Notes

Phylogenetic portrayal of Setaria equina and its association with some other filarids.
Rational design and fabrication of graphene nanoarchitectures with multifunctionality and multidimensionality remains quite a challenge. Here, we present a synthetic sequence, based on the combination of two advanced patterned-functionalization principles, namely, laser-writing and poly(methyl methacrylate) (PMMA)-assisted lithographic processes, leading to unprecedented covalently doped graphene superlattices. Spatially resolved supratopic- and Janus-binding were periodically weaved on the graphene sheet, leading to four different types of zones with distinct chemical doping and structural properties. Notably, this is also the first realization of patterned Janus graphene. The elaborate chemical doping with micrometer resolution is unequivocally evidenced by scanning Raman spectroscopy (SRS) and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). The design of the pattern as well as the degree of chemical doping on both opposite sides of graphene can be easily manipulated, rendering exciting potential for graphene nanosystems.Controlled radical polymerization of vinyl monomers with multivinyl cross-linkers leads to the synthesis of highly branched polymers with controlled spatial density of functional chain ends. The resulting polymers synthesized in this manner have large dispersities resulting from a mixture of unreacted primary chains, low molecular weight branched species, and high molecular weight highly branched species. Through the use of fractional precipitation, we present a synthetic route to high molecular weight highly branched polymers that are absent of low molecular weight species and that contain reactivity toward amines for controlled postpolymerization modification. The controlled spatial density of functional moieties on these high molecular weight macromolecular constructs enable new functional biomaterials with the potential for application in regenerative medicine, immunoengineering, imaging, and controlled drug delivery.A promising approach for the regeneration of tissues or organs with three-dimensional hierarchical structures is the preparation of scaffold-cell complexes that mimic these hierarchical structures. This requires an effective technique for immobilizing cell-specific ligands at arbitrarily chosen positions on matrices. Here, we report a versatile system for arranging cell-specific ligands onto desired compartments of biodegradable matrices for site-selective cell arrangement. We utilized the specific binding abilities of specific DNAs, immobilizing them as tags to arrange cell-recognition ligands at desired areas of the matrices by specific binding with cell-recognition ligand-DNA conjugates. We synthesized poly(l-lactide) (PLLA), a biodegradable polymer, with an oligo-DNA (trimer of deoxyguanosine dG3) attached via a poly(ethylene glycol) (PEG) spacer to generate dG3-PEG-b-PLLA. The peptides Arg-Gly-Asp-Ser (RGDS) and Arg-Glu-Asp-Val (REDV) were chosen as cell-recognition ligands and were attached to an adapter DNA (aDNA), which can specifically bind to the dG3 moiety through G-quadruplex formation. The obtained dG3-PEG-b-PLLA was deposited on a small spot of the PLLA film, and the aDNA-RGDS or aDNA-REDV conjugate was added on the film to immobilize these ligands at the spot. We confirmed the specific adhesion of L929 cells (a mouse fibroblast cell line) and human umbilical vein endothelial cells (HUVECs) on the small areas coated with dG3-PEG-b-PLLA in the presence of aDNA-RGDS and aDNA-REDV, respectively, even after applying shear stress by flowing medium across the spot. Cell-specific attachment of the target cells was effectively achieved in a spatially controlled manner. This technique has the potential for the construction of cell-scaffold complexes that mimic the hierarchical structures of natural organs and may represent a breakthrough in realizing regenerative medicine and tissue engineering of complex organs.The mechanical and morphological cues of fibrillar extracellular matrices (ECMs) play vital roles in controlling the cellular behaviors. Understanding and regulating the correlation of the mechanics with morphologies, at the micro-/nanoscale are of great relevance to guide the growth and differentiation of stem or progenitor cells into the desired tissues. However, the investigations directed toward acquiring such a kind of correlation are very limited and far from satisfactory. Here, rheological and nanoindentation tests were employed to appraise the mechanical behaviors of biomimetic ECMs assembled from type I collagen solutions containing the equivalent content of alginate but with different molecular weights (MWs). An alginate-molecular-weight-dependent trend was found in the fibrillogenesis process and the fibril aggregation of these collagen-alginate (CA) matrices. The present study revealed that the viscoelasticity and nonlinear elasticity of the CA matrices relied upon their specific fibrillar architectures in which a heterogeneous structure formed with varying alginate MW, including the coexistence of small fibrils and larger fibrillar bundles. The correlation of the mechanical behaviors with the inhomogeneity in the fibrillar structures was further discussed in combination with those of Ca2+ ionically cross-linked CA matrices. This study not only presented the delicate mechanics of fibrillar ECM analogues but also showed that the introduction of affiliative matters such as polysaccharides (alginate with different MWs) is a simple and convenient strategy to achieve biomimetic hydrogels with tunable viscoelastic properties.Tannic acid (TA) can form stable complexes with proteins, attracting significant attention as protein delivery systems. However, its systemic application has been limited due to nonspecific interaction. Here, we report a simple technique to prepare systemically applicable protein delivery systems using sequential self-assembly of a protein, TA, and phenylboronic acid-conjugated PEG-poly(amino acid) block copolymers in aqueous solution. Mixing the protein and TA in aqueous solution led to covering of the protein with TA, and subsequent addition of the copolymer resulted in the formation of boronate esters between TA and copolymers, constructing the core-shell-type ternary complex. The ternary complex covered with PEG exhibited a small hydrodynamic diameter