Kemometričko proučavanje i molekularno modeliranje derivata 1H-indol-3-octene kiseline s auksinskom aktivnošću

Quantitative Structure-Activity Relationship (QSAR) study on 22 1H-indole-3-acetic acid derivatives with auxin activity was performed by means of Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Partial Least Squares Regression (PLS) and Multiple Linear Regression (MLR). Molecular geometry of the auxins was optimized at MMFF94 and ab initio B3LYP 6-31G** levels. Modeling of complexes of some auxin molecules with the auxin binding protein 1 (ABP1) was also carried out. Parsimonius PLS and MLR models for prediction of optimal and half-optimal auxin concentrations for Avena L. Sativa coleoptile elongation were obtained with 15 auxins in the training set. HCA and PCA on data for the half-optimal concentration exhibit auxin clustering with respect to substitutent type and position, biological activity, and the size of the active site pockets of ABP1. Molecular graphics of ABP1 – NAA derivative complexes and of the coordination spheres around NAA (1-naphthalenic acid) hydrogen atoms in the ABP1 – NAA complex agrees well with the chemometrics/QSAR results.Proučavanje kvantitativnih relacija izme|u strukture i auksinske aktivnosti za 22 derivata 1H-indol-3-octene kiseline sprovedeno je pomoću analize glavnih komponenata (PCA), hijerarhijske grozdaste analize (HCA), regresije parcijalnih najmanjih kvadrata (PLS) i višestruke linearne regresije (MLR). Molekularna geometrija auksina optimizirana je na razini MMFF94 i ab initio B3LYP 6-31G** teorije. Izvršeno je i modeliranje kompleksa nekih auksinskih molekula i auksinskoga proteina 1 (ABP1). Zadovoljavajući modeli PLS i MLR za predvi|anje optimalne i polu-optimalne auksinske koncentracije za produljenje koleoptila Avena L. Sativa dobiveni su pomoću test-skupova s 15 auksina. Analize podataka HCA i PCA za polu-optimalnu koncentraciju klasificirale su auksine s obzirom na vrstu i mjesto supstitucije, biološku aktivnost i veličinu d`epova u aktivnom mjestu proteina ABP1. Molekularna grafika kompleksa ABP1–NAA derivati kao i koordinacijskih sfera oko vodikovih atoma u NAA (1-naftalenska kiselina) dobro se sla`e s kemometričkim i QSAR rezultatima

A Priori Descriptors in QSAR: a Case of Gram-Negative Bacterial Multidrug Resistance to ß-Lactams

Over hundred new a priori global/local molecular descriptors that encoded steric, topological, electronic, hydrogen bonding, compositional and hydrophobic properties were generated for 16 ß-lactams, and two partial least squares regression models were constructed and cross-validated. These a priori models (Q2 > 0.80, R2 > 0.95, SEV < 0.50) are comparable with the previously obtained computed models. ß-Lactam intramolecular and ß-lactam-receptor intermolecular interactions are also discussed in terms of molecular descriptors

Determination of cellulose crystallinity of banana residues using near infrared spectroscopy and multivariate analysis

Crystallinity is an important property of lignocellulosic biomass due to its significant effect on acid/enzymatic hydrolysis. Normally, physicochemical analysis, such as powder X-ray diffraction and nuclear magnetic resonance, is used to reveal the crystallinity content. However, these analytical methods are expensive and laborious. In this context, methods that rapidly predict the crystallinity are important, even if used only for screening calibration. Thus, we intend to show the potential of near-infrared spectroscopy (NIRS) and chemometrics to replace reference methods in crystallinity determination. The results show that NIRS can be used to determine crystallinity in banana residues by the use of partial least squares regression, providing good coefficients of determination (R2cal,pred > 0.82), low relative errors (< 14%) and good range error ratio (≥ 7.7). The interpretation of the regression coefficients, multivariate figures of merit and external validation results indicate a strong relationship between the NIR spectrum and crystallinity in banana samples26714911499FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPSem informaçã

Basic validation procedures for regression models in QSAR and QSPR studies: theory and application

Four quantitative structure-activity relationships (QSAR) and quantitative structure-property relationship (QSPR) data sets were selected from the literature and used to build regression models with 75, 56, 50 and 15 training samples. The models were validated by leave-one-out crossvalidation, leave-N-out crossvalidation (LNO), external validation, y-randomization and bootstrapping. Validations have shown that the size of the training sets is the crucial factor in determining model performance, which deteriorates as the data set becomes smaller. Models from very small data sets suffer from the impossibility of being thoroughly validated, failure and atypical behavior in certain validations (chance correlation, lack of robustness to resampling and LNO), regardless of their good performance in leave-one-out crossvalidation, fitting and even in external validation. A simple determination of the critical Nin LNO has been introduced by using the limit of 0.1 for oscillations in Q², quantified as the variation range in single LNO and two standard deviations in multiple LNO. It has been demonstrated that it is sufficient to perform 10 -25 y-randomization and bootstrap runs for a typical model validation. The bootstrap schemes based on hierarchical cluster analysis give more reliable and reasonable results than bootstraps relying only on randomization of the complete data set. Data quality in terms of statistical significance of descriptor -yrelationships is the second important factor for model performance.Variable selection that does not eliminate insignificant descriptor - yrelationships may lead to situations in which they are not detected during model validation, especially when dealing with large data sets

Molecular graphics-structural and molecular graphics descriptors in a QSAR study of 17-alpha-acetoxyprogesterones

Quantitative Structure-Activity Relationship study on 21 oral progestogens, 19 of which are 17-alpha-acetoxyprogesterones, was performed by using Partial Least Squares. Fairly good regression models were achieved, the best being Q²=0.707, R²=0.811 with two Principal Components and four descriptors. Most of the molecular descriptors were generated from molecular graphics of DFT 6-31G** optimized geometries (molecular graphics descriptors) or were additionally combined with experimental structural parameters of progesterone receptor - progesterone complex (molecular graphics-structural, or molecular graphics and modeling descriptors). Regression models employing only these molecular graphics-based descriptors reached Q²=0.556, R²=0.718 with three Principal Components and five descriptors, demonstrating their usefulness in QSAR studies. In the case of progesterone derivatives, molecular graphics descriptors successfully included various conformational, steric and electronic substituent effects.Neste trabalho, foi feito um estudo de relações quantitativas entre a estrutura e a atividade biológica de 21 derivados de progesteronas ministrados via oral, dentre os quais 19 são 17-alfa-acetoxiprogesteronas. O método de quadrados mínimos parciais foi usado para construir modelos de regressão de boa qualidade, com Q² = 0,707 e R² = 0,811 utilizando duas componentes principais e quatro descritores. A maioria dos descritores moleculares foi obtida a partir de gráficos moleculares das geometrias otimizadas por meio de cálculos ab initio com um conjunto de base DFT 6-31G** (descritores moleculares gráficos). Os outros descritores foram obtidos pela combinação dos descritores anteriores com parâmetros estruturais experimentais extraídos do complexo progesterona-receptor da progesterona (descritores gráfico-estruturais ou descritores gráficos e de modelagem). Os modelos de regressão empregando somente cinco descritores gráficos e três componentes principais foram satisfatórios, Q²=0,556, R²=0,718, demonstrando a utilidade dos mesmos em estudos QSAR. Neste trabalho, onde foram estudados derivados de progesterona, ficou evidente que os descritores moleculares gráficos descreveram com sucesso os efeitos conformacionais, estéreos e eletrônicos dos diferentes substituintes.2026Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Coupling a bioelectrochemical cell with a redox flow battery for sustainable energy production and storage

Book of Abstracts of CEB Annual Meeting 2017[Excerpt] Bioelectrochemical systems (BES) are devices capable to convert chemical energy into electricity through the degradation of different organic compounds using electroactive bacteria as biocatalyst. The ability of microorganisms to form biofilms in electrode surfaces allows the transport of electrons, resultant from the oxidation of carbon sources, to an terminal electron acceptor [1]. Redox flow batteries (RFB) are electrochemical systems applied in the conversion and storage of chemical energy in electricity. Redox chemical species (in soluble form) are the main responsible for the energy storage [2]. Quinones are electroactive molecules applied in RFB because of their chemical and physical properties. The aim of this work is to develop an innovative technology to generate and storage the energy resultant from BES. The strategy outlined is coupling a BES with a RFB that present potential to combine bioenergy production and storage in a microbially charged redox flow battery. [...]info:eu-repo/semantics/publishedVersio

Is the plant Bolboschoenus maritimus an adequate biomonitor for trace metal contamination in saltmarshes? A field study from the Óbidos lagoon (Portugal)

Monitoring the negative impacts of trace metals is crucial to assess the health and stability of ecosystems. In salt marshes, halophyte plants were reported as possible bioaccumulators of these elements. The aim of this work was to explore the bioaccumulation potential of Bolboschoenus maritimus as a tool for monitoring the presence of metals in coastal environments. Bolboschoenus maritimus were collected from a brackish water lagoon, and the presence of the trace metals lead, cadmium, and nickel were seasonally evaluated in distinct parts of the plants, and in water and sediment samples. Lead was the trace metal with the highest concentration detected in water and sediments of the sampling site. The highest lead concentrations in B. maritimus were recorded in the spring season. The transport index indicated an accumulation of lead in the leaves of around 70% in the spring of 2009. Cadmium in leaves in spring and summer of 2009 reached values above 5 mg Cd. kg−1. Nickel was not detected in most samples collected. Bolboschoenus maritimus was considered an adequate biomonitor for lead and cadmium, since it bioaccumulates both metals with seasonally distinct results, as the bioaccumulation factor results indicated.info:eu-repo/semantics/publishedVersio

A study of octanol/water partition coefficient of polychlorinated biphenyls (PCBs) using topological indices

A model based on chemical structure was developed for the accurate prediction of octanol/water partition coefficient (K OW) of polychlorinated biphenyls (PCBs), which are molecules of environmental interest. Partial least squares (PLS) was used to build the regression model. Topological indices were used as molecular descriptors. Variable selection was performed by Hierarchical Cluster Analysis (HCA). In the modeling process, the experimental K OW measured for 30 PCBs by thin-layer chromatography - retention time (TLC-RT) has been used. The developed model (Q² = 0,990 and r² = 0,994) was used to estimate the log K OW values for the 179 PCB congeners whose K OW data have not yet been measured by TLC-RT method. The results showed that topological indices can be very useful to predict the K OW.312318Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br

QSAR modeling: a new open source computational package to generate and validate QSAR models

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.554560Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq