Docking and molecular dynamics simulation of quinone compounds with trypanocidal activity

In this work, two different docking programs were used, AutoDock and FlexX, which use different types of scoring functions and searching methods. The docking poses of all quinone compounds studied stayed in the same region in the trypanothione reductase. This region is a hydrophobic pocket near to Phe396, Pro398 and Leu399 amino acid residues. The compounds studied displays a higher affinity in trypanothione reductase (TR) than glutathione reductase (GR), since only two out of 28 quinone compounds presented more favorable docking energy in the site of human enzyme. The interaction of quinone compounds with the TR enzyme is in agreement with other studies, which showed different binding sites from the ones formed by cysteines 52 and 58. To verify the results obtained by docking, we carried out a molecular dynamics simulation with the compounds that presented the highest and lowest docking energies. The results showed that the root mean square deviation (RMSD) between the initial and final pose were very small. In addition, the hydrogen bond pattern was conserved along the simulation. In the parasite enzyme, the amino acid residues Leu399, Met400 and Lys402 are replaced in the human enzyme by Met406, Tyr407 and Ala409, respectively. In view of the fact that Leu399 is an amino acid of the Z site, this difference could be explored to design selective inhibitors of TR. Docking and molecular dynamics simulation of genuine compounds with trypanocidal activity     

Improvement of trypanocidal metabolites production by Aspergillus fumigatus using neural networks

An optimization procedure using artificial neural networks was developed to determine the optimal combination of parameters, such as medium culture, initial pH, temperature and time of fermentation for maximal trypanocidal metabolites production by Aspergillus fumigatus. A data set of 81 experiments was carried out and an artificial neural network was trained to identify the optimal conditions for this process. Good correlation was obtained between the experimental and predicted values of lysis of the trypomastigote forms of Trypanosoma cruzi (r2 = 0.9990). The simulations of fermentation performance were undertaken on combinations of input variables and the highest level of activity against T. cruzi was obtained from the chloroform extract of the modified Jackson medium culture, initial pH of 6.0, incubated at 40 degrees C for 144 h. It displayed lysis of 95% of the trypomastigote forms of T. cruzi and the red blood cells remained normal.     

A diterpene quinone from the bark of Cryptomeria japonica

A diterpene, cryptoquinone, was isolated from the bark of Cryptomeria japonica, the structure, 7,11,14-trioxoabieta-8,12-diene, was established by spectral analyses and X-ray crystallography. This diterpene quinone showed moderate antifungal activities against Pyricularia orizae and Alternaria alternata, and cytotoxic activity against mouse lymphoid neoplasm (P388) cells with an IC(50) of 0.26 microg/ml.     

Trypanocidal activity and selectivity in vitro of aromatic amidine compounds upon bloodstream and intracellular forms of Trypanosoma cruzi

Trypanosoma cruzi is the etiological agent of Chagas disease, an important neglected illness affecting about 12–14 million people in endemic areas of Latin America. The chemotherapy of Chagas disease is quite unsatisfactory mainly due to its poor efficacy especially during the later chronic phase and the considerable well-known side effects. These facts emphasize the need to search for find new drugs. Diamidines and related compounds are minor groove binders of DNA at AT-rich sites and present excellent anti-trypanosomal activity. In the present study, six novel aromatic amidine compounds (arylimidamides and diamidines) were tested in vitro to determine activity against the infective and intracellular stages of T. cruzi, which are responsible for sustaining the infection in the mammalian hosts. In addition, their selectivity and toxicity towards primary cultures of cardiomyocyte were evaluated since these cells represent important targets of infection and inflammation in vivo. The aromatic amidines were active against T. cruzi in vitro, the arylimidamide DB1470 was the most effective compound presenting a submicromolar LD₅₀ values, good selectivity index, and good activity at 4 °C in the presence of blood constituents. Our results further justify trypanocidal screening assays with these classes of compounds both in vitro and in vivo in experimental models of T. cruzi infection.     

