A functional feature analysis on diverse protein–protein interactions: application for the prediction of binding affinity

Protein–protein interactions (PPIs) play crucial roles in diverse cellular processes. There are different types of PPIs based on the composition, affinity and whether the association is permanent or transient. Analyzing the diversity of PPIs at the atomic level is crucial for uncovering the key features governing the interactions involved in PPI. A systematic physico-chemical and conformational studies were implemented on interfaces involved in different PPIs, including crystal packing, weak transient heterodimers, weak transient homodimers, strong transient heterodimers and homodimers. The comparative analysis shows that the interfaces tend to be larger, less planar, and more tightly packed with the increase of the interaction strength. Meanwhile the strong interactions undergo greater conformational changes than the weak ones involving main chains as well as side chains. Finally, using 18 features derived from our analysis, we developed a support vector regression model to predict the binding affinity with a promising result, which further demonstrate the reliability of our studies. We believe this study will provide great help in more thorough understanding the mechanism of diverse PPIs.     

Construction and application of a novel library: Fourier transform infrared wavelet coefficients library

This article aims at designing a wavelet alternative to Fourier transform infrared spectra (FTIR). In order to select the most suitable wavelet parameters to perform, several decomposition levels and 53 wavelets were tested by trial and error approach, respectively. The result indicated that discrete meyer wavelet (dmey) associated with its third decomposition level was very efficient for this purpose. On the base of it, a novel library named as Fourier transform infrared wavelet coefficients library (FTIR-WC) has been constructed. Finally, two tools such as library search and structure elucidation were developed to evaluate the capability of the new library system. The results obtained were also compared with those from FTIR library by a variety of indices. The results suggested that the new library performed better but with less volume. This work is expected to propose a novel and practical strategy in infrared spectroscopic analysis.     

Study of the feasibility of distinguishing cigarettes of different brands using an Adaboost algorithm and near-infrared spectroscopy

The feasibility of utilizing an Adaboost algorithm in conjuction with near-infrared (NIR) spectroscopy to automatically distinguish cigarettes of different brands was explored. Simple linear discriminant analysis (LDA) was used as the base algorithm to train all weak classifiers in Adaboost. Both principal component analysis (PCA) and its kernel version (kernel principal component analysis, KPCA) were used for feature extraction and were also compared to each other. The influence of the training set size on the final classification model was also investigated. Using a case study, it was demonstrated that Adaboost coupled with PCA or KPCA can obviously improve the ability to discriminate between samples that cannot be separated by a single linear classifier. However, in term of the overall performance, KPCA appears preferable to PCA for feature extraction, especially when the samples used for training are relatively small. The results also indicate that more training samples should be applied, if possible, in order to fully demonstrate the superiority of Adaboost. It seems that the use of an Adaboost algorithm in conjunction with NIR spectroscopy in combination with KPCA for feature extraction comprises a promising tool for distinguishing cigarettes of different brands, especially in situations where there is an obvious overlap between the NIR spectra afforded by cigarettes of different brands.     

A Concise Synthesis of (－)-Indolizidines 209D and 209B

A general stereoselective synthetic route to 5-substituted and 5,8-disubstituted indolizidine alkaloids has been developed starting from commercially available L-proline. (－)-Indolizidines 209D and 209B were efficiently synthesized in 9.8% and 14.8% overall yields in seven and five-step reactions from readily available aldehyde 3 and ketone 10, respectively. The key steps of this synthesis involve a substrate-induced asymmetric addition of ethyl lithiopropiolate to aldehyde 3 or methyl ketone 10, and a two-step one-pot hydrogenation/cyclization sequence to construct the bicyclic skeleton.     

Thermal behavior and thermal stabilization of guanidine hydrochloride-modified acrylic fiber for preparation of low-cost carbon fiber

Journal of Thermal Analysis and Calorimetry - A commercial textile-grade acrylic fiber was modified with guanidine hydrochloride for the preparation of low-cost carbon fibers. Thermal behavior of...     

Individually double minimum-distance definition of protein–RNA binding residues and application to structure-based prediction

Identifying protein–RNA binding residues is essential for understanding the mechanism of protein–RNA interactions. So far, rigid distance thresholds are commonly used to define protein–RNA binding residues. However, after investigating 182 non-redundant protein–RNA complexes, we find that it would be unsuitable for a certain amount of complexes since the distances between proteins and RNAs vary widely. In this work, a novel definition method was proposed based on a flexible distance cutoff. This method can fully consider the individual differences among complexes by setting a variable tolerance limit of protein–RNA interactions, i.e. the double minimum-distance by which different distance thresholds are achieved for different complexes. In order to validate our method, a comprehensive comparison between our flexible method and traditional rigid methods was implemented in terms of interface structure, amino acid composition, interface area and interaction force, etc. The results indicate that this method is more reasonable because it incorporates the specificity of different complexes by extracting the important residues lost by rigid distance methods and discarding some redundant residues. Finally, to further test our double minimum-distance definition strategy, we developed a classifier to predict those binding sites derived from our new method by using structural features and a random forest machine learning algorithm. The model achieved a satisfactory prediction performance and the accuracy on independent data sets reaches to 85.0%. To the best of our knowledge, it is the first prediction model to define positive and negative samples using a flexible cutoff. So the comparison analysis and modeling results have demonstrated that our method would be a very promising strategy for more precisely defining protein–RNA binding sites.     

神经网络与紫外光谱法同时测定芳香类化合物

测定了苯酚、苯胺、α萘胺、间二硝基苯及对氧基苯甲醛混合体系的紫外光谱，用神经网络法直接对混合体系紫外光谱数据进行处理，成功地测定了混合体系各组分含量，结果良好