on protein-protein interaction strength. Analysis using the Stokes-Einstein relation indicated that the reduction in diffusion exceeded what was expected from viscosity changes alone, with the additional slow-down attributable to transient cluster formation driven by weakly attractive interactions. Contact kinetics analysis further revealed that longer-lived interactions contributed more significantly to reduced diffusion rates than short-lived interactions. This study also highlights the accuracy of current computational methodologies for capturing the dynamics of proteins in highly concentrated solutions and provides insights into the molecular mechanisms affecting protein mobility in crowded environments.

mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics

Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science.

Coordination of transporter, cargo, and membrane properties during non-vesicular lipid transport

Homeostasis of cellular membranes is maintained by fine-tuning their lipid composition. Yeast lipid transporter Osh6, belonging to the oxysterol-binding protein-related proteins family, was found to participate in the transport of phosphatidylserine (PS). PS synthesized in the endoplasmic reticulum is delivered to the plasma membrane, where it is exchanged for phosphatidylinositol 4-phosphate (PI4P). PI4P provides the driving force for the directed PS transport against its concentration gradient. In this study, we employed an in vitro approach to reconstitute the transport process into the minimalistic system of large unilamellar vesicles to reveal its fundamental biophysical determinants. Our study draws a comprehensive portrait of the interplay between the structure and dynamics of Osh6, the carried cargo lipid, and the physical properties of the involved membranes, with particular attention to the presence of charged lipids and to membrane fluidity. Specifically, we address the role of the cargo lipid, which, by occupying the transporter, imposes changes in its dynamics and, consequently, predisposes the cargo to disembark in the correct target membrane.

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics

All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities.

Development of Multiscale Force Field for Actinide (An3+) Solutions

A multiscale force field (FF) is developed for an aqueous solution of trivalent actinide cations An3+ (An = U, Np, Pu, Am, Cm, Bk, and Cf) by using a 12-6-4 Lennard-Jones type potential considering ion-induced dipole interaction. Potential parameters are rigorously and automatically optimized by the meta-multilinear interpolation parametrization (meta-MIP) algorithm via matching the experimental properties, including ion-oxygen distance (IOD) and coordination number (CN) in the first solvation shell and hydration free energy (HFE). The water solvent models incorporate an especially developed polar coarse-grained (CG) water scheme named PW32 and three widely used all-atom (AA) level SPC/E, TIP3P, and TIP4P water schemes. Each PW32 is modeled as two bonded beads to represent three neighboring water molecules, the simulation efficiency of which is 1 to 2 orders of magnitude higher than that of AA waters. The newly developed FF shows high accuracy and transferability in reproducing the IOD, CN, and HFE of An3+. The molecular structure and water exchange dynamics of the first An3+ hydration shell and the ionic (van der Waals) radii are reinvestigated in this work. Moreover, the new FF can readily be transferred to other popular FFs, as it has practicably predicted the permeability of An3+ in a graphene oxide filter within the framework of optimized potentials for liquid simulations (OPLS)-AA FF. It holds promise for applications in exploring actinide aqueous solutions with multiscale computational overhead.

Design and Synthesis of Triazine-Based Hydrogel for Combined Targeted Doxorubicin Delivery and PI3K Inhibition

Melanoma, an aggressive skin cancer originating from melanocytes, presents substantial challenges due to its high metastatic potential and resistance to conventional therapies. Hydrogels, three-dimensional networks of hydrophilic polymers with high water-retention capacities, offer significant promise for controlled drug delivery applications. In this study, we report the synthesis and characterization of hydrogelators based on the triazine molecular scaffold, which self-assemble into fibrous networks conducive to hydrogel formation. Rheological analysis confirmed their hydrogelation properties, while microscopic techniques including FE-SEM and FEG-TEM provided insights into their morphological networks. The drug delivery capability of these hydrogelators was evaluated using doxorubicin, a widely employed anticancer agent, demonstrating enhanced biocompatibility and reduced side effects compared to free doxorubicin. Additionally, the hydrogelators exhibited inhibitory activity against phosphoinositide 3-kinase (PI3K), a key enzyme frequently mutated in cancer, and also involved in melanoma progression. The dual functionality of this delivery system - controlled drug release and PI3K inhibition - highlights the potential of triazine-based hydrogelators as innovative therapeutic platforms for melanoma treatment.

Analysis of transition rates from variational flooding using analytical theory

Variational flooding is an enhanced sampling method for obtaining kinetic rates from molecular dynamics simulations. This method is inspired by the idea of conformational flooding that employs a boost potential acting along a chosen reaction coordinate to accelerate rare events. In this work, we show how the empirical distribution of crossing times from variational flooding simulations can be modeled with analytical Kramers' time-dependent rate (KTR) theory. An optimized bias potential that fills metastable free energy basins is constructed from the variationally enhanced sampling (VES) method. This VES-derived flooding potential is then augmented by a switching function that determines the fill level of the boost. Having a prescribed time-dependent fill rate of the flooding potential gives an analytical expression for the distribution of crossing times from KTR theory that is used to extract unbiased rates. In the case of a static boost potential, the distribution of barrier crossing times follows an expected exponential distribution, and unbiased rates are extracted from a series of boosted simulations at discrete fill levels. Introducing a time-dependent boost that increases the fill level gradually over the simulation time leads to a simplified procedure for fitting the biased distribution of crossing times to analytical theory. We demonstrate the approach for the paradigmatic cases of alanine dipeptide in vacuum, the asymmetric SN2 reaction, and the folding of chignolin in explicit solvent.