of ∼10-20 nm and prevented an unfavorable interaction with serum components, thereby accomplishing significantly prolonged blood circulation and enhanced tumor accumulation in a subcutaneous tumor model. The technique utilizing supramolecular self-assembly may serve as a novel approach for designing protein delivery systems.As a key mechanical signal of natural extracellular matrix (ECM), stress relaxation plays an essential role in cell fate decision. However, the biomimetic matrix with fast stress relaxation and its cellular response mechanism have received little attention. Meanwhile, the nanofibrillar architecture which is conductive to mechanical transduction has invariably been ignored in the previous viscoelastic matrix design. Herein, by introducing a dynamic covalent imine bond into a physically cross-linked collagen hydrogel, we prepared bionic fast-relaxing nanofibrillar hydrogels with relaxation time less than 10 s. Through a single control of imine bond content, we realized fine-tuning of the relaxation rate while maintaining a constant initial modulus and fiber density. Using MC3T3-E1 cells as a model, we then proved that the nanofibrillar matrix with fast relaxation mechanics can effectively promote cell spreading and differentiation. In particular, TRPV4 as a molecular sensor of matrix viscoelasticity was demonstrated to regulate cell fate on the nanofibrillar hydrogels by mediating calcium influx. It is expected that the material design principle combining both nanofibrillar structure and tunable fast-relaxation can provide a more broadly adaptable materials platform for simulating natural ECM mechanical cues, and the investigation of the TRPV4 ion channel mediated cellular response will facilitate discovery of more fundamental mechanisms in tissue growth and development.Molecular transport of biomolecules plays a pivotal role in the machinery of life. Yet, this role is poorly understood due the lack of quantitative information. Here, the role and properties of the C-terminal region of Escherichia coli Hfq is reported, involved in controlling the flow of a DNA solution. A combination of experimental methodologies has been used to probe the interaction of Hfq with DNA and to measure the rheological properties of the complex. A physical gel with a temperature reversible elasticity modulus is formed due to the formation of noncovalent cross-links. The mechanical response of the complexes shows that they are inhomogeneous soft solids. Our experiments indicate that the Hfq C-terminal region could contribute to the genome's mechanical response. The reported viscoelasticity of the DNA-protein complex might have implications for cellular processes involving molecular transport of DNA or segments thereof.Antimicrobial peptides (AMPs) have attracted great interest as they constitute one of the most promising alternatives against drug-resistant infections. Their amphipathic nature not only provides them antimicrobial and immunomodulatory properties but also the ability to self-assemble into supramolecular nanostructures. Here, we propose their use as self-assembling domains to drive hierarchical organization of intrinsically disordered protein polymers (IDPPs). Using a modular approach, hybrid protein-engineered polymers were recombinantly produced, thus combining designer AMPs and a thermoresponsive IDPP, an elastin-like recombinamer (ELR). We exploited the ability of these AMPs and ELRs to self-assemble to develop supramolecular nanomaterials by way of a dual-assembly process. First, the AMPs trigger the formation of nanofibers; then, the thermoresponsiveness of the ELRs enables assembly into fibrillar aggregates. The interplay between the assembly of AMPs and ELRs provides an innovative molecular tool in the development of self-assembling nanosystems with potential use for biotechnological and biomedical applications.A promising magnetic refrigerant, AlFe2B2, has been prepared for the first time by microwave (MW) melting of a mixture of constituent elements. For comparison, samples of AlFe2B2 have been also prepared by arc-melting, traditionally used for the synthesis of this material, and by induction (RF) melting, which was used in the very first report on the synthesis of AlFe2B2. Although an excess of Al has to be used to suppress the formation of ferromagnetic FeB, the other byproduct, Al13Fe4, is easily removed by acid treatment, affording phase-pure samples of AlFe2B2. Our analysis indicates that the equimolar Fe/B ratio typically used for the preparation of AlFe2B2 might not provide the best synthetic conditions, as it does not account for the full reaction stoichiometry. Furthermore, we find that the initial Al/Fe loading ratio strongly influences magnetic properties of the sample, as judged by the range of ferromagnetic ordering temperatures (TC = 280-293 K) observed in our experiments. The TC value increases with the decrease in the Al/Fe ratio, due to the change in the Al/Fe antisite disorder. The use of the same Al/Fe loading ratio in the arc-, RF-, and MW-melting experiments leads to samples with a more consistent TC of 286-287 K and similar values of the magnetocaloric effect.cis-2-Methyl-4-propyl-1,3-oxathiane (cis-2-MPO) was recently identified in wine and proposed to arise from the reaction of 3-sulfanylhexan-1-ol (3-SH) and acetaldehyde. However, the evolution profile of cis-2-MPO during alcoholic fermentation (AF) and storage and its relationship with varietal thiols and acetaldehyde production were unknown. These aspects were investigated by fermenting Sauvignon blanc juice with J7 and/or VIN13 yeast strains and assessing the stability of cis-2-MPO during wine storage. Moderate to strong Pearson correlations verified similar evolution trends between acetaldehyde, 3-sulfanylhexyl acetate, and cis-2-MPO, with initial increases and a peak during the early to middle stages of AF before consecutive decreases until the end. Contrarily, 3-SH correlated moderately only at the end of AF. A consistent decrease observed for cis-2-MPO when spiked into Sauvignon blanc wine and assessed during 1-year storage revealed its general instability, but acetaldehyde addition (100 mg/L), pH 3.0, and storage at 4 °C all appeared to retain cis-2-MPO. These results have implications for wine aroma and the potential for cis-2-MPO to act as a sink (or source) for 3-SH in wine over time.The fragrant bolete, Suillus punctipes (Peck) Singer, is an edible mushroom with a unique aroma reminiscent of mushroom and citrus peel with an undertone of apricot. Thirty-five odorants were identified using solvent-assisted flavor evaporation (SAFE) and aroma extract dilution analysis (AEDA). Fourteen odorants including those with flavor dilution (FD) factors ≥64 were quantitated using stable isotope dilution assays (SIDA). Some odorants with high OAVs included 1-octen-3-one (OAV 164368), 1-octen-3-ol (OAV 3421), linalool (OAV 812), and nonanal (OAV 487). An odor simulation model was prepared closely matching the aroma of the mushroom. Omissions experiments revealed that 1-octen-3-one, 1-octen-3-ol, (2E)-oct-2-enal, linalool, δ-dodecalactone, and a mixture of three aldehydes, octanal, nonanal, and decanal, were essential odorants for the aroma profile. Enantiomeric ratios were determined for several odorants employing chiral chromatography. The results from this study lay the groundwork for future studies in the aroma chemistry of S. punctipes and other mushrooms from the Suillus genus.The specificity of anionic phospholipids-calcium ion interaction and lipid demixing has been established as a key regulatory mechanism in several cellular signaling processes. The mechanism and implications of this calcium-assisted demixing have not been elucidated from a microscopic point of view. Here, we present an overview of atomic interactions between calcium and phospholipids that can drive nonideal mixing of lipid molecules in a model lipid bilayer composed of zwitterionic (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC)) and anionic (1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine (POPS)) lipids with computer simulations at multiple resolutions. Lipid nanodomain formation and growth were driven by calcium-enabled lipid bridging of the charged phosphatidylserine (PS) headgroups, which were favored against inter-POPS dipole interactions. Consistent with several experimental studies of calcium-associated membrane sculpting, our analyses also suggest modifications in local membrane curvature and cross-leaflet couplings as a response to such induced lateral heterogeneity. In addition, reverse mapping to a complementary atomistic description revealed structural insights in the presence of anionic nanodomains, at timescales not accessed by previous computational studies. This work bridges information across multiple scales to reveal a mechanistic picture of calcium ion's impact on membrane biophysics.Although both pressure and temperature are essential parameters governing thermodynamics, the effects of the pressure on solution-phase equilibria have not been well studied compared to those of temperature. Here, we demonstrate the interesting pressure-dependent behavior of tetraphenylethylene (TPE) derivatives in multiphase systems composed of an organic phase and an aqueous phase in the presence and absence of γ-cyclodextrin (γ-CD). In this system, tetraphenylethylene monocarboxylic acid (TPE1H) and its dicarboxylic acid (TPE2H2) are distributed in the aqueous phase and dissociated into the corresponding anions, that is, TPE1- and TPE22-, when the pH is sufficiently high. The distribution ratios of TPE1H/TPE1- and TPE2H/TPE22- show opposing pressure dependencies the distribution of the former in the organic phase increases with increasing pressure, whereas that of the latter decreases. The 11 complexation constants of TPE1- and TPE22- with γ-CD, which can be determined from the distribution ratios in the presence of γ-CD, also show opposing pressure dependencies the former shows a positive pressure dependence, but the latter exhibits a negative one. These pressure effects on the distribution and complexation of TPE derivatives can be interpreted based on the differences in the molecular polarity of these solutes. The water permittivity is enhanced at high pressure, thus stabilizing the more polar TPE22- in the aqueous phase to a larger extent than TPE1- and, as a result, reducing its distribution in the organic phase, as well as its complexation with γ-CD. Fluorescence spectra in the aqueous phase suggest that the TPE derivatives form aggregates with γ-CD molecules, as detected by the specific fluorescence. In addition, the fluorescence intensities of the γ-CD complexes are enhanced at high pressures because of the restricted rotation of the phenyl rings in the TPE molecules. This study provides new perspectives for multiphase partitioning and an attractive alternative to conventional extraction methods.Ionic liquid (IL) has been considered as a potential electrolyte for developing next-generation sodium-ion batteries. A highly concentrated ionic system such as IL is characterized by the significant influence of intramolecular polarization and intermolecular charge transfer that vary with the combination of cations and anions in the system. In this work, a self-consistent atomic charge determination using the combination of classical molecular dynamics (MD) simulation and density functional theory (DFT) calculation is employed to investigate the transport properties of three mixtures of ILs with sodium salt relevant to the electrolyte for a sodium-ion battery [1-ethyl-3-methylimidazolium, Na][bis(fluorosulfonyl)amide] ([C2C1im, Na][FSA]), [N-methyl-N-propylpyrrolidinium, Na][FSA] ([C3C1pyrr, Na][FSA]), and [K, Na][FSA]. The self-consistent method is versatile to address the intramolecular polarization and intermolecular charge transfer in response to the cation-anion combination, as well as the variation in their compositions. The structure and dynamic properties of IL mixtures obtained from the method are in line with those from the experimental works. The comparison to the Nernst-Einstein estimates shows that the electrical conductivity is reduced due to correlated motions among the ions, and the contribution to the conductivity from each ion species is not necessarily ranked in the same order as the diffusion coefficient. It is further seen that the increase of the sodium-ion composition reduces the fluidity of the system. The results highlight the potential of the method and the microscopic description that it can provide to assist the investigation toward a sensible design of IL mixtures as an electrolyte for a high-performance sodium-ion battery.It is well understood that tetrahydrofuran (THF) molecules are able to stabilize the large cages (51264) of structure II