An improved adaptive genetic algorithm for protein–ligand docking

A new optimization model of molecular docking is proposed, and a fast flexible docking method based on an improved adaptive genetic algorithm is developed in this paper. The algorithm takes some advanced techniques, such as multi-population genetic strategy, entropy-based searching technique with self-adaptation and the quasi-exact penalty. A new iteration scheme in conjunction with above techniques is employed to speed up the optimization process and to ensure very rapid and steady convergence. The docking accuracy and efficiency of the method are evaluated by docking results from GOLD test data set, which contains 134 protein-ligand complexes. In over 66.2% of the complexes, the docked pose was within 2.0 A root-mean-square deviation (RMSD) of the X-ray structure. Docking time is approximately in proportion to the number of the rotatable bonds of ligands.     

A novel method for predicting decomposition onset temperature of high-energy metal–organic frameworks

Journal of Thermal Analysis and Calorimetry - The decomposition onset temperature, Tdecom, is an important parameter for investigating the thermal stability of chemicals. A novel method is...     

An algorithm for depth–dose curves of electrons fitted to Monte Carlo data

An accurate algorithm has been developed for the depth profiles of energy deposition by plane-parallel electron beams normally incident on semi-infinite absorbers. The energies of electrons considered are from 0.1 to 20 MeV, and the absorbers considered are elemental materials of atomic numbers between 4 and 92. Adjustable coefficients in the algorithm have been determined so as to minimize its deviations from the Monte Carlo data published by Tabata et al. (1994). The algorithm is also applicable to light compounds and mixtures.     

Assessment of the Van Voorhis–Scuseria exchange–correlation functional for predicting excitation energies using time-dependent density functional theory

We assess the performance of the Van Voorhis–Scuseria exchange–correlation functional (VSXC), a kinetic-energy-density-dependent exchange–correlation functional recently developed in our group, for calculating vertical excitation energies using time-dependent density functional theory in a benchmark set of molecules. Overall, VSXC performs very well, with accuracy similar to that of hybrid functionals such as the hybrid Perdew–Burke–Ernzerhof functional and Becke's three parameter hybrid method with the Lee, Yang, and Parr correlation functional, which contain a portion of Hartree–Fock exchange. Received: 29 December 1999 / Accepted: 5 June 2000 / Published online: 11 September 2000     

Liquid–Liquid Equilibrium Data for 1-Ethyl-3-methylimidazolium Acetate–Thiophene–Diesel Compound: Experiments and Correlations

Extraction of thiophene from cyclohexane, isooctane and toluene were performed using the ionic liquid 1-ethyl-3-methylimidazolium acetate ([EMIM][OAc]) at T=298.15 K. The liquid?Cliquid equilibrium (LLE) experiments were performed on three systems, namely: [EMIM][OAc]?Cthiophene?Ccyclohexane, [EMIM][OAc]?Cthiophene?Cisooctane and [EMIM][OAc]?Cthiophene?Ctoluene. The LLE data showed that [EMIM][OAc] has a higher selectivity at low concentration of thiophene and also showed that the hydrocarbon-rich phase is free of ionic liquid. This implies that there will be no cross contamination and the ionic liquid will be a non-pollutant in fuel after extraction. Further, the amount of hydrocarbon in the ionic-liquid-rich phase is very small. This implies that ionic liquid can be regenerated with negligible loss of fuel. LLE data was then correlated using the NRTL and UNIQUAC models. These showed root mean square deviation (RMSD) values of 0.82?% and 1.46?% for the isooctane system, 1.37?% and 1.57?% for the cyclohexane system and 1.39?% and 1.53?% for the toluene system.     

PepPro: A Nonredundant Structure Data Set for Benchmarking Peptide–Protein Computational Docking

We present a nonredundant benchmark, coined PepPro, for testing peptide–protein docking algorithms. Currently, PepPro contains 89 nonredundant experimentally determined peptide–protein complex structures, with peptide sequence lengths ranging from 5 to 30 amino acids. The benchmark covers peptides with distinct secondary structures, including helix, partial helix, a mixture of helix and β-sheet, β-sheet formed through binding, β-sheet formed through self-folding, and coil. In addition, unbound proteins' structures are provided for 58 complexes and can be used for testing the ability of a docking algorithm handling the conformational changes of proteins during the binding process. PepPro should benefit the docking community for the development and improvement of peptide docking algorithms. The benchmark is available at http://zoulab.dalton.missouri.edu/PepPro_benchmark . © 2019 Wiley Periodicals, Inc.     

