Insilico evaluation on potential Mt-Sp1/matriptase inhibitors data: DFT and molecular modelling approaches.

Nine heterocyclic compounds were investigated using density functional theory, molecular operating environment software, material studio, swissparam (Swiss drug design) software. In this work, the descriptors generated from the optimized compounds proved to be efficient and explain the level of reactivity of the investigated compound. The developed quantitative structure activity relationship (QSAR) model was predictive and reliable. Also, compound 9 proved to be capable of inhibiting Mt-Sp1/Matriptase (pdb id: 1eax) than other examined heterocyclic compounds. Target prediction analysis was carried out on the compound with highest binding affinity (Compound 9) and the results were reported.

Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach.

Drug-induced nephrotoxicity remains a common problem after exposure to medications and diagnostic agents, which may be heightened in the kidney microenvironment and deteriorate kidney function. In this study, the toxic effects of fourteen marked drugs with the individual chemical structure were evaluated in kidney cells. The quantitative structure-activity relationship (QSAR) approach was employed to investigate the potential structural descriptors of each drug-related to their toxic effects. The most reasonable equation of the QSAR model displayed that the estimated regression coefficients such as the number of ring assemblies, three-membered rings, and six-membered rings were strongly related to toxic effects on renal cells. Meanwhile, the chemical properties of the tested compounds including carbon atoms, bridge bonds, H-bond donors, negative atoms, and rotatable bonds were favored properties and promote the toxic effects on renal cells. Particularly, more numbers of rotatable bonds were positively correlated with strong toxic effects that displayed on the most toxic compound. The useful information discovered from our regression QSAR models may help to identify potential hazardous moiety to avoid nephrotoxicity in renal preventive medicine.

Failure to Identify Robust Latent Variables of Positive or Negative Valence Processing Across Units of Analysis.

BACKGROUND: The heterogeneous nature of mood and anxiety disorders highlights a need for dimensionally based descriptions of psychopathology that inform better classification and treatment approaches. Following the Research Domain Criteria approach, this investigation sought to derive constructs assessing positive and negative valence domains across multiple units of analysis. METHODS: Adults with clinically impairing mood and anxiety symptoms (N = 225) completed comprehensive assessments across several units of analysis. Self-report assessments included nine questionnaires that assess mood and anxiety symptoms and traits reflecting the negative and positive valence systems. Behavioral assessments included emotional reactivity and distress tolerance tasks, during which skin conductance and heart rate were measured. Neuroimaging assessments included fear conditioning and a reward processing task. The latent variable structure underlying these measures was explored using sparse Bayesian group factor analysis. RESULTS: Group factor analysis identified 11 latent variables explaining 31.2% of the variance across tasks, none of which loaded across units of analysis or tasks. Instead, variance was best explained by individual latent variables for each unit of analysis within each task. Post hoc analyses 1) showed associations with small effect sizes between latent variables that were derived separately from functional magnetic resonance imaging and self-report data and 2) showed that some latent variables are not directly related to individual valence system constructs. CONCLUSIONS: The lack of latent structure across units of analysis highlights challenges of the Research Domain Criteria approach and suggests that while dimensional analyses work well to reveal within-task features, more targeted approaches are needed to reveal latent cross-modal relationships that could illuminate psychopathology.

Study on quantitative structure-biodegradability relationships of amine collectors by GFA-ANN method.

In this study, the biodegradability of 17 amine collectors, categorized by fatty amine, quaternary ammonium compounds and oxygen-containing amine collectors, are tested with the Closed Bottle Test for 90 days, and the results indicate most amine collectors are not readily biodegradable. The oxygen-containing amine collectors have the best biodegradability due to the introduced oxygen-containing functional groups, subsequently fatty amine collectors with branched chains, while the tested quaternary ammonium compounds all have poor biodegradation ability. Besides, we search for and calculate 35 molecular descriptors to develop the quantitative structure biodegradability relationship (QSBR) of amine collectors. With the Genetic Function Approximation (GFA) algorithm, two sets of important molecular descriptors related to biodegradability (q) of amine collectors are selected from 35 molecular descriptors. Based on internal and external validations, the robust and reliable non-linear QSBR model with the squared correlation coefficient above 0.99 is determined via Artificial Neural Network (ANN) method, where the descriptors are respectively CL, N, ELUMO, δv2, indicating the biodegradable ability of amine collectors is correlated with the alkyl chain lengths (CL), the number of nitrogen atom-containing compounds (N), energy of the lowest unoccupied molecular orbital (ELUMO) and valence second-order connectivity index (δv2).