to form the THF hydrate with empty small cages even at atmospheric pressure. This leaves the small cages to store gas molecules at relatively lower pressures and higher temperatures. The dissociation enthalpy and temperature strongly depend on the size of gas molecules enclathrated in the small cages of structure II THF hydrate. A high-pressure microdifferential scanning calorimeter was applied to measure the dissociation enthalpies and temperatures of THF hydrates pressurized by helium and methane under a constant pressure ranging from 0.10 to 35.00 MPa and a wide THF concentration ranging from 0.25 to 8.11 mol %. The dissociation temperature of binary He + THF and methane + THF hydrates increases along with an increase in the THF concentration in the liquid phase at a fixed pressure (e.g., 30 MPa), reaching a maximum value of 280.8 and 312.8 K, respectively, at stoichiometric concentratioupancy of methane molecules in the small cages. These findings provide important information for the design of a potential medium of gas storage and transportation.This study deals with poly(butylene 2,5-furan-dicarboxylate), PBF, a renewable bio-based polyester expected to replace non-eco-friendly fossil-based homologues. PBF exhibits excellent gas barrier properties, which makes it promising for packaging applications; however, its rather low and slow crystallinity affects good mechanical performance. The crystallization of this relatively new polymer is enhanced here via reinforcement by introduction in situ of 1 wt % montmorillonite, MMT, nanoclays of three types (functionalizations). We study PBF and its nanocomposites (PNCs) also from the basic research point of view, molecular dynamics. For this work, we employ the widely used combination of techniques, differential scanning calorimetry (DSC) with broad-band dielectric relaxation spectroscopy (BDS), supplemented by polarized light microscopy (PLM) and thermogravimetric analysis (TGA). In the PNCs, the crystalline rate and fraction, CF, were found to be strongly enhanced as these fillers act as additional crystallproof for weak MMT-PBF interactions. Overall, our results, along with data from the literature, suggest that such furan-based polyesters reinforced with properly chosen nanofillers could potentially serve well as tailor-made PNCs for targeted applications.Flavoproteins are important blue light sensors in photobiology and play a key role in optogenetics. The characterization of their excited state structure and dynamics is thus an important objective. Here, we present a detailed study of excited state vibrational spectra of flavin mononucleotide (FMN), in solution and bound to the LOV-2 (Light-Oxygen-Voltage) domain of Avena sativa phototropin. Vibrational frequencies are determined for the optically excited singlet state and the reactive triplet state, through resonant ultrafast femtosecond stimulated Raman spectroscopy (FSRS). To assign the observed spectra, vibrational frequencies of the excited states are calculated using density functional theory, and both measurement and theory are applied to four different isotopologues of FMN. Excited state mode assignments are refined in both states, and their sensitivity to deuteration and protein environment are investigated. We show that resonant FSRS provides a useful tool for characterizing photoactive flavoproteins and is able to highlight chromophore localized modes and to record hydrogen/deuterium exchange.Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. This strategy keeps structural descriptors symmetry invariant so that the resulting tensorial NN models are as efficient as their scalar counterparts. We validate the performance and universality of this approach by learning response properties of water oligomers and liquid water and transition dipole moment of a model structural unit of proteins. Machine-learned tensorial models have enabled efficient simulations of vibrational spectra of liquid water and ultraviolet spectra of realistic proteins, promising feasible and accurate spectroscopic simulations for biomolecules and materials.Amorphous network materials are becoming increasingly important with applications, for example, as supercapacitors, battery anodes, and proton conduction membranes. The design of these materials is hampered by the amorphous nature of the structure and sensitivity to synthetic conditions. Here, we show that through artificial synthesis, fully mimicking the catalytic formation cycle, and full synthetic conditions, we can generate structural models that can fully describe the physical properties of these amorphous network materials. This opens up pathways for the rational design where complex structural influences, such as the solvent and catalyst choice, can be taken into account.Urea is an important chemical with many biological and industrial applications. In this work, we develop a first-principles polarizable force field for urea crystals and aqueous solutions within the symmetry-adapted perturbation theory (SAPT) protocol with the SWM4-NDP model for water. We make three adjustments to the SAPT force field protocol We augment the carbonyl oxygen atom of urea with additional interaction sites in order to address the "chelated" bent double hydrogen bonds in urea, we reduce the polarizability of urea by a factor of 0.70 to reproduce experimental in-crystal dipole moments, and we re-fit atomic pre-exponential parameters to correct the predicted liquid structure. We find that the resulting force field is in good agreement for the static and dynamic properties of aqueous urea solutions when compared to experiment or first-principles molecular dynamics simulations. The polarizable urea model accurately reproduces the crystal-solution phase diagram in the temperature range of 261 to 310 K; for which, it is superior to non-polarizable models. We expect that this force field will be useful in the modeling of complex biomolecular systems and enable studies of polarizability effects of solid-liquid phase behavior of complex fluids.Understanding ionic structure and electrostatic environments near a surface has both fundamental and practical value. In electrochemistry, especially when room temperature ionic liquids (ILs) are involved, the complex ionic structure near the interface is expected to crucially influence reactions. Here we report evidence that even in dilute aqueous solutions of several ILs, the