Singlet Oxygen Generation by Porphyrins and Metalloporphyrins Revisited: a Quantitative Structure-property Relationship (QSPR) Study

state followed by formation of singlet oxygen (1O2), which is a highly reactive species and mediates various oxidative processes. The design of advanced sensitizers based on porphyrin compounds have attracted significant attention in recent years. However, it is still difficult to predict the efficiency of singlet oxygen generation for a given structure. Our goal was to develop a quantitative structure-property relationship (QSPR) model for the fast virtual screening and prediction of singlet oxygen quantum yields for pophyrins and metalloporphyrins. We performed QSPR analysis of a dataset containing 32 compounds, including various porphyrins and their analogues (chlorins and bacteriochlorins). Quantum-chemical descriptors were calculated using Density Functional Theory (DFT), namely B3LYP and M062X functionals. Three different machine learning methods were used to develop QSPR models: random forest regression (RFR), support vector regression (SVR), and multiple linear regression (MLR). The optimal QSPR model «structure – singlet oxygen generation quantum yield» obtained using RFR method demonstrated high determination coefficient for the training set (R2 = 0.949) and the highest predicting ability for the test set (pred_R2 = 0.875). This proves that the developed QSPR method is realiable and can be directly applied in the studies of singlet oxygen generation both for free base porphyrins and their metal complexes. We believe that QSPR approach developed in this study can be useful for the search of new poprhyrin photosensitizers with enhanced singlet oxygen generation ability

Development of a stabilized trimer pre-fusion RSV F recombinant viral glycoprotein vaccine

It has been known that the RSV fusion protein F is a target vaccine protein to produce a protective immune response. The VRC has shown (Ngwuta, et.al.) through binding competition assays that the amount of pre-fusion site Ø–specific antibodies correlates with neutralizing (NT) activity, whereas the pre/post-fusion site II mAbs does not correlate with neutralization. Our results indicate that RSV NT activity in human sera is primarily derived from pre-F–specific antibodies, and therefore, inducing or boosting NT activity by vaccination will be facilitated by using pre-F antigens that preserve site Ø. Therefore, the instability of the RSV pre-fusion conformation has limited the potential of this as a vaccine antigen. Therefore, the VRC has designed a structurally stabilized glycoprotein pre-fusion RSV F trimer vaccine antigen and has shown it to be highly immunogenic in preclinical studies. A description of challenges in the development of a high productivity CHO cell line, production process and product quality and antigenic characterization assays for Phase I clinical material will be presented along with comparison of pre-clinical results of research to development material. Please click Additional Files below to see the full abstract

Evaluating the efficacy of amino acids as CO<SUB>2</SUB> capturing agents: a first principles investigation

Comprehension of the basic concepts for the design of systems for CO2 adsorption is imperative for increasing interest in technology for CO2 capture from the effluents. The efficacy of 20 naturally occurring amino acids (AAs) is demonstrated as the most potent CO2 capturing agents in the process of chemical absorption and physisorption through a systematic computational study using highly parametrized M05–2X/6-311+G(d,p) method. The ability of AAs to bind CO2 both in the noncovalent and covalent fashion and presence of multiple adsorption sites with varying magnitude of binding strengths in all 20 AAs makes them as most promising materials in the process of physisorption. The binding energies (BEs) estimating the strength of noncovalent interaction of AAs and CO2 are calculated and results are interpreted in terms of the nature and strength of the various types of cooperative interactions which are present. The study underlines the possibility to engineer the porous solid materials with extended networks by judiciously employing AA chains as linkers which can substantially augment their efficacy. Results show that a significant increase in the CO2···AA affinity is achieved in the case of AAs with polar neutral side chains. Furthermore, the study proposes AAs as effective alternatives to alkanolamines in chemical dissolution of CO2

Estimating the binding ability of onium ions with CO<SUB>2</SUB> and π systems: a computational investigation

Density functional theory (DFT) calculations have been employed on 165 complexes of onium ions (NH4+, PH4+, OH3+, SH3+) and methylated onium ions with CO2, aromatic (C6H6) and heteroaromatic (C5H5X, X = N, P; C4H5Y, Y = N, P; C4H4Z, Z = O, S) systems. The stability of CO2⋯onium, CO2⋯π and onium⋯π complexes was shown to be mediated through various noncovalent interactions such as hydrogen bonding, NH–π, PH–π, OH–π, SH–π, CH–π and π–π. We have discussed 17 complexes wherein the proton transfer occurs between the onium ion and the heteroaromatic system. The binding energy is found to decrease with increasing methyl substitution of the complexes containing onium ions. Binding energy components of all the noncovalent complexes were explored using localized molecular orbital energy decomposition analysis (LMO-EDA). The CO2⋯π complexes were primarily stabilized by the dispersion term followed by contributions from electrostatic and polarization components. In general, for onium ion complexes with CO2 or π systems, the electrostatic and polarization terms primarily contribute to stabilize the complex. As the number of methyl groups increases on the onium ion, the dispersion term is seen to have a key role in the stabilization of the complex. Quantum theory of atoms in molecules (QTAIM) analysis and charges based on natural population analysis (NPA) in various complexes have also been reported in order to determine the nature of noncovalent interactions in different complexes

Buckybowls as adsorbents for CO<SUB>2</SUB>, CH<SUB>4</SUB>, and C<SUB>2</SUB>H<SUB>2</SUB>: binding and structural insights from computational study