Metal Ion Sensing by Tetraloop-like RNA Fragment: Role of Compact Intermediates with Non-Native Metal Ion-RNA Inner-Shell Contacts

Divalent metal ions influence the folding and function of ribonucleic acid (RNA) in the cells. The mechanism of how RNA structural elements in riboswitches sense specific metal ions is unclear. RNA interacts with ions through two distinct binding modes: direct interaction between the ion and RNA (inner-shell (IS) coordination) and indirect interaction between the ion and RNA mediated through water molecules (outer-shell (OS) coordination). To understand how RNA senses metal ions such as Mg2+ and Ca2+, we studied the folding of a small RNA segment from the Mg2+ sensing M-Box riboswitch using computer simulations. This RNA segment has the characteristics of a GNRA tetraloop motif and interestingly requires the binding of a single Mg2+ ion. The folding free energy surface of this simple tetraloop system is multidimensional, with a population of multiple intermediates where the tetraloop and cation interact through IS and OS coordination. The partially folded compact tetraloop intermediates form multiple non-native IS contacts with the metal ion. Thermal fluctuations should break these strong non-native IS contacts so that the tetraloop can fold to the native state, resulting in higher folding free energy barriers. Ca2+ undergoes rapid OS to IS transitions and vice versa due to its lower charge density than Mg2+. However, the ability of Ca2+ to stabilize the native tetraloop state is weaker, as it could not hold the loop-closing nucleotides together due to its weaker interactions with the nucleotides. These insights are critical to understanding the specific ion sensing mechanisms in riboswitches, and the predictions are amenable for verification by nuclear magnetic resonance (NMR) experiments.

Activation of the Influenza B M2 Proton Channel (BM2)

Influenza B viruses have cocirculated during most seasonal flu epidemics and can cause significant human morbidity and mortality due to their rapid mutation, emerging drug resistance, and severe impact on vulnerable populations. The influenza B M2 proton channel (BM2) plays an essential role in viral replication, but the mechanisms behind its symmetric proton conductance and the involvement of a second histidine (His27) cluster remain unclear. Here we performed membrane-enabled continuous constant-pH molecular dynamics simulations on wildtype BM2 and a key H27A mutant channel to explore its pH-dependent conformational switch. Simulations captured the activation as the first histidine (His19) protonates and revealed the transition at lower pH values compared to AM2 is a result of electrostatic repulsions between His19 and preprotonated His27. Crucially, we provided an atomic-level understanding of the symmetric proton conduction by identifying preactivating channel hydration in the C-terminal portion. This research advances our understanding of the function of BM2 function and lays the groundwork for further chemically reactive modeling of the explicit proton transport process as well as possible antiflu drug design efforts.

Engineering the Mechanical Stability of a Therapeutic Complex between Affibody and Programmed Death-Ligand 1 by Anchor Point Selection

Protein-protein complexes can vary in mechanical stability depending on the direction from which force is applied. Here, we investigated the mechanical stability of a complex between a binding scaffold called Affibody and an immune checkpoint protein Programmed Death-Ligand 1 (PD-L1). We used AFM single-molecule force spectroscopy with bioorthogonal clickable peptide handles, shear stress bead adhesion assays, molecular modeling, and steered molecular dynamics (SMD) to understand the pulling point dependency of the mechanostability of the Affibody:(PD-L1) complex. We observed a wide range of rupture forces depending on the anchor point. Pulling from residue #22 on Affibody generated an intermediate state attributed to partially unfolded PD-L1, while pulling from Affibody's N-terminus generated a force-activated catch bond. Pulling from residue #22 or #47 on Affibody generated high rupture forces, with the complex breaking at up to ∼190 pN under loading rates of ∼104-105 pN/s, representing a ∼4-fold increase as compared with low-force N-terminal pulling. SMD simulations showed relative tendencies in rupture forces that were consistent with experiments and, through visualization of force propagation networks, provided mechanistic insights. These results demonstrate how the mechanical properties of protein-protein interfaces can be controlled by informed choice of site-specific bioconjugation points within molecules, with implications for optimal bioconjugation strategies in drug delivery vehicles.

Improvement of discharge properties for aqueous magnesium-air batteries via a multi-functional and universal electrolyte additive

Aqueous magnesium-air batteries exhibit significant promise for energy storage because of their superior discharge efficiency, safety features, and affordability. Nonetheless, severe anode self-corrosion and discharge product accumulation can cause the actual discharge performance to be lower than the theoretical value. In this study, glucuronolactone (GLD), an additive with the ability to complex Mg, is utilized to construct a new solvated sheath and to reconfigure the hydrogen-bonding network to reduce the activity of free water. Additionally, GLD can regulate the interfacial behavior of water molecules through the formation of a water-deficient electric double layer (EDL) and solid-electrolyte interphase (SEI), thereby inhibiting self-corrosion. As expected, the AZ31 anode in 0.3 M GLD electrolyte reached an extraordinary specific energy of 1952.70 Wh·kg-1 at 5 mA·cm-2 and the cycle life was extended to 7 times that of blank electrolyte at 10 mA·cm-2. Furthermore, the addition of GLD also significantly improved the performance of batteries utilizing LA103Z, VW83, and VW103 anodes, highlighting its universal applicability. This research offers both theoretical and practical insights for the creation of universal and multifunctional electrolyte additives, and it paves the way for further exploration into aqueous magnesium-air batteries.

How Salt and Temperature Drive Reentrant Condensation of Aβ40

Within the framework of liquid-liquid phase separation (LLPS), biomolecular condensation orchestrates vital cellular processes, and its dysregulation is implicated in severe pathological conditions. Recent studies highlight the role of intrinsically disordered proteins (IDPs) in LLPS, yet the influence of microenvironmental factors has remained a puzzling factor. Here, via computational simulation of the impact of solution conditions on LLPS behavior of neurologically pathogenic IDP Aβ40, we chanced upon a salt-driven reentrant condensation phenomenon, wherein Aβ40 aggregation increases with low salt concentrations (25-50 mM), followed by a decline with further salt increments. An exploration of the thermodynamic and kinetic signatures of reentrant condensation unveils a nuanced interplay between protein electrostatics and ionic strength as potential drivers. Notably, the charged residues of the N-terminus exhibit a nonmonotonic response to salt screening, intricately linked to the recurrence of reentrant behavior in hydrophobic core-induced condensation. Intriguingly, our findings also unveil the reappearance of similar reentrant condensation phenomena under varying temperature conditions. Collectively, our study illuminates the profoundly context-dependent nature of Aβ40s liquid-liquid phase separation behavior, extending beyond its intrinsic molecular framework, where microenvironmental cues wield significant influence over its aberrant functionality.