Synthesis of 5-enamine-4-thiazolidinone derivatives with trypanocidal and anticancer activity

A series of novel 2-(5-aminomethylene-4-oxo-2-thioxothiazolidin-3-yl)-3-phenylpropionic acid ethyl esters has been synthesized. Target compounds were evaluated for their trypanocidal activity towards Trypanosoma brucei brucei and Trypanosoma brucei gambiense. Several hit-compounds (8, 10, 12) inhibited growth of the parasites at sub-micromolar concentrations (IC₅₀ 0.027–1.936 µM) and showed significant selectivity indices (SI = 108–1396.2) being non-toxic towards the human primary fibroblasts. The screening of anticancer activity in vitro within NCI DTP protocol allowed to identify active 2-(5-{[5-(2,4-dichlorobenzyl)-thiazol-2-ylamino]-methylene}-4-oxo-2-thioxothiazolidin-3-yl)-3-phenylpropionic acid ethyl ester 14 that demonstrated inhibition against all 59 human tumor cell lines with the average GI₅₀ value of 2.57 μM. It was established that the activity type (antitrypanosomal or anticancer) as well as its level depends on the character of enamine fragment in the C5 position of thiazolidinone core.     

A novel approach to the recognition of protein architecture from sequence using Fourier analysis and neural networks

A novel method is presented for the prediction of protein architecture from sequence using neural networks. The method involves the preprocessing of protein sequence data by numerically encoding it and then applying a Fourier transform. The encoded and transformed data are then used to train a neural network to recognize a number of different protein architectures. The method proved significantly better than comparable alternative strategies such as percentage dipeptide frequency, but is still limited by the size of the data set and the input demands of a neural network. Its main potential is as a complement to existing fold recognition techniques, with its ability to identify global symmetries within protein structures its greatest strength.     

A circadian rhythm in neural activity can be recorded from the central nervous system of the cockroach

Summary Evidence presented in this paper indicates that a robust circadian rhythm in the frequency of neural activity can be recorded from the central nervous system of intact cockroaches, Leucophaea maderae. This rhythmicity was abolished by optic lobe removal. Spontaneous neural activity was then used as an assay to demonstrate that the optic lobe is able to generate circadian oscillations in vitro. These results provide direct evidence that the cockroach optic lobe is a self-sustained circadian oscillator capable of generating daily rhythms in the absence of neural or hormonal communications with the rest of the organism.Abbreviations CNS central nervous system - DD constant dark - LD light/dark cycle - SCN suprachiasmatic nucleus - ZT Zeitgeber time     

Spike-time reliability of layered neural oscillator networks

We study the reliability of layered networks of coupled “type I” neural oscillators in response to fluctuating input signals. Reliability means that a signal elicits essentially identical responses upon repeated presentations, regardless of the network’s initial condition. We study reliability on two distinct scales: neuronal reliability, which concerns the repeatability of spike times of individual neurons embedded within a network, and pooled-response reliability, which concerns the repeatability of total synaptic outputs from a subpopulation of the neurons in a network. We find that neuronal reliability depends strongly both on the overall architecture of a network, such as whether it is arranged into one or two layers, and on the strengths of the synaptic connections. Specifically, for the type of single-neuron dynamics and coupling considered, single-layer networks are found to be very reliable, while two-layer networks lose their reliability with the introduction of even a small amount of feedback. As expected, pooled responses for large enough populations become more reliable, even when individual neurons are not. We also study the effects of noise on reliability, and find that noise that affects all neurons similarly has much greater impact on reliability than noise that affects each neuron differently. Qualitative explanations are proposed for the phenomena observed.