ions aggregate near the surface in ways that are qualitatively different from simple electrolytes. We have used a vibrational probe molecule, 4-mercaptobenzonitrile (MBN), tethered to a metal surface to monitor the behavior of the ionic layers. The characteristic nitrile vibrational frequency of this molecule has distinct values in the presence of pure water (∼2232 cm-1) and pure IL (for example, ∼2226 cm-1 for ethylmethylimidazolium tetrafluoroborate, [EMIM][BF4]). This difference reflects the local electrostatic field and the hydrogen-bonding variations between these two limiting cases. We tracked this frequency shift as a function oggregate at the surface. Because ILs serve as electrolytes in a range of electrochemical reactions, including those requiring water, our results are likely useful for mechanistic understanding and tuning of such reactions.Drug-induced torsade de pointes (TdP) is a life-threatening ventricular arrhythmia responsible for the withdrawal of many drugs from the market. Although currently used TdP risk-assessment methods are effective, they are expensive and prone to produce false positives. In recent years, in silico cardiac simulations have proven to be a valuable tool for the prediction of drug effects. The objective of this work is to evaluate different biomarkers of drug-induced proarrhythmic risk and to develop an in silico risk classifier. Cellular simulations were performed using a modified version of the O'Hara et al. ventricular action potential model and existing pharmacological data (IC50 and effective free therapeutic plasma concentration, EFTPC) for 109 drugs of known torsadogenic risk (51 positive). For each compound, four biomarkers were tested Tx (drug concentration leading to a 10% prolongation of the action potential over the EFTPC), TqNet (net charge carried by ionic currents when exposed to 10 times the EFTPC wi, we built a ready-to-use tool (based on more than 450 000 simulations), which can be used to quickly assess the proarrhythmic risk of a compound. In conclusion, our in silico tool can be useful for the preclinical assessment of TdP-risk and to reduce costs related with new drug development. The TdP risk-assessment tool and the software used in this work are available at https//riunet.upv.es/handle/10251/136919.The ability of coronaviruses to infect humans is invariably associated with their binding strengths to human receptor proteins. Both SARS-CoV-2, initially named 2019-nCoV, and SARS-CoV were reported to utilize angiotensin-converting enzyme 2 (ACE2) as an entry receptor in human cells. To better understand the interplay between SARS-CoV-2 and ACE2, we performed computational alanine scanning mutagenesis on the "hotspot" residues at protein-protein interfaces using relative free energy calculations. Our data suggest that the mutations in SARS-CoV-2 lead to a greater binding affinity relative to SARS-CoV. In addition, our free energy calculations provide insight into the infectious ability of viruses on a physical basis and also provide useful information for the design of antiviral drugs.Human G protein-coupled receptors (hGPCRs) are the most frequent targets of Food and Drug Administration (FDA)-approved drugs. Structural bioinformatics, along with molecular simulation, can support structure-based drug design targeting hGPCRs. In this context, several years ago, we developed a hybrid molecular mechanics (MM)/coarse-grained (CG) approach to predict ligand poses in low-resolution hGPCR models. The approach was based on the GROMOS96 43A1 and PRODRG united-atom force fields for the MM part. Here, we present a new MM/CG implementation using, instead, the Amber 14SB and GAFF all-atom potentials for proteins and ligands, respectively. The new implementation outperforms the previous one, as shown by a variety of applications on models of hGPCR/ligand complexes at different resolutions, and it is also more user-friendly. Thus, it emerges as a useful tool to predict poses in low-resolution models and provides insights into ligand binding similarly to all-atom molecular dynamics, albeit at a lower computational cost.The accurate prediction of protein-ligand binding affinity is a central challenge in computational chemistry and in-silico drug discovery. The free energy perturbation (FEP) method based on molecular dynamics (MD) simulation provides reasonably accurate results only if a reliable structure is available via high-resolution X-ray crystallography. To overcome the limitation, we propose a sequential prediction protocol using generalized replica exchange with solute tempering (gREST) and FEP. At first, ligand binding poses are predicted using gREST, which weakens protein-ligand interactions at high temperatures to sample multiple binding poses. To avoid ligand dissociation at high temperatures, a flat-bottom restraint potential centered on the binding site is applied in the simulation. The binding affinity of the most reliable pose is then calculated using FEP. The protocol is applied to the bindings of ten ligands to FK506 binding proteins (FKBP), showing the excellent agreement between the calculated and experimental binding affinities. The present protocol, which is referred to as the gREST+FEP method, would help to predict the binding affinities without high-resolution structural information on the ligand-bound state.This Article describes a novel geometric methodology for analyzing free energy and kinetics of assembly driven by short-range pair-potentials in an implicit solvent and provides a proof-of-concept illustration of its unique capabilities. An atlas is a labeled partition of the assembly landscape into a roadmap of maximal, contiguous, nearly-equipotential-energy conformational regions or macrostates, together with their neighborhood relationships. The new methodology decouples the roadmap generation from sampling and produces (1) a queryable atlas of local potential energy minima, their basin structure, energy barriers, and neighboring basins; (2) paths between a specified pair of basins, each path being a sequence of conformational regions or macrostates below a desired energy threshold; and (3) approximations of relative path lengths, basin volumes (configurational entropy), and path probabilities. Results demonstrating the core algorithm's capabilities and high computational efficiency have been generated byso be used to complement the strengths of prevailing