LC–MS–MS Analysis of Mirtazapine in Plasma, and Determination of Pharmacokinetic Data for Rats

A sensitive LC–MS–MS method with electrospray ionization has been developed for analysis of mirtazapine in rat plasma. After addition of diazepam as internal standard, liquid–liquid extraction was used to produce a protein-free extract. Chromatographic separation was achieved on a 150 × 4.6 mm, 5 μm particle, ODS column with 84:16 (v/v) methanol–water containing 0.1% ammonium acetate and 0.01% glacial acetic acid as mobile phase. LC–MS–MS was performed in selected-ion-monitoring (SIM) mode using target fragment ions m/z 195.09 for mirtazapine and m/z 192.80 for the IS. Calibration plots were linear over the range of 0.516–618.8 ng mL^?1. The lower limit of quantification was 0.516 ng mL^?1. Intra-day and inter-day precision were better than 12.6 and 8.8%, respectively. Mean recovery of mirtazapine from plasma was in the range 87.41–90.06%; average recovery was 88.40% (RSD 3.95%). Significant gender differences between mirtazapine pharmacokinetic data were observed in this study.     

A velocity program using the Kanade–Lucas–Tomasi feature-tracking algorithm with demonstration for pressure and electroosmosis conditions

Investigating microfluidic flow profiles is of interest in the microfluidics field for the determination of various characteristics of a lab-on-a-chip system. Microparticle tracking velocimetry uses computational methods upon recording video footage of microfluidic flow to ultimately visualize motion within a microfluidic system across all frames of a video. Current methods are computationally expensive or require extensive instrumentation. A computational method suited to microparticle tracking applications is the robust Kanade–Lucas–Tomasi (KLT) feature-tracking algorithm. This work explores a microparticle tracking velocimetry program using the KLT feature-tracking algorithm. The developed program is demonstrated using pressure-driven and EOF and compared with the respective mathematical fluid flow models. An electrostatics analysis of EOF conditions is performed in the development of the mathematical using a Poisson's Equation solver. This analysis is used to quantify the zeta potential of the electroosmotic system. Overall, the KLT feature-tracking algorithm presented in this work proved to be highly reliable and computationally efficient for investigations of pressure-driven and EOF in a microfluidic system.     

Two-dimensional subband Steiglitz–McBride algorithm for automatic analysis of two-dimensional nuclear magnetic resonance data

Rapid, accurate, and automatic quantitation of two-dimensional nuclear magnetic resonance(2D-NMR) data is a challenging problem. Recently, a Bayesian information criterion based subband Steiglitz–McBride algorithm has been shown to exhibit superior performance on all three fronts when applied to the quantitation of one-dimensional NMR free induction decay data. In this paper, we demonstrate that the 2D Steiglitz–McBride algorithm, in conjunction with 2D subband decomposition and the 2D Bayesian information criterion, also achieves excellent results for 2D-NMR data in terms of speed, accuracy, and automation—especially when compared in these respects to the previously published analysis techniques for 2D-NMR data.     

Qualitative and quantitative structure–activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals

In this study, structure–activity relationship (SAR) models have been established for qualitative and quantitative prediction of the blood–brain barrier (BBB) permeability of chemicals. The structural diversity of the chemicals and nonlinear structure in the data were tested. The predictive and generalization ability of the developed SAR models were tested through internal and external validation procedures. In complete data, the QSAR models rendered ternary classification accuracy of >98.15%, while the quantitative SAR models yielded correlation (r²) of >0.926 between the measured and the predicted BBB permeability values with the mean squared error (MSE) <0.045. The proposed models were also applied to an external new in vitro data and yielded classification accuracy of >82.7% and r² > 0.905 (MSE < 0.019). The sensitivity analysis revealed that topological polar surface area (TPSA) has the highest effect in qualitative and quantitative models for predicting the BBB permeability of chemicals. Moreover, these models showed predictive performance superior to those reported earlier in the literature. This demonstrates the appropriateness of the developed SAR models to reliably predict the BBB permeability of new chemicals, which can be used for initial screening of the molecules in the drug development process.     

TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models

Drug–target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug–drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user’s molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75–100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug–drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.     

Novel algorithm for simultaneous component detection and pseudo-molecular ion characterization in liquid chromatography–mass spectrometry

Resolving components and determining their pseudo-molecular ions (PMIs) are crucial steps in identifying complex herbal mixtures by liquid chromatography–mass spectrometry. To tackle such labor-intensive steps, we present here a novel algorithm for simultaneous detection of components and their PMIs. Our method consists of three steps: (1) obtaining a simplified dataset containing only mono-isotopic masses by removal of background noise and isotopic cluster ions based on the isotopic distribution model derived from all the reported natural compounds in dictionary of natural products; (2) stepwise resolving and removing all features of the highest abundant component from current simplified dataset and calculating PMI of each component according to an adduct-ion model, in which all non-fragment ions in a mass spectrum are considered as PMI plus one or several neutral species; (3) visual classification of detected components by principal component analysis (PCA) to exclude possible non-natural compounds (such as pharmaceutical excipients). This algorithm has been successfully applied to a standard mixture and three herbal extract/preparations. It indicated that our algorithm could detect components’ features as a whole and report their PMI with an accuracy of more than 98%. Furthermore, components originated from excipients/contaminants could be easily separated from those natural components in the bi-plots of PCA.     

Biomarkers for Alzheimer's Disease – An Overview

Alzheimer's disease (AD) is a complex neurodegenerative disorder with a significant global impact on public health. The emergence of atypical clinical phenotypes challenges traditional diagnostic approaches, necessitating a deeper exploration of biomarkers for accurate identification. The US Food and Drug Administration (FDA) classification of biomarkers and their integration into different stages of AD provide a structured framework for their application in research and clinical settings. Within the context of AD drug development, biomarkers are essential for participant selection, target engagement evaluation, and assessment of pathological hallmarks, including Aβ and tau protein abnormalities. The incorporation of nanoparticles with a biodegradable approach introduces innovative strategies to address the complexities of AD. This paper extensively discusses biomarkers associated with synaptic dysfunction, neuroinflammation, and glial activation, recognizing their significance in elucidating disease mechanisms. Common pathologies such as synuclein and TDP-43 further underscore the multifaceted nature of AD. Current biomarkers for AD diagnosis, encompassing cerebral spinal fluid (CSF) biomarkers and various imaging modalities, reflect the ongoing efforts to enhance early detection and monitoring. Intriguingly, novel biomarkers continue to emerge, offering promising avenues for improved understanding and intervention. Current review provides a comprehensive survey of biomarkers for AD, elucidating their diverse roles across different aspects of the disease. By highlighting their contributions to diagnosis, drug development, and mechanistic insights, this overview underscores the importance of biomarker research in the pursuit of effective AD management and treatment strategies.     

An alternative quadrilinear decomposition algorithm for four-way calibration with application to analysis of four-way fluorescence excitation–emission–pH data array

A novel quadrilinear decomposition algorithm for four-way calibration (third-order tensor calibration), which was called as regularized self-weighted alternating quadrilinear decomposition (RSWAQLD), has been developed in this work. It originates from the alternating trilinear decomposition (ATLD) algorithm, inherits the philosophy behind self-weighting operation from the self-weighted alternating trilinear decomposition (SWATLD) algorithm. The RSWAQLD algorithm is based on a nearby least-squares scheme, in which two extra terms are added to each loss function, making it more stable and flexible. Experiment shows that RSWAQLD has the features of fast convergence and being insensitive to the excess estimated factors in the model. Owing to its unique optimizing approach, RSWAQLD is much more efficient than four-way PARAFAC. Moreover, the performance of RSWAQLD is quit stable as the number of factors used in calculation varies (as long as it is no less than the true number of factors). Such a feature will simplify the analysis of four-way data arrays, since it is unnecessary to spend a lot of time and effort on accurately determining the appropriate number of factors in the matrix. In addition, the result of four-way fluorescence excitation–emission–pH data, as well as that of simulated data, illustrated that RSWAQLD can not only remain the “higher-order advantage” but also provide a satisfying result even in high collinear systems.     

Ranking and similarity for quantitative structure–retention relationship models in predicting Lee retention indices of polycyclic aromatic hydrocarbons

Quantitative structure–(chromatographic) retention relationship (QSRR) models for prediction of Lee retention indices for polycyclic aromatic hydrocarbons (PAHs) were gathered from the literature and the predictive performances of models were compared. Numerous Lee retention indices (46) were served as a reliable basis for ranking by a recently developed novel method of ordering based on the sum of ranking differences (SRD) [TrAC, Trends Anal. Chem. 29 (2010) 101–109], by which the best model can be selected easily. Two kinds of references for ranking were accepted, average (consensus) and the experimental retention indices. Leave-many-out cross validation of the SRD procedure provides an easy way to group similar models. Significant differences among models can be revealed by using Wilcoxon's matched pair test.     

Customization of the CEFM for predicting stress corrosion cracking in lightly sensitized Al–Mg alloys in marine applications

The Coupled Environment Fracture Model (CEFM) has been modified and calibrated to predict crack growth rate (CGR) in aluminum marine alloys. The customized CEFM provided quantitative predictions of the effects of O₂, electrochemical potential, stress intensity factor, and conductivity on CGR in lightly sensitized AA5083-H321 in 3.5 wt.% NaCl solution, as well as explaining the development of a semi-elliptical surface cracks. The importance of the properties of the external environment, such as conductivity, oxidant/reductant concentration, and the kinetics of the cathodic reactions on the surfaces external to the crack has been confirmed. Crack growth is attributed to a sequence of microfracture events at the crack front, the frequency of which is determined by the mechanical conditions that exist at the crack tip, as governed by the stress intensity while the microfracture dimension is determined by hydrogen-induced fracture, with the CGR being the product of these two quantities. The success in explaining the intergranular stress corrosion cracking (IGSCC) of aluminum alloys, argues that the basic concept of the CEFM, that the internal and external environments are strongly coupled, is sound and that the CEFM, which was originally developed to describe IGSCC in sensitized stainless steels is equally applicable for describing IGSCC in lightly sensitized aluminum alloys.     

The Choice of Polar Stationary Phases for Gas–Liquid Chromatography by Statistical Analysis of Retention Data

We have studied the retention indices of 127 volatile substances on a C₇₈-paraffin and on its seven nearly isochor and isomorphous polar derivatives over a temperature range of 90–210 °C. The retention index of a substance on the C₇₈ paraffin has been considered as the standard. The additional retention on the polar derivative was given by the difference of its retention index on the polar solvent and on the C₇₈-paraffin. Statistical analyses of the additional retention have shown that with respect to retention, the seven polar solvents can be classified into three groups: Type I: TTF (tetrakistrifluoromethyl), MTF (monotrifluoromethyl), Type II: PCN (primary cyano), PSH (primary thiol) and Type III: TMO (tetramethoxy), SOH (secondary alcohol) and POH (primary alcohol). It is shown that these three types are best represented by the solvents TTF, PCN and TMO. PSH (primary thiol) is aligned with PCN at temperatures up to about 150 °C, but is similar to TMO at 210 °C.     

Use of normal boiling point correlations for predicting critical parameters of paraffins for vapour–liquid equilibrium calculations with the SRK equation of state

Correlations between normal boiling points and critical parameters (critical temperatures and critical pressures) and between normal boiling points and acentric factors of normal and branched paraffins in the range from C₇ to C₁₀₀ have been developed. These correlations can be used to quickly and easily compute critical properties that allow to reproduce closely, by means of the Soave–Redlich–Kwong (SRK) equation of state (EoS), the vapour pressure of pure compounds. In the range of 0.5–5 mmHg the values of vapour pressure calculated by means of the SRK EoS become less accurate and they can be improved using a different equation for the temperature-depending attractive parameter (i.e., the Mathias–Copeman alpha function) instead of the classical Soave function.