High Yield of L-Sorbose via D-Glucose Isomerization in Ethanol over a Bifunctional Titanium-Boron-Beta Zeolite

D-Glucose-to-L-sorbose isomerization on Lewis acidic zeolite is a highly attractive avenue for sorbose production. But the L-sorbose yield is limited by the competing D-glucose-to-D-fructose isomerization and reaction equilibrium. In this work, it is suggested that ethanol directs the glucose conformation for selective D-glucose-to-L-sorbose isomerization. It also reacts with the produced L-sorbose to form ethyl-sorboside, which allows the D-glucose-to-L-sorbose isomerization to proceed beyond the thermodynamic equilibrium limit. It is shown that a bifunctional zeolite Beta containing framework titanium (Ti) and boron (B) is a selective catalyst for this tandem reaction: Lewis acidic framework Ti catalyzes the D-glucose-to-L-sorbose isomerization via an intramolecular 5,1-hydride shift process as confirmed by isotopic tracing experiments followed by 13C-NMR, while weak Brønsted acid framework B selectively promotes the sorbose ketalization with ethanol. A remarkably high yield of L-sorbose with a high fraction of sugar (>95 %: 27 % unreacted glucose, 11.4 % fructose, 57 % sorbose) was obtained after the mixture produced in ethanol was hydrolyzed.

Structural basis of respiratory complex adaptation to cold temperatures

In response to cold, mammals activate brown fat for respiratory-dependent thermogenesis reliant on the electron transport chain. Yet, the structural basis of respiratory complex adaptation upon cold exposure remains elusive. Herein, we combined thermoregulatory physiology and cryoelectron microscopy (cryo-EM) to study endogenous respiratory supercomplexes from mice exposed to different temperatures. A cold-induced conformation of CI:III2 (termed type 2) supercomplex was identified with a ∼25° rotation of CIII2 around its inter-dimer axis, shortening inter-complex Q exchange space, and exhibiting catalytic states that favor electron transfer. Large-scale supercomplex simulations in mitochondrial membranes reveal how lipid-protein arrangements stabilize type 2 complexes to enhance catalytic activity. Together, our cryo-EM studies, multiscale simulations, and biochemical analyses unveil the thermoregulatory mechanisms and dynamics of increased respiratory capacity in brown fat at the structural and energetic level.

Molecular Modeling of Glycosylated Catalytic Domain of Human Carbonic Anhydrase IX

Glycans exhibit significant structural diversity due to the flexibility of glycosidic bonds linking their constituent monosaccharides and the formation of numerous hydrogen bonds. The present work searches a simulated ensemble of glycan chain conformations attached to the catalytic domain of N-glycosylated human carbonic anhydrase IX (HCA IX-c) to identify conformations pointed away or back-folded toward the protein surface guided by different amino acid residues. A series of classical molecular dynamics (MD) simulation studies for a total of 30 μs followed by accelerated MD simulations for a total of 2 μs have been performed using two different force fields to capture varying degrees of fluctuations of both glycan chain and HCA IX. From the underlying free energy profile and kinetics derived using hidden Markov state model, several stable glycan orientations are identified that extend away from the protein surface and convert among each other with rate constants of the order 107-1010 S-1. Most importantly, we have identified a rare glycan conformation which reaches close to a catalytically important amino acid residue, Glu-106. We further enlist the protein residues that couple such less frequent event of the glycan chain back-folding toward the surface of the protein.

Probing Electrostatic Interactions in DNA-Bound CRISPR/Cas9 Complexes by Molecular Dynamics Simulations

Engineered protein mutations may be exploited to tune molecular interactions in the cellular environment. Here, we have explored the structural consequences of different Cas9 mutations in genome-editing CRISPR/Cas9 systems by means of Molecular Dynamics simulations. We have characterized mutation-induced structural changes and their implications for changes in protein-DNA, DNA-RNA, and DNA-DNA interactions. We present the analysis of multiple trajectories over the cumulative time scale of 7.7 μs, focusing on triple mutations that have been associated with enhancement of genome editing specificity, as well as control mutations. We find that the structural changes induced by the protein mutations are consistent with decreasing the strength of the interaction between Cas9 and the nontarget DNA strand. We discuss the implications of this finding for genome editing specificity.

Unraveling the mechanism of carbonic anhydrase IX inhibition by alkaloids from Ruta chalepensis: A synergistic analysis of in vitro and in silico data

Due to the pivotal role of carbonic anhydrase IX (CA IX) in pathological conditions, there's a pressing need for novel inhibitors to improve patient outcomes and clinical management. Herein, we investigated the inhibitory efficacy of six alkaloids from Ruta chalepensis against CA IX through in vitro inhibition assay and computational modeling. Skimmianine and maculosidine displayed significant inhibitory activity in vitro, with low IC50 values of 105.2 ± 3.2 and 295.7 ± 14.1 nM, respectively. Enzyme kinetics analyses revealed that skimmianine exhibited a mixed inhibition mode, contrasting with the noncompetitive inhibition mechanism observed for the reference drug (acetazolamide), as indicated by intersecting lines in the Lineweaver-Burk plots. The findings of docking calculations revealed that skimmianine and maculosidine exhibited extensive polar interactions with the enzyme. These alkaloids demonstrate substantial binding interactions and occupy identical binding site as acetazolamide, thereby enhancing their efficacy as inhibitors of CA IX. Utilizing a 100 ns molecular dynamics (MD) simulation, the dynamic interactions between isolated alkaloids and CA IX were intensively assessed. Analysis of diverse MD parameters revealed that skimmianine and maculosidine displayed consistent trajectories and notable energy stabilization during their interaction with CA IX. The findings of MM/PBSA analysis depicted the minimum binding free energy for skimmianine and maculosidine. In addition, the Potential Energy Landscape (PEL) analysis revealed distinct and stable conformational states for the CA IX-ligand complexes, with Skimmianine showing the most stable and lowest energy configuration. These computational findings align with experimental results, emphasizing the potential efficacy of skimmianine and maculosidine as inhibitors of CA IX.

Assessing the Partial Hessian Approximation in QM/MM-Based Vibrational Analysis

The partial Hessian approximation is often used in vibrational analysis of quantum mechanics/molecular mechanics (QM/MM) systems because calculating the full Hessian matrix is computationally impractical. This approach aligns with the core concept of QM/MM, which focuses on the QM subsystem. Thus, using the partial Hessian approximation implies that the main interest is in the local vibrational modes of the QM subsystem. Here, we investigate the accuracy and applicability of the partial Hessian vibrational analysis (PHVA) approach as it is typically used within QM/MM, i.e., only the Hessian belonging to the QM subsystem is computed. We focus on solute-solvent systems with small, rigid solutes. To separate two of the major sources of errors, we perform two separate analyses. First, we study the effects of the partial Hessian approximation on local normal modes, harmonic frequencies, and harmonic IR and Raman intensities by comparing them to those obtained using full Hessians, where both partial and full Hessians are calculated at the QM level. Then, we quantify the errors introduced by QM/MM used with the PHVA by comparing normal modes, frequencies, and intensities obtained using partial Hessians calculated using a QM/MM-type embedding approach to those obtained using partial Hessians calculated at the QM level. Another aspect of the PHVA is the appearance of normal modes resembling the translation and rotation of the QM subsystem. These pseudotranslational and pseudorotational modes should be removed as they are collective vibrations of the atoms in the QM subsystem relative to a frozen MM subsystem and, thus, not well-described. We show that projecting out translation and rotation, usually done for systems in isolation, can adversely affect other normal modes. Instead, the pseudotranslational and pseudorotational modes can be identified and removed.

Exploiting Materials Binding Peptides for the Organization of Resilient Biomolecular Constructs

Elastomers based on the resilin protein confer exceptional mechanical resilience in nature, but it remains elusive to recover the remarkable properties of these materials when they are made in the laboratory. This is possibly due to preorganized conformations of resilin in its natural setting, facilitating Tyr-based cross-linking. Here, resilin-like peptides (RLPs) are conjugated with a graphene-binding peptide, P1, to produce P1/RLP conjugates, in which the P1 domain may provide favorable preorganization on a graphene surface. Experiments using quartz crystal microbalance analysis and atomic force microscopy identified that the parent RLPs demonstrate negligible graphene binding; however, integration of the P1 with the RLPs resulted in the formation of dense, patterned bioligand overlayers on graphene. To complement this, molecular simulations revealed a notable difference in binding mode of the conjugates compared with typical materials binding peptides. Specifically, the adsorption of the P1/RLP conjugates did not focus on a few strongly bound "anchor" residues, but instead supported a more diffuse mode of binding, with many more participating residues featuring moderate contact. Analysis of the number of available Tyr residues (i.e., those not adsorbed at the surface) indicate that the RLP2-based conjugates will provide greater opportunity for cross-linking when adsorbed on graphene, providing a framework to generate patterned elastomeric materials.

Progress in Multiscale Modeling of Silk Materials

As a result of their hierarchical structure and biological processing, silk fibers rank among nature's most remarkable materials. The biocompatibility of silk-based materials and the exceptional mechanical properties of certain fibers has inspired the use of silk in numerous technical and medical applications. In recent years, computational modeling has clarified the relationship between the molecular architecture and emergent properties of silk fibers and has demonstrated predictive power in studies on novel biomaterials. Here, we review advances in modeling the structure and properties of natural and synthetic silk-based materials, from early structural studies of silkworm cocoon fibers to cutting-edge atomistic simulations of spider silk nanofibrils and the recent use of machine learning models. We explore applications of modeling across length scales: from quantum mechanical studies on model peptides, to atomistic and coarse-grained molecular dynamics simulations of silk proteins, to finite element analysis of spider webs. As computational power and algorithmic efficiency continue to advance, we expect multiscale modeling to become an indispensable tool for understanding nature's most impressive fibers and developing bioinspired functional materials.

Key Amino Acid Residues of the Agt1 Transporter for Trehalose Transport by Saccharomyces cerevisiae

Trehalose is crucial for the stress resistance of Saccharomyces cerevisiae, primarily through its stabilization of proteins and membranes. The Agt1 transporter, a member of the Major Facilitator Superfamily, mediates trehalose uptake, a key process for maintaining cellular integrity under stress. Despite its importance, the molecular mechanisms of Agt1-mediated trehalose transport remain underexplored. In this study, we expressed and purified the trehalase enzyme TreA from E. coli to develop reliable trehalose assays. We screened 257 wild S. cerevisiae isolates, identifying strains with enhanced trehalose transport capacities. Comparative analyses, including structural modeling and molecular docking, revealed that specific Agt1 variants exhibited significantly higher transport efficiency, influenced by key residues in the transporter. Molecular dynamics simulations and steered molecular dynamics provided further insights, particularly into the role of the Agt1 channel head region in substrate recognition and binding. Site-directed mutagenesis validated these findings, showing that mutations at critical residues, such as 156Q, 164L, 256Q, 395E, 396R, and 507Y significantly reduced transport activity, while 137Q, 230T, and 514 N increased efficiency under certain conditions.

Modeling of host PDZ-dependent interactions with SARS-CoV-2 envelope protein and changes in PDZ protein expression in macrophages and dendritic cells

PDZ (PSD-95 [postsynaptic density protein 95]/Dlg [Discs large]/ZO-1 [zonula occludens-1]) domain-containing proteins constitute a large family of scaffolds involved in a wide range of cellular tasks and are mainly studied in polarity functions. Diverse host PDZ proteins can be targeted by viral pathogens that express proteins containing PDZ-binding motifs (PDZbms). Previously, we have identified host PDZ-based interactions with the SARS-CoV-2 E protein (2E) in human monocytes. Here, we deepen the study of these interactions by docking and molecular dynamics analyses to identify the most favorable PDZ-PDZbm interaction of 7 host PDZ proteins with the PDZbm of 2E. In addition, we analyzed changes in the expression of 3 of the PDZ proteins identified as 2E interactors in monocytes (syntenin, ZO-2, and interleukin-16), in human monocyte-derived macrophages and in dendritic cells upon stimulation. Our results suggest that these PDZ proteins may have important functions in professional antigen-presenting cells, and their targeting by the PDZbm of 2E, a central virulence determinant of SARS-CoV-2, supports the hypothesis that such PDZ-dependent interaction in immune cells may constitute a viral evasion mechanism. An inhibitor design based on the PDZbm of 2E in the development of drugs against a variety of diseases is discussed.

Molecular dynamics simulations reveal concentration-dependent blockage of graphene quantum dots to water channel protein openings

Graphene quantum dots (GQDs) have attracted significant attention across various scientific research areas due to their exceptional properties. However, studies on the potential toxicity of GQDs have yielded conflicting results. Therefore, a comprehensive evaluation of the toxicity profile of GQDs is essential for a thorough understanding of their biosafety. In this work, employing a molecular dynamics (MD) simulation approach, we investigate the interactions between GQDs and graphene oxide quantum dots (GOQDs) with the AQP1 water channel protein, aiming to explore the potential biological influence of GQDs/GOQDs. Our MD simulation results reveal that GQDs can adsorb to the loop region around the openings of AQP1 water channels, resulting in the blockage of these channels and potential toxicity. Interestingly, this blockage is concentration-dependent, with higher GQD concentrations leading to a greater likelihood of blockage. Additionally, GOQDs show a lower probability of blocking the openings of AQP1 water channels compared to GQDs, due to the hydrophobicity of the loop regions around the openings, which ultimately leads to lower interaction energy. Therefore, these findings provide new insights into the potential adverse impact of GQDs on AQP1 water channels through the blockage of their openings, offering valuable molecular insights into the toxicity profile of GQD nanomaterials.

Examining prestructured β-actin peptides as substrates of histidine methyltransferase SETD3

The Nτ-His73 methylation of β-actin by histidine methyltransferase SETD3 is required for the integrity of the cellular cytoskeleton. Modulation of SETD3 activity in human cells facilitates cancer-like changes to the cell phenotype. SETD3 binds β-actin in an extended conformation, with a conserved bend-like motif surrounding His73. Here, we report on the catalytic specificity of SETD3 towards i, i + 3 stapled β-actin peptides possessing a limited conformational freedom surrounding the His73 substrate residue via positions Glu72 and Ile75. Stapled β-actin peptides were observed to be methylated less efficiently than the linear β-actin peptide. None of the stapled β-actin peptides efficiently inhibited the SETD3-catalyzed Nτ-His73 methylation reaction. Molecular dynamics simulations demonstrated that the unbound and SETD3-bound β-actin peptides display different backbone flexibility and bend-like conformations, highlighting their important role in substrate binding and catalysis. Overall, these findings suggest that reduced backbone flexibility of β-actin prevents the formation of optimal protein-peptide interactions between the enzyme and substrate, highlighting that the backbone flexibility needs to be considered when designing β-actin-based probes and inhibitors of biomedically important SETD3.

Structure based computational RNA design towards MafA transcriptional repressor implicated in multiple myeloma

Multiple myeloma is recognized as the second most common hematological cancer. MafA transcriptional repressor is an established mediator of myelomagenesis. While there are multitude of drugs available for targeting various effectors in multiple myeloma, current literature lacks a candidate RNA based MafA modulator. Thus, using the structure of MafA homodimer-consensus target DNA, a computational effort was implemented to design a novel RNA based chemical modulator against MafA. First, available MafA-consensus DNA structure was employed to generate an RNA library. This library was further subjected to global docking to select the most plausible RNA candidates, preferring to bind DNA binding region of MafA. Following global docking, MD-ready complexes that were prepared via local docking program, were subjected to 500 ns of MD simulations. First, each of these MD simulations were analyzed for relative binding free energy through MM-PBSA method, which pointed towards a strong RNA based MafA binder, RNA1. Second, through a detailed MD analysis, RNA1 was shown to prefer binding to a single monomer of the dimeric DNA binding domain of MafA using higher number of hydrophobic interactions compared with positive control MafA-DNA complex. At the final phase, a principal component analyses was conducted, which led us to identify the actual interaction region of RNA1 and MafA monomer. Overall, to our knowledge, this is the first computational study that presents an RNA molecule capable of potentially targeting MafA protein. Furthermore, limitations of our study together with possible future implications of RNA1 in multiple myeloma were also discussed.

Patterned graphene: An effective platform for adsorption, immobilization, and destruction of SARS-CoV-2 Mpro

To address the ongoing challenges posed by the SARS-CoV-2 and potentially stronger viruses in the future, the development of effective methods to fabricate patterned graphene (PG) and other precisely functional products has become a new research frontier. Herein, we modeled the "checkerboard" graphene (CG) and stripped graphene (SG) as representatives of PG, and studied their interaction mechanism with the target protein (Mpro) by molecular dynamics simulation. The calculation results on the binding strength and the root mean square deviation values of the active pocket revealed that PG is an effective platform for adsorption, immobilization, and destruction of Mpro. Specifically, CG is found to promote disruption of the active pocket for Mpro, but the presence of "checkerboard" oxidized regions inhibits the adsorption of Mpro. Meanwhile, the SG can effectively confine Mpro within the non-oxidized strips and enhances their binding strength, but doesn't play well on disrupting the active pocket. Our work not only elucidates the biological effects of PGs, but also provides guidance for their targeted and precise utilization in combating the SARS-CoV-2.

A vitamin D-based strategy overcomes chemoresistance in prostate cancer

BACKGROUND AND PURPOSE

Castration-resistant prostate cancer (CRPC) is a common male malignancy that requires new therapeutic strategies due to acquired resistance to its first-line treatment, docetaxel. The benefits of vitamin D on prostate cancer (PCa) progression have been previously reported. This study aimed to investigate the effects of vitamin D on chemoresistance in CRPC.