Induction and propagation of transient synchronous activity in neural networks endowed with short-term plasticity

Transient, task related synchronous activity within neural populations has been recognized as the substrate of temporal coding in the brain. The mechanisms underlying inducing and propagation of transient synchronous activity are still unknown, and we propose that short-term plasticity (STP) of neural circuits may serve as a supplemental mechanism therein. By computational modeling, we showed that short-term facilitation greatly increases the reactivation rate of population spikes and decreases the latency of response to reactivation stimuli in local recurrent neural networks. Meanwhile, the timing of population spike reactivation is controlled by the memory effect of STP, and it is mediated primarily by the facilitation time constant. Furthermore, we demonstrated that synaptic facilitation dramatically enhances synchrony propagation in feedforward neural networks and that response timing mediated by synaptic facilitation offers a scheme for information routing. In addition, we verified that synaptic strengthening of intralayer or interlayer coupling enhances synchrony propagation, and we verified that other factors such as the delay of synaptic transmission and the mode of synaptic connectivity are also involved in regulating synchronous activity propagation. Overall, our results highlight the functional role of STP in regulating the inducing and propagation of transient synchronous activity, and they may inspire testable hypotheses for future experimental studies.     

Predicting secondary structures of membrane proteins with neural networks

Back-propagation, feed-forward neural networks are used to predict the secondary structures of membrane proteins whose structures are known to atomic resolution. These networks are trained on globular proteins and can predict globular protein structures having no homology to those of the training set with correlation coefficients (C) of 0.45, 0.32 and 0.43 for a-helix, -strand and random coil structures, respectively. When tested on membrane proteins, neural networks trained on globular proteins do, on average, correctly predict (Q_i) 62%, 38% and 69% of the residues in the -helix, -strand and random coil structures. These scores rank higher than those obtained with the currently used statistical methods and are comparable to those obtained with the joint approaches tested so far on membrane proteins. The lower success score for -strand as compared to the other structures suggests that the sample of -strand patterns contained in the training set is less representative than those of a-helix and random coil. Our analysis, which includes the effects of the network parameters and of the structural composition of the training set on the prediction, shows that regular patterns of secondary structures can be successfully extrapolated from globular to membrane proteins. Correspondence to: R. Casadio     

A linear model for characterization of synchronization frequencies of neural networks

The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.     

The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with a typical Mediterranean climate

Biological particles in the air such as pollen grains can cause environmental problems in the allergic population. Medical studies report that a prior knowledge of pollen season severity can be useful in the management of pollen-related diseases. The aim of this work was to forecast the severity of the Poaceae pollen season by using weather parameters prior to the pollen season. To carry out the study a historical database of 21 years of pollen and meteorological data was used. First, the years were grouped into classes by using cluster analysis. As a result of the grouping, the 21 years were divided into 3 classes according to their potential allergenic load. Pre-season meteorological variables were used, as well as a series of characteristics related to the pollen season. When considering pre-season meteorological variables, winter variables were separated from early spring variables due to the nature of the Mediterranean climate. Second, a neural network model as well as a discriminant linear analysis were built to forecast Poaceae pollen season severity, according to the three classes previously defined. The neural network yielded better results than linear models. In conclusion, neural network models could have a high applicability in the area of prevention, as the allergenic potential of a year can be determined with a high degree of reliability, based on a series of meteorological values accumulated prior to the pollen season.     

自组织特征人工神经网络在庞泉沟自然保护区植物群落分类中的应用

人工神经网络是较新的数学分析工具,其中的自组织特征映射网络（SOFM）具有较强的聚类功能。应用SOFM网络对庞泉沟自然保护区植物群落进行了分类研究。在讨论了SOFM网络的数学原理、聚类方法和步骤的前提下,分类过程在MATLAB（6.5）神经网络工具箱（NNTool）中编程实现。结果将89个样方分为13个植物群落类型。分类结果符合植被实际,生态意义明确,表明SOFM网络可以很好地反映植物群落的生态关系,是非常有效的植物群落数量分类方法。     

Application of self-organizing neural networks to classification of plant communities in Pangquangou Nature Reserve, North China