Molecular modeling and design of some ß-amino alcohol grafted 1,4,5-trisubstituted 1,2,3-triazoles derivatives against chloroquine sensitive, 3D7 strain of Plasmodium falciparum.

Resistance nature of Plasmodium falciparum (P. falciparum) to the most effective antimalarial drug, Artemisinin, intimidate the global goal of total eradication of malarial. In an attempt to overcome this challenge, the research was aimed at designing derivatives of ß-amino alcohol grafted 1,4,5-trisubstituted 1,2,3-triazoles with improve activity against the P. falciparum through structural modifications of the most active compound (design template), and their activity determined using the developed theoretical predictive model. To achieve this, the geometries were optimized via density functional theory (DFT) using B3LYP/6-31G∗ basis set to generate molecular descriptors for model development. Analysis of the developed model and the descriptors mean effect lead to the design of derivatives with improved activity. Five (5) theoretical models were developed, where the model {pIC50 = 5.95067(SpMin5_Bhi) - 0.0323461(RDF45m) + 0.0203865 (RDF95e) + 0.0499285 (L1m) - 3.50822} with the highest coefficient of determination (R2) of 0.9367, cross-validated R2 (Q2cv) of 0.8242, and the external validated R2 (R2 pred) of 0.9462, selected as the best model. The mean effect analysis revealed descriptor SpMin5_Bhi as the most contributive. The descriptor encodes the first ionization potentials of the compounds and are influenced by electron-withdrawing/donating substituents. Hence, structural modifications of the compound with the highest activity (a design template) using electron-withdrawing substituents such as -NO2, -SO3H, -Br, -I, -CH2CH3, and -CH3 was done at a different positions, to obtain five (5) hypothetical novel compounds. The statistical results, shows the robustness, excellent prediction power, and validity of the selected model. Descriptor analysis revealed the first ionization potential (SpMin5_Bhi) to play a significant role in the activity of ß-amino alcohol grafted 1,4,5-trisubstituted 1,2,3-triazoles derivatives. The five design derivatives of ß-amino alcohol grafted 1,4,5-trisubstituted 1,2,3-triazoles with higher activities revealed compound 21C to have an antimalarial activity of pIC50 = 6.7573 higher than it co-designed compounds and even the standard drug. This claim could be verified through molecular docking to determine their interaction with the target protein.

Redundant targeting of Isr1 by two CDKs in mitotic cells.

Protein phosphorylation is an essential regulatory mechanism that controls most cellular processes, integrating a variety of environmental signals to drive cellular growth. Isr1 is a negative regulator of the hexosamine biosynthesis pathway (HBP), which produces UDP-GlcNAc, an essential carbohydrate that is the building block of N-glycosylation, GPI anchors and chitin. Isr1 was recently shown to be regulated by phosphorylation by the nutrient-responsive CDK kinase Pho85, allowing it to be targeted for degradation by the SCFCDC4. Here, we show that while deletion of PHO85 stabilizes Isr1 in asynchronous cells, Isr1 is still unstable in mitotically arrested cells in a pho85∆ strain. We provide evidence to suggest that this is through phosphorylation by CDK1. Redundant targeting of Isr1 by two distinct kinases may allow for tight regulation of the HBP in response to different cellular signals.

Quantitative structure-activity relationship between the toxicity of amine surfactant and its molecular structure.