EXPERIMENTAL APPROACH

Structure function relationships of potent vitamin D analogues were determined. The combination of the most potent analogue and docetaxel was explored in chemoresistant primary PCa spheroids and in a xenograft mouse model derived from a patient with a chemoresistant CRPC.

KEY RESULTS

Here, we show that Xe4MeCF3 is more potent than the natural ligand to induce vitamin D receptor (VDR) transcriptional activities and that it has a larger therapeutic window. Moreover, we demonstrate that VDR agonists restore docetaxel sensitivity in PCa spheroids. Importantly, Xe4MeCF3 reduces tumour growth in a chemoresistant CRPC patient-derived xenograft. In addition, this treatment targets signalling pathways associated with cancer progression in the remaining cells.

CONCLUSION AND IMPLICATIONS

Taken together, these results unravel the potency of VDR agonists to overcome chemoresistance in CRPC and open new avenues for the clinical management of PCa.

Allosteric competition and inhibition in AMPA receptors

Excitatory neurotransmission is principally mediated by α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-subtype ionotropic glutamate receptors (AMPARs). Negative allosteric modulators are therapeutic candidates that inhibit AMPAR activation and can compete with positive modulators to control AMPAR function through unresolved mechanisms. Here we show that allosteric inhibition pushes AMPARs into a distinct state that prevents both activation and positive allosteric modulation. We used cryo-electron microscopy to capture AMPARs bound to glutamate, while a negative allosteric modulator, GYKI-52466, and positive allosteric modulator, cyclothiazide, compete for control of the AMPARs. GYKI-52466 binds in the ion channel collar and inhibits AMPARs by decoupling the ligand-binding domains from the ion channel. The rearrangement of the ligand-binding domains ruptures the cyclothiazide site, preventing positive modulation. Our data provide a framework for understanding allostery of AMPARs and for rational design of therapeutics targeting AMPARs in neurological diseases.

Saloplastics based on protein-peptides complexes immobilizing organic molecules in gastrointestinal drug delivery for ulcerative colitis treatment

Ulcerative colitis (UC) stands as a chronic inflammatory intestinal disease. This study aimed to explore a sustained-release strategy to alleviate DSS-induced colitis in mice using polyelectrolyte complexes (PECs) encapsulating an alkaloid, isoliensinine (ISO). The drug delivery platform, termed "Saloplastics (SAL)", was prepared by fabrication of PECs through the solid-liquid phase separation of sodium caseinate (SC) and ε-polylysine (EPL), followed by centrifugation to yield compact structures. Coarse-grained molecular dynamics simulations demonstrated that SAL had a nanorod-like structure formation between EPL and SC, which implied that the self-assembly of SAL is driven by hydrophobic aggregation and strong electrostatic attractions. A comprehensive evaluation of SAL was conducted, including characterizations of its physicochemical and biological properties. The results showed SAL had thermal plasticization properties and excellent swelling capacity as well as susceptibility to hydrolysis by pH and proteinase in simulated gastric fluid. Moreover, SAL displayed a porous morphology with high surface area for immobilizing organic molecules. ISO@SAL, formulated by ISO encapsulated in SAL, not only demonstrated high potency in alleviating DSS-induced colitis in mice, but also increased the dosing intervals from one day to three days. In conclusion, SAL is a biocompatible sustained-release oral drug delivery platform for gastrointestinal applications.

Atomistic understanding on desalination performance of pristine graphenylene nanosheet membrane at high applied pressures

Molecular dynamics (MD) simulations are conducted to assess pristine graphenylene membranes' effectiveness in seawater desalination, explicitly focusing on their salt rejection and water permeability capabilities. This study investigates the potential of the graphenylene for separation of the Na+ as monovalent cation, in order to evaluate its further application for separation of the other type of contaminants. To this end, the pristine graphenylene nanosheet is introduced into the simulation box which included the water molecules, sodium and chlorine ions. Subsequently, MD simulations were conducted by applying different amounts of external pressures in which the temperature changes are investigated as another effective parameter in water permeability and salt rejection properties. Furthermore, the water density map, radial distribution functions, and water density elucidate the performance of the considered membrane in the presence of water molecules, Na+ ions, and Cl- ions. The optimum performance of the pristine graphenylene for seawater desalination is achieved at P = 400 MPa and T = 298 K that results in the water flux of 2920 L/m2 h bar and 98.8 % salt rejection. The pristine graphenylene nanosheet shows significant potential in effectively separating salt ions, which has elucidated its importance and subsequently, the functionalized membrane for this application.

Multi-method analysis revealed the mechanism of substrate selectivity in NHase: A gatekeeper residue at the activity center

Recognizing the critical need to elucidate the molecular determinants of this selectivity offers a pathway to engineer enzymes with broader and more versatile catalytic capabilities. Through integrated methods including phylogenetic analysis, molecular docking, and structural analysis, we identified a pivotal amino acid residue, αTrp116, linking the substrate binding pocket and the active site of a NHase from Pseudonocardia thermophila JCM 3095 (PtNHase). This residue acts as a crucial determinant of substrate specificity within the NHase enzyme. The mutant αW116R modified the substrate specificity of PtNHase, significantly enhancing its catalytic efficiency towards aromatic substrates. The catalytic activity for aromatic compounds such as 3-Cyanopyridine was 14-fold that of the wild-type, whereas its activity for aliphatic substrates diminished to one-sixth. MD simulations revealed that replacing αTrp116 with Arg allowed aromatic nitrile substrates to achieve more favorable conformations within the active site. Based on the mutant αW116R, we further constructed a combinatorial variant Pt-4, tailored for aromatic substrates, which exhibited an enzyme activity 50 times that of the wild-type. These results highlight the critical influence of amino acid residues in the enzyme's active site on substrate specificity and offer fresh perspectives and approaches for the evolution of enzymes.