Noncovalent functionalization of buckybowls sumanene (S), corannulene (R), and coronene (C) with greenhouse gases (GGs) such as CO<SUB>2</SUB>, CH<SUB>4</SUB> (M), and C<SUB>2</SUB>H<SUB>2</SUB> (A) has been studied using hybrid density functional theory. The propensity and preferences of these small molecules to interact with the concave and convex surfaces of the buckybowls has been quantitatively estimated. The results indicate that curvature plays a significant role in the adsorption of these small molecules on the π surface and it is observed that buckybowls have higher binding energies (BEs) compared with their planar counterpart coronene. The concave surface of the buckybowl is found to be more feasible for adsorption of small molecules. BEs of small molecules towards π systems is CO<SUB>2</SUB> > A > M and the BEs of π systems toward small molecules is S > R > C. Obviously, the binding preference is dictated by the way in which various noncovalent interactions, such as π···π, lone pair···π, and CH···π manifest themselves on carbaneous surfaces. To delineate the intricate details of the interactions, we have employed Bader's quantum theory of atoms in molecule and localized molecular orbital energy decomposition analysis (LMO-EDA). LMO-EDA, which measures the contribution of various components and traces the physical origin of the interactions, indicates that the complexes are stabilized largely by dispersion interactions

Physicochemical and Ion-Sensing Properties of Benzofurazan-Appended Calix[4]arene in Solution and on Gold Nanoparticles: Spectroscopy, Microscopy, and DFT Computations in Support of the Species of Recognition

A calix[4]­arene conjugate (L) functionalized at the lower rim with a benzofurazan fluorophore (NBD) and at the upper rim with a thioether moiety has been synthesized and characterized by 1H NMR, 13C NMR, and mass spectrometry techniques. Both the absorption and emission spectral data for L in different solvents exhibited progressive changes with an increase in polarity. Ion recognition studies were performed by absorption and fluorescence spectroscopy using 10 different metal ions. Among these, Hg2+ exhibited greater changes in these spectra, whereas Cu2+ showed only significant changes and all other ions showed no change in the spectral features. Although the Hg2+ has dominant influence on the spectral features and provides a detection limit of 56.0 ± 0.6 ppb, the selectivity was hampered because of the presence of the derivatizations present on both the rims of L for ion interaction in solution. Therefore, L was immobilized onto gold nanoparticles (AuNPL’s) so that the upper rim derivatizations anchor onto the gold surface through Au–S interactions, and this leaves out only the lower rim NBD derivatization for interaction with ions selectively. The AuNPL’s were characterized by transmission electron microscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy (XPS) analyses. The surface characteristics were analyzed by contact angle measurements. The AuNPL’s exhibit greater selectivity and enhanced sensitivity for Hg2+ ions with a lowest detection limit of 48.0 ± 0.8 ppb. The immobilization of L onto AuNPs was reflected in the corresponding fluorescence lifetime values, and the addition of Hg2+ to either L or AuNPL showed fluorescence quenching. The reversible recognition of Hg2+ by L was demonstrated by titrating L or AuNPL with Hg2+ followed by tetra-butyl ammonium iodide for several cycles. The structural features of Hg2+-bound species were demonstrated by density functional theory computations and were supported by the XPS data. The Hg2+ induces aggregated fibrillar morphology into supramolecular L, as demonstrated by microscopy when Hg2+ was added either to L or to AuNPL, supporting aggregation-caused quenching

Computational Design of Functionalized Imidazolate Linkers of Zeolitic Imidazolate Frameworks for Enhanced CO₂ Adsorption

Zeolitic imidazolate frameworks (ZIFs) represent the class of metal–organic frameworks (MOFs) that possess high porosity, large surface area, exceptional thermal, and chemical stability. Because of these properties, ZIFs are being employed extensively in gas separation and selective CO₂ adsorption. We have chosen the structural modification approach to enhance the CO₂ binding ability of various imidazolate (Im) linkers of ZIFs by systematically varying the substituents at 2, 4, and 5 positions of Im ring with CH₃, Cl, CN, OH, NH₂, and NO₂ functional groups. Density functional theory (DFT) calculations have been employed to identify and quantify the CO₂ binding ability of various adsorption sites present in 137 Im linkers. The study demonstrates that the Im linkers with asymmetrical substitution, viz. NO₂/OH, CN/OH, and Cl/OH combinations are highly promising linkers of ZIFs for efficient CO₂ adsorption. The QTAIM analysis characterizes these interactions as noncovalent interactions which are stabilized by weak hydrogen bond and van der Waals (vdWs) interactions. Localized molecular orbital energy decomposition analysis (LMO-EDA) performed on substituted Im···CO₂ complexes reveals that CO₂ binding is governed by a combination of H-bonding, electrostatic, and dispersion interactions. The findings of the study will serve as guide-in principles to synthesize new adsorbents with enhanced and selective CO₂ adsorption

Neural Network Modelling of Speech Emotion Detection

In making the Machines Intelligent, and enable them to work as human, Speech recognition is one of the most essential requirement. Human Language conveys various types of information such as the energy, pitch, loudness, rhythm etc., in the sound, the speech and its context such as gender, age and the emotion. Identifying the emotion from a speech pattern is a challenging task and the most useful solution especially in the era of widely developing speech recognition systems with digital assistants. Digital assistants like Bixby, Blackberry assistant are building products that consist of emotion identification and reply the user in step with user point of view. The objective of this work is to improve the accuracy of the speech emotion prediction using deep learning models. Our work experiments with the MLP and CNN classification models on three benchmark datasets with 5700 speech files of 7 emotion categories. The proposed model showed improved accuracy