Clean SEA-TROSY Experiments to Map Solvent Exposed Amides in Large Proteins

It is well known that the SEA-TROSY experiment could alleviate some of the problems of resonance overlap in ^15N/^2H labeled proteins as it was designed to selectively map solvent exposed amide protons. However, SEATROSY spectra may be contaminated with exchange-relayed NOE contributions from fast exchanged hydroxyl or amine protons and contributions from longitudinal relaxation. Also, perdeuteration of the protein sample is a prerequisite for this experiment. In this communication, a modified version, clean SEA-TROSY, was proposed to eliminate these artifacts and to allow the experiment to be applied to protonated or partially deuterated proteins and protein complexes.     

Relationship between human IgG structure and retention time in hydroxyapatite chromatography with sodium‐phosphate gradient elution

The aim of this study was to classify the retention time of recombinant human monoclonal antibodies (rhMAbs) in hydroxyapatite chromatography with sodium‐phosphate gradient elution according to their physicochemical properties. We analyzed 37 rhMAbs and found that (i) the structures of both the constant and variable regions affected retention time, (ii) the number of basic amino acid residues in the variable region, particularly in the heavy chain, correlated well with retention time, and (iii) this correlation was more pronounced (e.g. r²=0.87 for 18 κ IgG₁ rhMAbs) when the surface accessibility of those residues are taken into account. These findings provide a useful guide for investigators and purification‐process developers working with monoclonal antibodies.     

Active site acidic residues and structural analysis of modelled human aromatase: A potential drug target for breast cancer

Summary This study sheds new light on the role of acidic residues present in the active site cavity of human aromatase. Eight acidic residues (E129, D222, E245, E302, D309, E379, D380 and D476) lining the cavity are identified and studied using comparative modeling, docking, molecular dynamics as well as statistical techniques. The structural environment of these acidic residues is studied to assess the stability of the corresponding carboxylate anions. Results indicate that the environment of the residues E245, E302 and D222 is most suitable for carboxylate ion formation in the uncomplexed form. However, the stability of D309, D222 and D476 anions is seen to increase on complexation to steroidal substrates. In particular, the interaction between D309 and T310, which assists proton transfer, is found to be formed following androgen/nor-androgen complexation. The residue D309 is found to be clamped in the presence of substrate which is not observed in the case of the other residues although they exhibit changes in properties following substrate binding. Information entropic analysis indicates that the residues D309, D222 and D476 have more conformational flexibility compared to E302 and E245 prior to substrate binding. Interaction similar to that between D476 and D309, which is expected to assist androgen aromatization, is proposed between E302 and E245. The inhibition of aromatase activity by 4-hydroxy androstenedione (formestane) is attributed to a critical hydrogen bond formation between the hydroxy moiety and T310/D309 as well as the large distance from D476. The results corroborate well with earlier site directed mutagenesis studies.     

Molecular dynamics simulation of Matrix Metalloproteinase 2: fluctuations and time evolution of recognition pockets

We report a molecular dynamics simulation study of a zinc-protease--gelatinase A or MMP2--which is a major target for drug design, being involved in tumor metastasis and other degenerative diseases. Two structures have been employed as starting conditions, one based on the crystal of multi-domain proMMP2, the other consisting of the catalytic domain only. The overall fold of the two models is maintained over the 1260 ps trajectory, enabling us to analyze correlations of fluctuations among domains, and to observe the presence of correlations within the catalytic domain in the multi-domain enzyme only, hence due to the presence of hemopexin and fibronectin domains. In the multi-domain protein, two cavities are conserved over the trajectory, one of them pointing to a key region, a crevice surrounding the catalytic zinc. The other one is localized across the three domains of the MMP2 metalloproteinase. These areas are partially covered by the propeptide in the crystal structure of proMMP2. We propose a model of MMP2-collagen interaction that involves both identified cavities and takes into account the inter/intra domain cross-correlations.     

A journey into low-dimensional spaces with autoassociative neural networks

The compression and the visualization of the data have been always a subject of a great deal of excitement. Since multidimensional data sets are difficult to interpret and visualize, much of the attention is drawn how to compress them efficiently. Usually, the compression of dimensionality is considered as the first step of exploratory data analysis. Here, we focus our attention on autoassociative neural networks (ANNs), which in a very elegant manner provide data compression and visualization. ANNs can deal with linear and nonlinear correlation among variables, what makes them a very powerful tool in exploratory data analysis. In the literature, ANNs are often referred as nonlinear principal component analysis (PCA), and due to their specific structure they are also known as bottleneck neural networks. In this paper, ANNs are discussed in details. Different training modes are described and illustrated on real example. The usefulness of ANNs for nonlinear data compression and visualization purposes is proven with the aid of chemical data sets, being the subject of analysis. The comparison of ANNs with well-known PCA is also presented.     

化学中的人工神经网络法

反向传输人工神经网络是应用最为广泛的一种方法, 本文较详细地介绍了该种方法及其相关的问题, 同时给出了Kohonen 模型和Hopfield 网络的简单算法。关于神经网络在化学中的应用, 该文介绍了6 个方面: 定量结构2活性性质相关性(QSAR/QSPR )研究, 有机化合物结构解析, 光谱的数据处理, 化学反应性, 流程优化, 故障诊断及控制, 蛋白质结构。     

Dimensionality of amino acid space and solvent accessibility prediction with neural networks

Solvent accessibility prediction from amino acid sequences has been pursued by several researchers. Such a prediction typically starts by transforming the amino acid category (or type) information into numerical representations. All twenty amino acids can be completely and uniquely represented by 20-dimensional vectors. Here, we investigate if the amino acid space defined in this way really requires twenty dimensions. We tried to develop corresponding representations in fewer dimensions. A method for searching optimal codification schema in an arbitrary space using neural networks was developed. The method is used to obtain optimal encoding of amino acids at various levels of dimensionality, and applied to optimize the amino acid codifications for the prediction of the solvent accessibility values of the proteins using feed-forward neural networks. The traditional 20-dimensional codification seems to be redundant in solving the solvent accessibility prediction problem, since a 1-dimensional codification is able to achieve almost the same degree of accuracy as the 20-dimensional codification. Optimal coding in much fewer dimensions could be used to make the predictions of accessible surface area with almost the same degree of accuracy as that obtained by a fully unique 20-dimensional coding. The 1-dimensional amino acid codification for solvent accessibility prediction obtained by a purely mathematical way based on neural networks is highly correlated with a physical property of the amino acids, namely their average solvent accessibility. The method developed to find the optimal codification is general, although the codification thus produced is dependent on the type of estimated property.     

小波神经网络在紫外光谱识别中的应用研究

介绍用于信号识别的小波神经网络的结构和算法，并将其用于酪氨酸，二羟基苯丙氨酸和色氨酸的紫外光谱识别。在波小神经网络中，采用Ｍｏｒｌｅｔ母波波和一维搜索变步长共轭梯度优化方法。结果表明，小波神经网络对于光谱间的细微结构差别具有很好的识别能力。