Advancing plant cell wall modelling: Atomistic insights into cellulose, disordered cellulose, and hemicelluloses - A review

The complexity of plant cell walls on different hierarchical levels still impedes the detailed understanding of biosynthetic pathways, interferes with processing in industry and finally limits applicability of cellulose materials. While there exist many challenges to readily accessing these hierarchies at (sub-) angström resolution, the development of advanced computational methods has the potential to unravel important questions in this field. Here, we summarize the contributions of molecular dynamics simulations in advancing the understanding of the physico-chemical properties of natural fibres. We aim to present a comprehensive view of the advancements and insights gained from molecular dynamics simulations in the field of carbohydrate polymers research. The review holds immense value as a vital reference for researchers seeking to undertake atomistic simulations of plant cell wall constituents. Its significance extends beyond the realm of molecular modeling and chemistry, as it offers a pathway to develop a more profound comprehension of plant cell wall chemistry, interactions, and behavior. By delving into these fundamental aspects, the review provides invaluable insights into future perspectives for exploration. Researchers within the molecular modeling and carbohydrates community can greatly benefit from this resource, enabling them to make significant strides in unraveling the intricacies of plant cell wall dynamics.

Role of Tyrosine Phosphorylation in PTP-PEST

We study the influence of tyrosine phosphorylation on PTP-PEST, a cytosolic protein tyrosine phosphatase. Utilizing a combination of experimental data and computational modeling, specific tyrosine sites, notably, Y64 and Y88, are identified for potential phosphorylation. Phosphorylation at these sites affects loop dynamics near the catalytic site, altering interactions among key residues and modifying the size of the binding pocket. This, in turn, impacts substrate binding, as indicated by changes in the binding energy. Our findings provide insights into the structural and functional consequences of tyrosine phosphorylation on PTP-PEST, enhancing our understanding of its effects on substrate binding and catalytic conformation.

Elucidating ATP's role as solubilizer of biomolecular aggregate

Proteins occurring in significantly high concentrations in cellular environments (over 100 mg/ml) and functioning in crowded cytoplasm, often face the prodigious challenges of aggregation which are the pathological hallmark of aging and are critically responsible for a wide spectrum of rising human diseases. Here, we combine a joint-venture of complementary wet-lab experiment and molecular simulation to discern the potential ability of adenosine triphosphate (ATP) as solubilizer of protein aggregates. We show that ATP prevents both condensation of aggregation-prone intrinsically disordered protein Aβ40 and promotes dissolution of preformed aggregates. Computer simulation links ATP's solubilizing role to its ability to modulate protein's structural plasticity by unwinding protein conformation. We show that ATP is positioned as a superior biological solubilizer of protein aggregates over traditional chemical hydrotropes, potentially holding promises in therapeutic interventions in protein-aggregation-related diseases. Going beyond its conventional activity as energy currency, the amphiphilic nature of ATP enables its protein-specific interaction that would enhance ATP's efficiency in cellular processes.

Interactions of the Biphenylene Network with α-Helical and β-Sheet Proteins: Molecular Dynamics Simulations

In recent years, nanomaterials have been widely used in the biomedical field. The biphenylene network is a highly promising planar carbon nanomaterial. To better explore its biomedical applications, we need to understand the biological effects of the biphenylene network. To investigate the biological effects of the novel nanomaterial biphenylene network, we used molecular dynamics simulations to study the interactions of the novel planar carbon nanomaterial biphenylene network with α-helical and β-sheet proteins. We found that both types of proteins adsorb flatly on the surface of the biphenylene network; the strong van der Waals interaction is the main adsorption force, while π-π stacking also provides an auxiliary force for the adsorption. When the HP35 protein whose secondary structure is an α-helix was adsorbed on biphenylene network, the entire structure of α-helix 2 was disrupted and α-helix 3 partly recovered its helical structure after being disrupted. In contrast to the β-sheet YAP65 protein, only part of the structure of β-sheet 1 was disrupted. Therefore, the biocompatibility of the biphenylene network with the β-sheet YAP65 protein is better than that of the α-helical HP35 protein, which may be due to the different surface curvature of the protein's secondary structure. Our research promotes the application of the biphenylene network in biomedicine and provides a theoretical basis and experimental direction for practical experiments.

Design, synthesis, molecular dynamics and gene silencing studies of novel therapeutic HIF-1α siRNAs in hypoxic cancer cells

Hypoxia inducible factors (HIFs) are heterodimeric proteins that belong to a small group of transcription factors, which mainly regulates transcription of genes under hypoxic conditions. Particularly, oxygen sensing subunit of HIF-1α is a predominant subtype that heterodimerizes with oxygen-independent HIF-1β subunit, to trigger the transcription of hypoxia responsive genes. Due to poor supply of blood and rapid division of cancerous cells, tumor microenvironment exhibits low oxygen condition and therefore increased levels of HIF-1α. One of the promising therapeutic strategies to cancer is modulation of HIF-1α signaling pathway. Small interfering RNA (siRNA) mediated downregulation of HIF-1α has been reported to prevent growth and progression of various types of cancer and holds great promise in the cancer treatment. In this study, computational approaches were used to design potential siRNAs targeting HIF-1α and investigate their interaction with the human argonaute-2 (hAgo2). Molecular dynamic simulation of HIF-1α siRNAs-hAgo2 complexes revealed key interactions required for the efficient binding of guide strand to hAgo2 protein. Two siRNAs (S2 and S5) exhibiting strong binding with hAgo2 were further considered. Subsequently, we transfected the MCF-7 cell line with both standard HIF-1α and our designed siRNAs (S2 and S5). Following transfection, translation changes in the MCF-7 cells were assessed through western blotting. S2 and S5 efficiently reduced the expression of HIF-1α in hypoxic conditions. The aim of the present study is to understand the siRNA-hAgo2 interaction. It is also focused on the desiging of effective siRNA against HIF-1α.