With the extensive applications and ongoing world demand, more and more amine surfactants are discharged into natural environment. However, the database about toxicity of amine surfactants is incomplete, which is not beneficial to environmental protection process. In this paper, the toxicity of 20 amine surfactants on Daphnia magna were tested to extend the toxicity data of amine surfactants. Besides, 35 molecular structure descriptors including quantum parameters, physicochemical parameters and topological indices were chosen and calculated as independent variables to develop the quantitative structure-activity relationship (QSAR) model between the toxicity of amine surfactants and their molecular structure by genetic function approximation (GFA) algorithm. According to statistical analysis, a robust model was built with the determination coefficient of (R2) was 0.962 and coefficient determinations of cross-validation (Rcv2) was 0.794. Meanwhile, external validation was implemented to evaluate the QSAR model. The result of coefficient determinations of cross-validation (Rext2) for external validation was calculated as 0.942, illustrating the model has great goodness-of-fit and good prediction ability.

Theoretical QSAR modelling and molecular docking studies of some 4-hydroxyphenylpyruvate dioxygenase (HPPD) enzyme inhibitors potentially used as herbicides.

Computational QSAR studies together with molecular docking calculations have been performed on 118 different derivatives of organic molecules potentially used as herbicides. The Becke's three parameter exchange functional (B3) hybrid with Lee, Yang and Parr correlation functional (LYP), termed as B3LYP hybrid function and 6-31G* basis set (B3LYP/6-31G*) were used to develop five models of QSAR using the GFA technique. Models 1, was preferred as the best model because it possesses certain statistical implications (Friedman LOF = 0.52567, R 2 = 0.9034, R a d j s t 2 = 0.8943, Q C V 2 = 0.87 98 and R p r e d . 2 = 0.8403)." The prepared model was validated internally and externally using training and test inhibitors. The molecular docking studies conducted in this study has actually outline the binding affinities of the 10 selected compounds (5, 25, 26, 27, 29, 35, 52, 55, 98 and 114) which were all in good correlation with their pIC50 values. The binding affinities of the 10 selected compounds range between -5.9 kcal/mol to -10.1 kcal/mol. The compounds 25 and 27 with binding affinities of -10.1 kcal/mol and -9.7 kcal/mol formed the most stable complexes with the receptor (HPPD) as compared to other inhibitors. The complexes of these inhibitors show two most important types of bonding; Hydrogen bonding and hydrophobic bond interaction with the target amino acid residues. The computational QSAR study together with the molecular docking has actually provided a valuable approach for agrochemical researchers in synthesizing and developing new herbicides with high potency against the target enzyme.

Molecular design of antioxidant lubricating oil additives via QSPR and analysis dynamic simulation method.

Alternatives antioxidant lubricant additives have been proposed by many researchers to replace long-time use of multifunctional lubricant additive, Zinc-dialkyl-dithiophosphate (ZDDP). Computational methods (QSPR and MD) were successfully used to design five novel anti-oxidant lubricating oil additives with improved properties and dynamic binding energies. The five novel antioxidant lubricant additives with improved properties and without sulfated ash, phosphorus, and sulfur (SAPS) were successfully designed. These group of newly designed additives were better than other similar research from the literature and could stop or terminate complete oxidation of the lubricant. Moreover, the result of molecular dynamics simulations (MD) in which 3-(2-(3-amino-4,5-dihydroxyphenyl)-3-chloro-4-oxoazetidin-1-yl)-2-argioquinazolin-4(3H)-one with the most promised dynamic binding energy of -1487.68 kcal/mol was found to be dynamically bound better on the simulated steel coated surface than the DLC coated surface and was also revealed to be excellently good when compared with commercially sold multifunctional additives, ZDDP (197.143 kcal/mol). These groups of five newly designed additives could be easily synthesized in the wet laboratory by adding -OH and or NH2 around the ortho, meta and para position of the phenyl group of the structure template. This research will help designing new oxidation resistance lubricating oil additives with improved properties that will reduce the capacity of base oil to oxidize and form sludge during the autoxidation process of the lubricating oil.

Myth or Truth: The Glass Forming Ability Class III Drugs Will Always Form Single-Phase Homogenous Amorphous Solid Dispersion Formulations.

The physical stability of amorphous solid dispersions (ASD) of active pharmaceutical ingredients (APIs) of high glass forming ability (GFA class III) is generally expected to be high among the scientific community. In this study, the ASD of ten-selected class III APIs with the two polymers, PVPVA 64 and HPMC-E5, have been prepared by spray-drying, film-casting, and their amorphicity at T0 was investigated by modulated differential scanning calorimetry and powder X-ray diffraction. It was witnessed that only five out of ten APIs form good quality amorphous solid dispersions with no phase separation and zero crystalline content, immediately after the preparation and drying process. Hence, it was further established that the classification of an API as GFA class III does not guarantee the formulation of single phase amorphous solid dispersions.