methodologies including Molecular Dynamics, Monte Carlo, and Fast Fourier Transform based methods.The extreme dynamic behavior of intrinsically disordered proteins hinders the development of drug-like compounds capable of modulating them. There are several examples of small molecules that specifically interact with disordered peptides. However, their mechanisms of action are still not well understood. Here, we use extensive molecular dynamics simulations combined with adaptive sampling algorithms to perform free ligand binding studies in the context of intrinsically disordered proteins. We tested this approach in the system composed by the D2 sub-domain of the disordered protein p27 and the small molecule SJ403. The results show several protein-ligand bound states characterized by the establishment of a loosely oriented interaction mediated by a limited number of contacts between the ligand and critical residues of p27. Finally, protein conformations in the bound state are likely to be explored by the isolated protein too, therefore supporting a model where the addition of the small molecule restricts the available conformational space.We present an analytical model representation of the electron density ρ(r) in molecules in the form of expansions of a few functions (exponentials and a Gaussian) per atom. Based on a former analytical model of ρ(r) in atoms, we devised its molecular implementation by introducing the anisotropy inherent in the electron distribution of atoms in molecules by means of proper anisotropic functions. The resulting model named A2MD (anisotropic analytical model of density) takes an analytical form highly suitable for obtaining the electron density in large biomolecules as its computational cost scales linearly with the number of atoms. To obtain the parameters of the model, we first devised a fitting procedure to reference electron densities obtained in ab initio correlated quantum calculations. Second, in order to skip costly ab initio calculations, we also developed a machine learning (ML)-based predictor that used neural networks trained on broad molecular datasets to determine the parameters of the model. The resulting ML methodology that we named A2MDnet (A2MD network-trained) was able to provide reliable electron densities as a basis to predict molecular features without requiring quantum calculations. The results presented together with the low computational scaling associated to the A2MD representation of ρ(r) suggest potential applications to obtain reliable electron densities and ρ(r)-based molecular properties in biomacromolecules.Rotavirus group A remains a major cause of diarrhea in infants and young children worldwide. The permanent emergence of new genotypes puts the potential effectiveness of vaccines under serious questions. Thirteen VP1 structures with mutations mapping to the RNA entry site were analyzed using molecular dynamics simulations, and the results were combined with the experimental findings reported previously. The results revealed structural fluctuations in the protein-protein recognition sites and in the bottleneck of the RNA entry site that may affect the interaction of different proteins and delay the initiation of the viral replication, respectively. Altogether, the structural analysis of VP1 in the region crucial for the initiation of the viral replication, mainly the bottleneck site, may boost efforts to develop antivirals, as they might complement the available vaccines.Microbe class I terpene cyclases (TPCs) are responsible for deriving numerous functionally and structurally diverse groups of terpenoid natural products. The conformational change of their active pockets from "open" state to "closed" state upon substrate binding has been clarified. However, the key structural basis relevant to this active pocket dynamics and its detailed molecular mechanism are still unclear. In this work, on the basis of the molecular dynamics (MD) on two microbe class I TPCs (SdS and bCinS), we propose that the active pocket dynamics is highly dependent on the residue orientation of two conserved structural bases R-D dyad and X-R-D triad, rather than the previously suggested flexibility of kink region. Actually, we considered that the flexibility of kink region is synchronous with the R residue orientation of the X-R-D triad, which could regulate the entrance size of active pocket and thus affect the substrate selectivity of active pocket by utilizing the promiscuity of the X-R-D triad. Furthermore, to better understand the function of the two structural bases, two intelligible models of "PPi catcher-locker" and "selector-PPi sensor-orienter" are proposed to, respectively, describe the R-D dyad and X-R-D triad and broadened to more microbe class I TPCs. These findings exhibit the dynamics of active pocket inaccessible in static crystal structures and provide useful structural basis knowledge for further design of microbe class I TPCs with different cyclization ability.Histone methylation reader proteins (HMRPs) regulate gene transcription by recognizing, at their "aromatic cage" domains, various Lys/Arg methylation states on histone tails. Because epigenetic dysregulation underlies a wide range of diseases, HMRPs have become attractive drug targets. However, structure-based efforts in targeting them are still in their infancy. Structural information from functionally unrelated aromatic-cage-containing proteins (ACCPs) and their cocrystallized ligands could be a good starting point. In this light, we mined the Protein Data Bank to retrieve the structures of ACCPs in complex with cationic peptidic/small-molecule ligands. Our analysis revealed that the vast majority of retrieved ACCPs belong to three classes transcription regulators (chiefly HMRPs), signaling proteins, and hydrolases. Although acyclic (and monocyclic) amines and quats are the typical cation-binding functional groups found in HMRP small-molecule inhibitors, numerous atypical cationic groups were identified in non-HMRP inhibitors, which could serve as potential bioisosteres to methylated Lys/Arg on histone tails. Also, as HMRPs are involved in protein-protein interactions, they possess large binding sites, and thus, their selective inhibition might only be achieved by large and more flexible (beyond rule of five) ligands. Hence, the ligands of the collected dataset represent suitable versatile templates for further