Dynamic interactions and inhibitory mechanisms of Artemisia annua terpenoids with carbonic anhydrase IX

This study evaluates the inhibitory potential of terpenoids isolated from Artemisia annua against carbonic anhydrase IX (CAIX), a crucial enzyme overexpressed in hypoxic tumor environments. Employing a multidisciplinary approach, we utilized in vitro assays, enzyme kinetics, molecular docking, and molecular dynamics (MD) simulations to comprehensively assess the efficacy of these compounds. Among the terpenoids tested, manool emerged as the most potent inhibitor, exhibiting the lowest IC50 value of 160.2 ± 15.2 nM. This was followed by labda-8(17),12-diene-15,16-dial with an IC50 of 297.9 ± 8.84 nM. Enzyme kinetics revealed a mixed inhibition mode for both compounds. Molecular docking aligned well with in vitro data, showing extensive polar and hydrophobic interactions within the CAIX binding site. Further insights were gained through 300 ns MD simulations, which highlighted the dynamic interactions and stability of these complexes. Manool demonstrated the most significant stabilization of CAIX, as evidenced by favorable RMSD, Rg, SASA profiles, and the strongest hydrogen bonding interactions. Additionally, MM/PBSA calculations confirmed manool's superior binding affinity. These findings underscore the therapeutic potential of manool as a potent CAIX inhibitor, providing a foundation for the development of effective anticancer agents targeting hypoxic tumor environments. ADMET analysis revealed favorable pharmacokinetic profiles for the terpenoids, with manool demonstrating high lipophilicity and BBB permeability, though potential CYP-mediated interactions were noted.

In silico screening and identifying phytoconstituents of Withania somnifera as potent inhibitors of BRCA1 mutants: A therapeutic against breast cancer

Breast CAncer gene 1 (BRCA1) is an anti-oncogene that helps the cell repair damaged DNA and preserve genetic material. BRCA1 also acts as a cell growth suppressor and produces tumor suppressor gene (TSG) proteins, i.e., BRCA1 protein. Remarkably, BRCA1 mutations account for 90 % of hereditary breast cancer and a majority of hereditary ovarian cancer. Hence, we have considered three mutants of BRCA1 (R1699W, R1699Q, T1700A) in this study and adopted an in-silico approach to find the best possible phytochemical to inhibit these mutated proteins, enabling early breast cancer diagnosis. Perceiving the importance, many natural molecules from ancient medicinal plants are considered for molecular docking. Our findings suggest that though many molecules bind actively with the receptor's active site, the top three phytoconstituents (27-Deoxy-14-hydroxywithaferin A, Withacoagulin, Somniferanolide) of Withania somnifera, commonly known as Ashwagandha, have high binding affinities and suitable pharmacokinetic properties, making these natural compounds potential drug candidates. Further, molecular dynamics (MD) simulation and the binding free energy calculation show stability and thermodynamically favourable. We can, therefore, draw the conclusion that these lead compounds act as potential inhibitors against BRCA1. However, wet lab experiments and clinical trials are recommended to ascertain its efficacy, hence the development of novel BRCA1 inhibitors.

Engineered red Opto-mGluR6 Opsins, a red-shifted optogenetic excitation tool, an in vitro study

Degenerative eye diseases cause partial or complete blindness due to photoreceptor degeneration. Optogenetic gene therapy is a revolutionary technique combining genetics and optical methods to control the function of neurons. Due to the inherent risk of photochemical damage, the light intensity necessary to activate Opto-mGluR6 surpasses the safe threshold for retinal illumination. Conversely, red-shifted lights pose a significantly lower risk of inducing such damage compared to blue lights. We designed red-shifted Opto-mGluR6 photopigments with a wide, red-shifted working spectrum compared to Opto-mGluR6 and examined their excitation capability in vitro. ROM19, ROM18 and ROM17, red-shifted variants of Opto-mGluR6, were designed by careful bioinformatics/computational studies. The predicted molecules with the best scores were selected, synthesised and cloned into the pAAV-CMV-IRES-EGFP vector. Expression of constructs was confirmed by functional assessment in engineered HEK-GIRK cells. Spectrophotometry and patch clamp experiments demonstrated that the candidate molecules were sensitive to the desired wavelengths of the light and directly coupled light stimuli to G-protein signalling. Herein, we introduce ROM17, ROM18 and ROM19 as newly generated, red-shifted variants with maximum excitation red-shifted of ~ 40nm, 70 nm and 126 nm compared to Opto-mGluR6.

The critical role of aromatic residues in the binding of the SARS-CoV-2 fusion peptide to phospholipid bilayer membranes

Based on the SARS-CoV-2 fusion peptide (FP) structure determined from the NMR experiment, we created six FP models under different environmental conditions to explore the effects of salt and cholesterol on FP-membrane binding. The all-atom molecular dynamics (MD) simulation results indicated that ionic environments notably impact the FP structure as well as the stability of the helical elements within the peptide. Our findings highlighted the unpredictable influence of ions on the secondary structures and dynamics of the FP, emphasizing the complexity and sensitivity of the peptide's conformations to ionic conditions. When exploring the peptide's interaction with a cholesterol-free phospholipid bilayer membrane, we found that the helical elements of the FP remain stable irrespective of the salt type (Na+ or Ca2+). This result emphasizes the crucial role of phospholipid bilayer membranes in supporting the secondary structures of the FP. The MD simulation results showed that Ca2+ ions facilitated deeper membrane penetration than Na+ ions, highlighting the critical role of calcium ions in the FP-membrane binding. Our study indicates the essential role of the aromatic residues (such as Phe833 and Tyr837) in the FP-membrane binding process. Finally, we investigated the FP-membrane binding patterns in the presence of cholesterol. The MD simulation results demonstrated that the coupling of Ca2+ ions and cholesterol would also benefit the FP-membrane binding. Furthermore, our findings reveal that while the type of ion and cholesterol content exert varied and unpredictable influences on FP-membrane binding patterns, aromatic residues like tyrosine (Tyr) and phenylalanine (Phe) play an essential role in FP-membrane binding. In particular, deep mutational scanning (DMS) experiments have confirmed that mutating phenylalanine in the FP significantly decreases viral mutational fitness, emphasizing the pivotal role of phenylalanine residues in membrane fusion. This knowledge can aid in developing more effective therapeutic strategies targeting the viral fusion peptide and its key amino acids, ultimately contributing to developing treatments and vaccines against the virus.