Vegetation classification is an important topic in plant ecology and many quantitative techniques for classification have been developed in the field. The artificial neural network is a comparatively new tool for data analysis. The self-organizing feature map (SOFM) is powerful tool for clustering analysis. SOFM has been applied to many research fields and it was applied to the classification of plant communities in the Pangquangou Nature Reserve in the present work. Pangquangou Nature Reserve, located at 37°20′–38°20′ N, 110°18′–111°18′ E, is a part of the Luliang Mountain range. Eighty-nine samples (quadrats) of 10m × 10m for forest, 4m × 4 m for shrubland and 1m × 1m for grassland along an elevation gradient, were set up and species data was recorded in each sample. After discussion of the mathematical algorism, clustering technique and the procedure of SOFM, the classification was carried out by using NNTool box in MATLAB (6.5). As a result, the 89 samples were clustered into 13 groups representing 13 types of plant communities. The characteristics of each community were described. The result of SOFM classification was identical to the result of fuzzy c-mean clustering and consistent with the distribution patterns of vegetation in the study area and shows significant ecological meanings. This suggests that SOFM may clearly describe the ecological relationships between plant communities and it is a very effective quantitative technique in plant ecology research. __________ Translated from Acta Ecologica Sinica, 2007, 27(3): 1005–1010 [译自: 生态学报]     

Application of self-organizing neural networks to classification of plant communities in Pangquangou Nature Reserve, North China

Vegetation classification is an important topic in plant ecology and many quantitative techniques for classification have been developed in the field. The arti-ficial neural network is a comparatively new tool for data analysis. The self-organizing feature map (SOFM) is powerful tool for clustering analysis. SOFM has been applied to many research fields and it was applied to the classification of plant communities in the Pangquangou Nature Reserve in the present work. Pangquangou Nature Reserve, located at 37°20'-38°20' N, 110°18'-111°18' E, is a part of the Luliang Mountain range. Eighty-nine samples (quadrats) of 10 m x 10 m for for-est, 4 m × 4 m for shrubland and 1m x 1m for grass-land along an elevation gradient, were set up and species data was recorded in each sample. After discussion of the mathematical algorism, clustering technique and the pro-cedure of SOFM, the classification was carried out by using NNTool box in MATLAB (6.5). As a result, the 89 samples were clustered into 13 groups representing 13 types of plant communities. The characteristics of each community were described. The result of SOFM clas-sification was identical to the result of fuzzy c-mean clus-tering and consistent with the distribution patterns of vegetation in the study area and shows significant eco-logical meanings. This suggests that SOFM may clearly describe the ecological relationships between plant com-munities and it is a very effective quantitative technique in plant ecology research.     

Sesquiterpene lactone-based classification of three Asteraceae tribes: a study based on self-organizing neural networks applied to chemosystematics

This work describes an application of artificial neural networks on a small data set of sesquiterpene lactones (STLs) of three tribes of the family Asteraceae. Structurally different types of representative STLs from seven subtribes of the tribes Eupatorieae, Heliantheae and Vernonieae were selected as input data for self-organizing neural networks. Encoding the 3D molecular structures of STLs and their projection onto Kohonen maps allowed the classification of Asteraceae into tribes and subtribes. This approach allowed the evaluation of structural similarities among different sets of 3D structures of sesquiterpene lactones and their correlation with the current taxonomic classification of the family. Predictions of the occurrence of STLs from a plant species according to the taxa they belong to were also performed by the networks. The methodology used in this work can be applied to chemosystematic or chemotaxonomic studies of Asteraceae.     

A comparative study of multilayer perceptron neural networks for the identification of rhubarb samples

Artificial neural networks have gained much attention in recent years as fast and flexible methods for quality control in traditional medicine. Near-infrared (NIR) spectroscopy has become an accepted method for the qualitative and quantitative analyses of traditional Chinese medicine since it is simple, rapid, and non-destructive. The present paper describes a method by which to discriminate official and unofficial rhubarb samples using three layer perceptron neural networks applied to NIR data. Multilayer perceptron neural networks were trained with back propagation, delta-bar-delta and quick propagation algorithms. Results obtained using these methods were all satisfactory, but the best outcomes were obtained with the delta-bar-delta algorithm.