Structural and Dynamic Basis of Human Cytochrome P450 7B1: A Survey of Substrate Selectivity and Major Active Site Access Channels

Cytochrome P450 (CYP) 7B1 is a steroid cytochrome P450 7α‐hydroxylase that has been linked directly with bile salt synthesis and hereditary spastic paraplegia type 5 (SPG5). The enzyme provides the primary metabolic route for neurosteroids dehydroepiandrosterone (DHEA), cholesterol derivatives 25‐hydroxycholesterol (25‐HOChol), and other steroids such as 5α‐androstane‐3β,17β‐diol (anediol), and 5α‐androstene‐3β,17β‐diol (enediol). A series of investigations including homology modeling, molecular dynamics (MD), and automatic docking, combined with the results of previous experimental site‐directed mutagenesis studies and access channels analysis, have identified the structural features relevant to the substrate selectivity of CYP7B1. The results clearly identify the dominant access channels and critical residues responsible for ligand binding. Both binding free energy analysis and total interaction energy analysis are consistent with the experimental conclusion that 25‐HOChol is the best substrate. According to 20 ns MD simulations, the Phe cluster residues that lie above the active site, particularly Phe489, are proposed to merge the active site with the adjacent channel to the surface and accommodate substrate binding in a reasonable orientation. The investigation of CYP7B1–substrate binding modes provides detailed insights into the poorly understood structural features of human CYP7B1 at the atomic level, and will be valuable information for drug development and protein engineering.     

Sorptive elimination of fluoride from contaminated groundwater in a fixed bed column: A kinetic model validation based study

The current investigation involves a continuous adsorption experiment in a packed bed column for the sorptive elucidation of fluoride from contaminated groundwater using an activated soil-clay mixture. Through the combination of naturally accessible laterite soil with silica enriched clay (3:1 ratio), a low-cost Al–Si heterogeneous material has been developed. Following detailed characterization, the developed materials were employed in a long-time column process to achieve a high degree of fluoride separation from real-world groundwater. In a packed bed column investigation, the effect of bed height, initial fluoride concentration, and flow rate on the breakthrough properties of the adsorption system were investigated. By using a non-linear regression equation, three model kinetics, such as the Thomas Model, Adams-Bohart Model, and Yoon-Nelson Model, were fitted to validate the column-based experimental data, by analysing the breakthrough curves profiles, and distinct kinetic parameters. The Bed Depth Service Time Analysis (BDST) model was tested to express the effect of bed height on breakthrough curves, as well as to predict the time for breakthrough, and material depletion under optimal conditions. The Thomas and Yoon-Nelson models were identified to be the most appropriate ones for describing the entire breakthrough curve, whereas the Adams-Bohart model was only utilised to predict the first half of the dynamic process. With correlation coefficients (R²) 0.96, the experimental results were well suited to Thomas, Yoon-Nelson, and Adams-Bohart models. Finally, regeneration assessment was carried out where even after four cycles of operation, regenerated adsorbent showed a rejection efficacy of 78% to fluoride that proves the viability of the material and methodology.     

Optimization of Fenoprofen solubility within green solvent through developing a novel and accurate GSO-GPR predictive model

Indisputable importance of drug solubility in various industrial perspectives has motivated the scientists to evaluate different techniques to improve it. Fenoprofen is a significant nonsteroidal anti-inflammatory drug (NSAID), that is the orally administered to relieve mild to moderate pain and the unfavorable symptoms of osteoarthritis and rheumatoid arthritis (i.e., inflammation and stiffness). Supercritical fluids (SCFs) belong to a certain type of fluids, in which their temperature and pressure are higher than the critical point. This property allows the CO₂SCF to simultaneously possess the characteristics of both a liquid and a gas. The prominent target of this paper is to mathematically develop three predictive models via machine learning (ML) technique to optimize the solubility of Fenoprofen in CO₂SCF. In this study, we have 32 data vectors in each dataset, including two input features of pressure and temperature. The output target is solubility, which we are going to model and analyze. Models are constructed through the use of Modular ANN (MANN), Gaussian processes regression (GPR), and the K-Nearest Neighbor technique (KNN) in this body of work. The glowworm swarm optimization (GSO) swarm-based method is utilized in order to carry out the process of model optimization. The root mean squared error (RMSE) rates for GSO-KNN, GSO-MANN, and GSO-GPR are respectively 5.25E-04, 5.46E-04, and 3.01E-05. The aforementioned models were also judged according to a number of other criteria, and since the GSO-GPR model was found to be the most effective according to all of these standards, it is being treated as the conclusive model of this investigation. In addition, the maximum error has been brought down to 5.02E-05 with the help of this model, which has an R2-score of 0.999.     

Switch From Pauli-Lowering to LUMO-Lowering Catalysis in Brønsted Acid-Catalyzed Aza-Diels-Alder Reactions

Brønsted acid-catalyzed inverse-electron demand (IED) aza-Diels-Alder reactions between 2-aza-dienes and ethylene were studied using quantum chemical calculations. The computed activation energy systematically decreases as the basic sites of the diene progressively become protonated. Our activation strain and Kohn-Sham molecular orbital analyses traced the origin of this enhanced reactivity to i) “Pauli-lowering catalysis” for mono-protonated 2-aza-dienes due to the induction of an asynchronous, but still concerted, reaction pathway that reduces the Pauli repulsion between the reactants; and ii) “LUMO-lowering catalysis” for multi-protonated 2-aza-dienes due to their highly stabilized LUMO(s) and more concerted synchronous reaction path that facilitates more efficient orbital overlaps in IED interactions. In all, we illustrate how the novel concept of “Pauli-lowering catalysis” can be overruled by the traditional concept of “LUMO-lowering catalysis” when the degree of LUMO stabilization is extreme as in the case of multi-protonated 2-aza-dienes.     

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available.     

A Multi-Objective Approach for Drug Repurposing in Preeclampsia

Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as “targets”. The proteins labeled as “off-targets” were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)_α=160.9 = 0.258, the Enrichment Factor (EF)_1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia.     

An investigation on surface tensions of Pb-free solder materials

Surface tensions of some Pb-free solder systems such as Ag–Bi–Sn with cross-sections Ag/Bi = 1/1, Ag/Bi = 1/2, Ag/Bi = 2/1, In–Sn–Zn with cross-sections Sn/In = 1/1, Sn/In = 1/3 and (Ag₇Cu₃)_100?x Sn_x with cross-section Ag/Cu = 7/3 are calculated from the sub-binary surface tension data using the models, such as the Muggianu, Kohler, Toop models, Butler’s equation and Chou’s General Solution Model (GSM) at 873, 923 and 1073 K, respectively. The surface tension of In–Sn–Zn increases wavily with increasing amount of Zn and it is found that the best models are the GSM for both cross-sections in question while GSM becomes the best model for (Ag₇Cu₃)_100?x Sn_x alloy in the whole experimental range. Moreover, the surface tension of (Ag₇Cu₃)_100?x Sn_x decreases slightly with increasing amount of Sn. The Muggianu, Butler and Butler models are determined as the best models for the cross-sections in the order given above for entire measurement range, respectively, and the surface tension of Ag–Bi–Sn decreases slightly with an increasing amount of Bi and Ag but increases with increasing Sn in liquid alloys.     

Gap-behavior in the vicinity of a saturation transverse field

We have studied the behavior of the energy gap of the 1D AF spin- XXZ model in a transverse magnetic field (h) using the exact diagonalization technique. The ground state phase diagram consists of two spin-flop and paramagnetic phases. Using a modified finite-size scaling approach, we have computed the critical exponent of the energy gap in the vicinity of the critical transverse field h_c(Δ). Our numerical results confirm that the continuous phase transition from the spin-flop phase to the paramagnetic one is in the universality class of the Ising model in the transverse field (ITF). By applying conformal estimates of a small perturbation (h≪1), we have also justified our numerical results.