Computational Simulations Identified Two Candidate Inhibitors of Cdk5/p25 to Abrogate Tau-associated Neurological Disorders.

Deregulation of Cdk5 is a hallmark in neurodegenerative diseases and its complex with p25 forms Cdk5/p25, thereby causes severe neuropathological insults. Cdk5/p25 abnormally phosphorylates tau protein, and induces tau-associated neurofibrillary tangles in neurological disorders. Therefore, the pharmacological inhibition of Cdk5/p25 alleviates tau-associated neurological disorders. Herein, computational simulations probed two candidate inhibitors of Cdk5/p25. Structure-based pharmacophore investigated the essential complementary chemical features of ATP-binding site of Cdk5 in complex with roscovitine. Resultant pharmacophore harbored polar interactions with Cys83 and Asp86 residues and non-polar interactions with Ile10, Phe80, and Lys133 residues of Cdk5. The chemical space of selected pharmacophore was comprised of two hydrogen bond donors, one hydrogen bond acceptor, and three hydrophobic features. Decoy test validation of pharmacophore obtained highest Guner-Henry score (0.88) and enrichment factor score (7.23). The screening of natural product drug-like databases by validated pharmacophore retrieved 1126 compounds as candidate inhibitors of Cdk5/p25. The docking of candidate inhibitors filtered 10 molecules with docking score >80.00 and established polar and non-polar interactions with the ATP-binding site residues of Cdk5/p25. Finally, molecular dynamics simulation and binding free energy analyses identified two candidate inhibitors of Cdk5/p25. During 30 ns simulation, the candidate inhibitors established <3.0 Šroot mean square deviation and stable hydrogen bond interactions with the ATP-binding site residues of Cdk5/p25. The final candidate inhibitors obtained lowest binding free energies of -122.18 kJ/mol and - 117.26 kJ/mol with Cdk5/p25. Overall, we recommend two natural product candidate inhibitors to target the pharmacological inhibition of Cdk5/p25 in tau-associated neurological disorders.

A deep learning approach to estimation of subject-level bias and variance in high angular resolution diffusion imaging.

The ability to evaluate empirical diffusion MRI acquisitions for quality and to correct the resulting imaging metrics allows for improved inference and increased replicability. Previous work has shown promise for estimation of bias and variance of generalized fractional anisotropy (GFA) but comes at the price of computational complexity. This paper aims to provide methods for estimating GFA, bias of GFA and standard deviation of GFA quickly and accurately. In order to provide a method for bias and variance estimation that can return results faster than the previously studied statistical techniques, three deep, fully-connected neural networks are developed for GFA, bias of GFA, and standard deviation of GFA. The results of these networks are compared to the observed values of the metrics as well as those fit from the statistical techniques (i.e. Simulation Extrapolation (SIMEX) for bias estimation and wild bootstrap for variance estimation). Our GFA network provides predictions that are closer to the true GFA values than a Q-ball fit of the observed data (root-mean-square error (RMSE) 0.0077 vs 0.0082, p < .001). The bias network also shows statistically significant improvement in comparison to the SIMEX-estimated error of GFA (RMSE 0.0071 vs. 0.01, p < .001).

Atomic-Approach to Predict the Energetically Favored Composition Region and to Characterize the Short-, Medium-, and Extended-Range Structures of the Ti-Nb-Al Ternary Metallic Glasses.

Ab initio calculations were conducted to assist the construction of the n-body potential of the Ti-Nb-Al ternary metal system. Applying the constructed Ti-Nb-Al interatomic potential, molecular dynamics and Monte Carlo simulations were performed to predict a quadrilateral composition region, within which metallic glass was energetically favored to be formed. In addition, the amorphous driving force of those predicted possible glassy alloys was derived and an optimized composition around Ti15Nb45Al40 was pinpointed, implying that this alloy was easier to be obtained. The atomic structure of Ti-Nb-Al metallic glasses was identified by short-, medium-, and extended-range analysis/calculations, and their hierarchical structures were responsible to the formation ability and unique properties in many aspects.

Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning.

PURPOSE: To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-space datasets for use in stroke imaging. METHODS: Traditional diffusion spectrum imaging (DSI) capable of producing accurate NODDI and GFA parameter maps requires hundreds of q-space samples which renders the scan time clinically untenable. A convolutional neural network (CNN) was trained to generated NODDI and GFA parameter maps simultaneously from 10× undersampled q-space data. A total of 48 DSI scans from 15 stroke patients and 14 normal subjects were acquired for training, validating, and testing this method. The proposed network was compared to previously proposed voxel-wise machine learning based approaches for q-space imaging. Network-generated images were used to predict stroke functional outcome measures. RESULTS: The proposed network achieves significant performance advantages compared to previously proposed machine learning approaches, showing significant improvements across image quality metrics. Generating these parameter maps using CNNs also comes with the computational benefits of only needing to generate and train a single network instead of multiple networks for each parameter type. Post-stroke outcome prediction metrics do not appreciably change when using images generated from this proposed technique. Over three test participants, the predicted stroke functional outcome scores were within 1-6% of the clinical evaluations. CONCLUSIONS: Estimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q-space data were improved compared to other state-of-the-art methods providing a 10-fold reduction scan time compared to conventional methods.

QSAR and Docking Studies on Piperidyl-cyclohexylurea Derivatives for Prediction of Selective and Potent Inhibitor of Matriptase.

BACKGROUND: QSAR models as PLS, GFA, and 3D were developed for a series of matriptase inhibitors using 35 piperidyl-cyclohexylurea compounds. The training and test sets were divided into a set of 28 and 8 compounds, respectively and the pki values of each compound were used in the analysis. METHODS: Docking and alignment methodologies were used to develop models in 3D QSAR. The best models among all were selected on the basis of regression statistics as r2, predictive r2 and Friedman Lack of fit measure. Hydrogen donors and rotatable bonds were found to be positively correlated properties for this target. The models were validated and used for the prediction of new compounds. Based on the predictions of 3D-QSAR model, 17 new compounds were prepared and their activities were predicted and compared with the active compound. Prediction of activities was performed for these 18 compounds using consensus results of all models. ADMET was also performed for the best-chosen compound and compared with the known active. RESULTS AND CONCLUSION: The developed model was able to validate the obtained results and can be successfully used to predict new potential and active compounds.

Grandiflorenic acid promotes death of promastigotes via apoptosis-like mechanism and affects amastigotes by increasing total iron bound capacity.

BACKGROUND: American tegumentary leishmaniasis (ATL) is a zoonotic disease caused by protozoa of the genus Leishmania. The high toxicity, high costs and resistance of some strains to current drugs has prompted the search for therapeutic alternatives for the management of this disease. Sphagneticola trilobata is a plant that has diterpenes as main constituents, including grandiflorenic acid (GFA) that has antiinflammatory, antiprotozoal, antibacterial and antinociceptive activity. PURPOSE: The aim of our study was to determine the effect of GFA on both the promastigotes and the amastigotes of Leishmania amazonensis. METHODS: Isolation by chromatographic methods and chemical identification of GFA, then evaluation of the in vitro leishmanicidal activity of this compound against Leishmania amazonensis promastigotes and L. amazonensis infected peritoneal Balb/c macrophages, as well its action and microbicide mechanisms. RESULTS: GFA treatment significantly inhibited the proliferation of promastigotes. This antiproliferative effect was accompanied by morphological changes in the parasite with 25 nM GFA. Afterwards, we investigated the mechanisms involved in the death of the protozoan; there was an increase in the production of reactive oxygen species (ROS), phosphatidylserine exposure, permeabilization of the plasma membrane and decreased mitochondrial depolarization. In addition, we observed that the treatment caused a reduction in the percentage of infected cells and the number of amastigotes per macrophage, without showing cytotoxicity in low doses to peritoneal macrophages and sheep erythrocytes. GFA increased IL-10 and total iron bound to transferrin in infected macrophages. Our results showed that GFA treatment acts on promastigote forms through an apoptosis-like mechanism and on intracellular amastigote forms, dependent of regulatory cytokine IL-10 modulation with increase in total iron bound to transferrin. CONCLUSION: GFA showed in vitro antileishmanial activity on L. amazonensis promastigotes forms and on L. amazonensis-infected macrophages.

Evaluation of the antioxidant properties of curcumin derivatives by genetic function algorithm.

The prevalence of degenerative diseases in recent time has triggered extensive research on their control. This condition could be prevented if the body has an efficient antioxidant mechanism to scavenge the free radicals which are their main causes. Curcumin and its derivatives are widely employed as antioxidants. The free radical scavenging activities of curcumin and its derivatives have been explored in this research by the application of quantitative structure activity relationship (QSAR). The entire data set was optimized at the density functional theory (DFT) level using the Becke's three-parameter Lee-Yang-Parr hybrid functional (B3LYP) in combination with the 6-311G∗ basis set. The training set was subjected to QSAR studies by genetic function algorithm (GFA). Five predictive QSAR models were developed and statistically subjected to both internal and external validations. Also the applicability domain of the developed model was accessed by the leverage approach. Furthermore, the variation inflation factor, (VIF), mean effect (MF) and the degree of contribution (DC) of each descriptor in the resulting model were calculated. The developed models met all the standard requirements for acceptability upon validation with highly impressive results ( R=0.965,R2=0.931,Q2(RCV2)=0.887,Rpred2=0.844,cRp2=0.842s=0.226,rmsep=0.362 ). Based on the results of this research, the most crucial descriptor that influence the free radical scavenge of the curcumins is the nsssN (count of atom-type E-state: >N-) descriptor with DC and MF values of 12.980 and 0.965 respectively.

In-silico prediction of sweetness using structure-activity relationship models.

Quantitative structure activity relationship (QSAR) models appear to be an ideal tool for quick screening of promising candidates from a vast library of molecules, which can then be further designed, synthesized and tested using a combination of rigorous first principle simulations, such as molecular docking, molecular dynamics simulation and experiments. In this study, QSAR models have been built with an extensive dataset of 487 compounds to predict the sweetness potency relative to sucrose (ranging 0.2-220,000). The whole dataset was randomly split into training and test sets in a 70:30 ratio. The models were developed using Genetic Function Approximation (Rtest2 = 0.832) and Artificial Neural Network (Rtest2 = 0.831). Our models thus offer a convenient route for fast screening of molecules prior to synthesis and testing. Additionally, this study can supplement a molecular modelling approach to improve binding of molecules with sweet taste receptors, leading to design of novel sweeteners.

Empirical single sample quantification of bias and variance in Q-ball imaging.

PURPOSE: The bias and variance of high angular resolution diffusion imaging methods have not been thoroughly explored in the literature and may benefit from the simulation extrapolation (SIMEX) and bootstrap techniques to estimate bias and variance of high angular resolution diffusion imaging metrics. METHODS: The SIMEX approach is well established in the statistics literature and uses simulation of increasingly noisy data to extrapolate back to a hypothetical case with no noise. The bias of calculated metrics can then be computed by subtracting the SIMEX estimate from the original pointwise measurement. The SIMEX technique has been studied in the context of diffusion imaging to accurately capture the bias in fractional anisotropy measurements in DTI. Herein, we extend the application of SIMEX and bootstrap approaches to characterize bias and variance in metrics obtained from a Q-ball imaging reconstruction of high angular resolution diffusion imaging data. RESULTS: The results demonstrate that SIMEX and bootstrap approaches provide consistent estimates of the bias and variance of generalized fractional anisotropy, respectively. The RMSE for the generalized fractional anisotropy estimates shows a 7% decrease in white matter and an 8% decrease in gray matter when compared with the observed generalized fractional anisotropy estimates. On average, the bootstrap technique results in SD estimates that are approximately 97% of the true variation in white matter, and 86% in gray matter. CONCLUSION: Both SIMEX and bootstrap methods are flexible, estimate population characteristics based on single scans, and may be extended for bias and variance estimation on a variety of high angular resolution diffusion imaging metrics.