elaboration into potent and selective HMRP inhibitors.Deep learning has demonstrated significant potential in advancing state of the art in many problem domains, especially those benefiting from automated feature extraction. Yet, the methodology has seen limited adoption in the field of ligand-based virtual screening (LBVS) as traditional approaches typically require large, target-specific training sets, which limits their value in most prospective applications. Here, we report the development of a neural network architecture and a learning framework designed to yield a generally applicable tool for LBVS. Our approach uses the molecular graph as input and involves learning a representation that places compounds of similar biological profiles in close proximity within a hyperdimensional feature space; this is achieved by simultaneously leveraging historical screening data against a multitude of targets during training. Cosine distance between molecules in this space becomes a general similarity metric and can readily be used to rank order database compounds in LBVS workflows. We demonstrate the resulting model generalizes exceptionally well to compounds and targets not used in its training. In three commonly employed LBVS benchmarks, our method outperforms popular fingerprinting algorithms without the need for any target-specific training. Moreover, we show the learned representation yields superior performance in scaffold hopping tasks and is largely orthogonal to existing fingerprints. Summarily, we have developed and validated a framework for learning a molecular representation that is applicable to LBVS in a target-agnostic fashion, with as few as one query compound. Our approach can also enable organizations to generate additional value from large screening data repositories, and to this end we are making its implementation freely available at https//github.com/totient-bio/gatnn-vs.The efflux transporter P-glycoprotein (P-gp) is responsible for the extrusion of a wide variety of molecules, including drug molecules, from the cell. Therefore, P-gp-mediated efflux transport limits the bioavailability of drugs. To identify potential P-gp substrates early in the drug discovery process, in silico models have been developed based on structural and physicochemical descriptors. In this study, we investigate the use of molecular dynamics fingerprints (MDFPs) as an orthogonal descriptor for the training of machine learning (ML) models to classify small molecules into substrates and nonsubstrates of P-gp. MDFPs encode the information from short MD simulations of the molecules in different environments (water, membrane, or protein pocket). The performance of the MDFPs, evaluated on both an in-house dataset (3930 compounds) and a public dataset from ChEMBL (1114 compounds), is compared to that of commonly used 2D molecular descriptors, including structure-based and property-based descriptors. We find that all tested classifiers interpolate well, achieving high accuracy on chemically diverse subsets. However, by challenging the models with external validation and prospective analysis, we show that only tree-based ML models trained on MDFPs or property-based descriptors generalize well to regions of the chemical space not covered by the training set.Prediction of protein stability changes caused by mutation is of major importance to protein engineering and for understanding protein misfolding diseases and protein evolution. The major limitation to these applications is the fact that different prediction methods vary substantially in terms of performance for specific proteins; i.e., performance is not transferable from one type of mutation or protein to another. In this study, we investigated the performance and transferability of eight widely used methods. We first constructed a new data set composed of 2647 mutations using strict selection criteria for the experimental data and then defined a variety of subdata sets that are unbiased with respect to various aspects such as mutation type, stabilization extent, structure type, and solvent exposure. Benchmarking the methods against these subdata sets enabled us to systematically investigate how data set biases affect predictor performance. In particular, we use a reduced amino acid alphabet to quantify the bias toward mutation type, which we identify as the major bias in current approaches. Our results show that all prediction methods exhibit large biases, stemming not from failures of the models applied but mostly from the selection biases of experimental data used for training or parametrization. Our identification of these biases and the construction of new mutation-type-balanced data should lead to the development of more balanced and transferable prediction methods in the future.Computational prediction of limiting activity coefficients is of great relevance for process design. For highly nonideal mixtures including molecules with directed interactions, methods that maintain the molecular character of the solvent are most promising. Computational expense and force-field deficiencies are the main limiting factors that prevent the use of high-throughput molecular dynamics (MD) simulations in a predictive setup. The combination of MD simulations and machine learning used in this work accounts for both issues. Comparison to published data including free-energy simulations, COSMO-RS and UNIFAC models, reveals competitive prediction accuracy.The development of molecular descriptors is a central challenge in cheminformatics. Most approaches use algorithms that extract atomic environments or end-to-end machine learning. However, a looming question is that how do these approaches compare with the critical eye of trained chemists. The CAS fingerprint engages expert chemists to curate chemical motifs, which they deem could influence bioactivity. In this paper, we benchmark the CAS fingerprint against commonly used fingerprints using a well-established benchmark set of 88 targets. We show that the CAS fingerprint outperforms most of the commonly used molecular fingerprints. Analysis of the CAS fingerprint reveals that experts tend to select features that are rarely reported in the literature, though not all rare features are selected. Our analysis also shows that the CAS fingerprint provides a different source of information compared to other commonly used fingerprints. These results suggest that