The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uni-exponential {\em vs.} multi-exponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.

Funnels, Pathways and the Energy Landscape of Protein Folding: A Synthesis

Background and purpose:
Accumulated evidence suggests that oxidative stress is involved in amyloid β (Aβ)-induced cognitive dysfunction. Silibinin (silybin), a flavonoid derived from the herb milk thistle (Silybum marianum), has been shown to have antioxidative properties; however, it remains unclear whether silibinin improves Aβ-induced neurotoxicity. In the present study, we examined the effect of silibinin on the memory impairment and accumulation of oxidative stress induced by Aβ25–35 in mice.

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Silibinin prevents amyloid β peptide-induced memory impairment and oxidative stress in mice

The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.

Machine learning techniques and drug design.

Palm oil mill effluent (POME) is generated from the sterilization, condensation and hydrocycloning of palm oil in mills. If the effluent is discharged into the aquatic and terrestrial ecosystem without treatment, it could lead to high biological oxygen demand (BOD), chemical oxygen demand (COD) and acidic pH of the receiving waters. Biogas consisting mostly of methane, carbon dioxide, and to a lesser hydrogen has been produced through anaerobic treatment of this toxic effluent. The process of biogas production involves microbial synthesis involving hydrolysis, acidogenesis, acetogenesis and methanogenesis. Biogas is formed during anaerobic degradation of POME by indigenous microbial communities. This review updates the current state of art of biogas production through anaerobic digestion of POME using different configurations of reactors such as fluidized bed reactor, anaerobic filtration, up-flow anaerobic sludge blanket (UASB) reactor, anaerobic contact digestion, up-flow anaerobic sludge fixed-film (UASFF) reactor, modified anaerobic baffled bioreactor (MABB), anaerobic baffled bioreactor (ABR), continuous stirred tank reactor (CSTR), expanded granular sludge bed (EGSB) reactor, Ultrasonicated membrane anaerobic system (UMAS), Ultrasonic-assisted Membrane Anaerobic System (UAMAS), membrane anaerobic system (MAS)and upflow anaerobic sludge blanket reactor (UASBR). The factors that influences biogas yield during treatment include pH, temperature (environmental factors), organic loading rate (OLR), hydraulic retention time (HRT), mixing rate, pressure, equilibrium, nutrient and microbial activities (Internal factors). Based on this study, UAMAS is the best configuration for methane production from POME during anaerobic treatment. Biogas from POME could contribute to energy sources of oil palm producing nations, while preventing the attendant environmental impacts associated with its disposal.

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A review of biogas production from palm oil mill effluents using different configurations of bioreactors

Despite the high prevalence of neuropsychiatric disorders, their aetiology and molecular mechanisms remain poorly understood. The zebrafish (Danio rerio) is increasingly utilized as a powerful animal model in neuropharmacology research and in vivo drug screening. Collectively, this makes zebrafish a useful tool for drug discovery and the identification of disordered molecular pathways. Here, we discuss zebrafish models of selected human neuropsychiatric disorders and drug-induced phenotypes. As well as covering a broad range of brain disorders (from anxiety and psychoses to neurodegeneration), we also summarize recent developments in zebrafish genetics and small molecule screening, which markedly enhance the disease modelling and the discovery of novel drug targets.

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Zebrafish Models in NeuroPsychopharmacology and CNS Drug Discovery

New psychoactive substances (NPSs) have concerned authorities worldwide, and monitoring them has become increasingly complex. In addition to the frequent emergence of new chemical structures, the composition of adulterants has changed rapidly. Reliable reference data on NPS are not always available, and identifying them has become an operational problem. In this study, we evaluated the infrared spectral data of 68 seized samples suspected of containing a synthetic cathinone (N-ethylpentylone). We used quantum chemistry tools to simulate infrared spectra as a benchmark and obtained infrared spectra for different cathinones, structurally analogous amphetamines, and possible adulterants. We employed these in silico data to construct different chemometric models and investigated the internal and external validation and classification requirements of the models. We applied the best models to predict the classification of the experimental data, which showed that the seized samples did not have a well-defined profile. Infrared spectra alone did not allow N-ethylpentylone to be distinguished from other substances. This study enabled us to evaluate whether experimental, in silico, and applied statistical techniques help to promote forensic analysis for decision-making. The seized samples required in-depth treatment and evaluation so that they could be correctly analyzed for forensic purposes.

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In Silico Infrared Spectroscopy as a Benchmark for Identifying Seized Samples Suspected of Being N-Ethylpentylone

Illicit drugs and their trafficking require worldwide efforts in investigation, detection, and control. Colorimetric tests are often applied to identify drugs. Cocaine has some well-known adulterants that can provide a false positive response. Cucurbit[6]uril (CB[6]) has been suggested as a potential detector for cocaine and other illicit drugs. This work uses in silico methods to evaluate the use of CB[6] to detect cocaine and these interfering substances. More specifically, this work analyzes different possibilities of CB[6] complexation with cocaine, lidocaine, caffeine, and procaine and compares the results achieved for cocaine and its adulterants. Different methodologies were employed: quantum chemistry was investigated through DFT B3LYP/TZVP (density functional theory-Becke, three-parameter, Lee-Yang-Parr with triple zeta valence plus polarization basis set) and the semi-empirical methods Austin model 1 (AM1), parametric methods 3, 6, and 7 (PM3, PM6, PM7), and Recife model 1 (RM1). We used these methodologies intending to compare the reasonability and reproducibility of the results in the gas phase condition. Solvent influence was studied by molecular dynamics (MD) simulations. Results showed that CB[6] does not bind to these substances, as judged from the positive values of binding free energy obtained with all methods. DFT and MD were the most reliable methods whereas semiempirical ones were not reproductible in describing these systems. Results also showed that interactions are not specific, so CB[6] does not provide a good response for cocaine detection.

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In silico Evaluation of Cucurbit[6]uril as a Potential Detector for Cocaine and Its Adulterants Lidocaine, Caffeine, and Procaine

Editorial do Volume 8, número 1.      

http://rbc.org.br/ojs/index.php/rbc/article/download/388/pdf

Procedimentos Periciais em Foco

O direito à vida é inviolável e conta com a proteção penal, de forma que o trabalho pericial é imprescindível à persecução penal e à busca pela justiça. A entrega das informações obtidas nos exames de locais de crime é feita por meio do laudo pericial, que deve ser claro e objetivo quanto aos procedimentos realizados. Por basear-se no método científico, a padronização da metodologia de processamento de locais de crime é fundamental à Criminalística, que deve atuar de maneira uniforme sobre o território nacional. Este trabalho analisou laudos periciais oficiais de natureza criminal de diferentes Unidades Federativas do Brasil em casos de homicídios perpetrados com o uso de arma de fogo. As informações sobre o processamento do local foram avaliadas por meio de quesitos elaborados pelos autores, com base na literatura especializada e no Procedimento Operacional Padrão em Perícia Criminal publicado pelo Ministério da Justiça do Brasil no ano de 2013, com o objetivo de verificar como têm sido realizados os protocolos periciais nas diferentes regiões do país. Os dados foram analisados qualitativamente por meio das respostas obtidas aos quesitos propostos.

Procedimentos periciais realizados em locais de homicídios perpetrados com o uso de arma de fogo no território brasileiro

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Aline Thaís Bruni

Papers

In Silico Infrared Spectroscopy as a Benchmark for Identifying Seized Samples Suspected of Being N-Ethylpentylone

In silico Evaluation of Cucurbit[6]uril as a Potential Detector for Cocaine and Its Adulterants Lidocaine, Caffeine, and Procaine

Procedimentos Periciais em Foco

Procedimentos periciais realizados em locais de homicídios perpetrados com o uso de arma de fogo no território brasileiro

A declaração de Sydney e a conceituação de ciência forense