anthropomorphic insights do have predictive power and highlight the importance of a chemist-in-the-loop approach in the era of machine learning.The emergence of the new coronavirus (nCoV-19) has impacted human health on a global scale, while the interaction between the virus and the host is the foundation of the disease. The viral genome codes a cluster of proteins, each with a unique function in the event of host invasion or viral development. Under the current adverse situation, we employ virtual screening tools in searching for drugs and natural products which have been already deposited in DrugBank in an attempt to accelerate the drug discovery process. This study provides an initial evaluation of current drug candidates from various reports using our systemic in silico drug screening based on structures of viral proteins and human ACE2 receptor. Additionally, we have built an interactive online platform (https//shennongproject.ai/) for browsing these results with the visual display of a small molecule docked on its potential target protein, without installing any specialized structural software. With continuous maintenance and incorporation of data from laboratory work, it may serve not only as the assessment tool for the new drug discovery but also an educational web site for the public.A traditional single-target analgesic, though it may be highly selective and potent, may not be sufficient to mitigate pain. An alternative strategy for alleviation of pain is to seek simultaneous modulation at multiple nodes in the network of pain-signaling pathways through a multitarget analgesic or drug combinations. Here we present a comprehensive pain-domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated computing tools for target identification and systems pharmacology research. Pain-CKB is constructed on the basis of our established chemogenomics technology with new features, including multiple compound support, multicavity protein support, and customizable symbol display. The determination of bioactivity is also revised to avoid the use of complex machine learning models. Our one-stop computing platform describes the chemical molecules, genes, and proteins involved in pain regulation. To date, Pain-CKB has archived 272 analgesics in the market, 84 pain-related targets with 207 available 3D crystal or cryo-EM structures, and 234 662 chemical agents reported for these target proteins. Moreover, Pain-CKB implements user-friendly web-interfaced computing tools and applications for the prediction and analysis of the relevant protein targets and visualization of the outputs, including HTDocking, TargetHunter, BBB permeation predictor, NGL viewer, Spider Plot, etc. The Pain-CKB server is accessible at https//www.cbligand.org/g/pain-ckb.Uric acid (UA) has an enormous competence to aggregate over melamine (Mel), producing large UA clusters that "drag" Mel toward them. Such a combination of donor-acceptor pairs provides a robust Mel-UA composite, thereby denoting a high complexity. Thus, a straightforward but pragmatic methodology might indeed require either destruction of the aggregation of UA or impediment of the hydrogen-bonded cluster of Mel and UA. Here, potassium citrate (K3Cit) is used as a potent inhibitor for a significant decrease of large UA-Mel clusters. The underlying mechanisms of synchronous interactions between K3Cit and the Mel-UA pair are examined by the classical molecular dynamics simulation coupled with the enhanced sampling method. K3Cit binds to the Mel-UA pair profoundly to produce a Mel-UA-K3Cit complex with favorable complexation energy (as indicated by the reckoning of pairwise ΔGbind° employing the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method). The strength of interaction follows the order UA-K3Cit > Mel-K3Cit > Mel-UA, thus clearly demonstrating the instability caused by upsetting the π-stacking of UA and hydrogen bonding of Mel-UA simultaneously. The comprehensive, strategically designed "direct approach" and "indirect approach" cluster structure analysis shows that K3Cit reduces the direct approach Mel-UA cluster size significantly irrespective of ensemble variation. Furthermore, the estimation of potentials of mean force (PMFs) reveals that the (UA)decamer-Mel interaction prevails over (UA)tetramer-Mel. The dynamic property (dimer existence autocorrelation functions) proves the essence of dimerization between Mel and UA in the absence and presence of K3Cit. Moreover, the calculation of the preferential interaction parameter provides the concentration at which Mel-K3Cit and UA-K3Cit interactions are predominant over the interaction of Mel and UA.A highly infectious coronavirus, SARS-CoV-2, has spread in many countries. This virus recognizes its receptor, angiotensin-converting enzyme 2 (ACE2), using the receptor binding domain of its spike protein subunit S1. Many missense mutations are reported in various human populations for the ACE2 gene. In the current study, we predict the affinity of many ACE2 variants for binding to S1 protein using different computational approaches. The dissociation process of S1 from some variants of ACE2 is studied in the current work by molecular dynamics approaches. We study the relation between structural dynamics of ACE2 in closed and open states and its affinity for S1 protein of SARS-CoV-2.Over 5 million people around the world have tested positive for the beta coronavirus SARS-CoV-2 as of May 29, 2020, a third of which are in the United States alone. These infections are associated with the development of a disease known as COVID-19, which is characterized by several symptoms, including persistent dry cough, shortness of breath, chills, muscle pain, headache, loss of taste or smell, and gastrointestinal distress. COVID-19 has been characterized by elevated mortality (over 100 thousand people have already died in the US alone), mostly due to thromboinflammatory complications that impair lung perfusion and systemic oxygenation in the most severe cases. While the levels of pro-inflammatory cytokines such as interleukin-6 (IL-6) have been associated with the severity of the disease, little is known about the impact of IL-6 levels on the proteome of COVID-19 patients. The present study provides the first proteomics analysis of sera from COVID-19 patients, stratified by circulating levels of IL-6, and correlated to markers of inflammation and renal function.
My Website: