Using a statistical analysis on beta-sheet structures from the Protein Data Bank, characteristic angles within beta-strands were correlated to the nature of the side chains. The twists were computed from the atomic co...Using a statistical analysis on beta-sheet structures from the Protein Data Bank, characteristic angles within beta-strands were correlated to the nature of the side chains. The twists were computed from the atomic coordinates of five consecutive amino acids’ alpha carbons from single beta-strand sequences. Conditions on the angles for twists to be mainly left-handed are given together with the frequency of occurrence for these non-standard geometrical properties within protein beta-strands. Applications in protein structure prediction and CASP challenges in particular are envisioned by making use of the probabilities of occurrence in protein structures of angle value ranges for given amino acids.展开更多
Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate predi...Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.展开更多
文摘Using a statistical analysis on beta-sheet structures from the Protein Data Bank, characteristic angles within beta-strands were correlated to the nature of the side chains. The twists were computed from the atomic coordinates of five consecutive amino acids’ alpha carbons from single beta-strand sequences. Conditions on the angles for twists to be mainly left-handed are given together with the frequency of occurrence for these non-standard geometrical properties within protein beta-strands. Applications in protein structure prediction and CASP challenges in particular are envisioned by making use of the probabilities of occurrence in protein structures of angle value ranges for given amino acids.
文摘Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.
文摘接触脱水膜(contact dehydrating sheet,CDS)是一种适用于鱼肉等富含蛋白质食品干燥的新型冷脱水技术。为研究低温脱水对南美白对虾干复水特性的改善作用及其内在原因,本实验研究了CDS脱水至不同水分质量分数虾干的蛋白质结构变化及复水过程中复原率、持水力和水分状态的分布情况,并和热风干燥(hot air drying,HAD)的虾干进行比较。结果表明:采用CDS脱水法制备的不同水分质量分数(60%、45%、30%,以湿基计)虾干复原率、持水力均高于相对应的HAD脱水虾干组,其中水分质量分数为30%时,CDS脱水虾干的复原率、持水力分别为85.22%、69.17%,明显高于HAD脱水虾干(62.55%、65.73%)。低场核磁共振分析表明CDS脱水处理的水分质量分数为30%的虾干复水后虾仁的结合水、不易流动水、自由水比例均比HAD处理组更接近鲜虾。表面疏水性和蛋白质结构分析表明CDS脱水虾干蛋白的表面疏水性变化程度比HAD脱水低,蛋白质二级结构变化比HAD脱水虾干小。因此,CDS脱水法能维持蛋白质结构的完整性,复水后的虾干有更好的复原率、持水力,使产品品质更接近鲜虾,可以作为对虾及其他高值水产品的脱水干燥新途径。
基金supported by the National Science Foundation,USA(CHE1111000)National Institute of Health,USA(GM081655)+3 种基金Army Research Office,USA(W911NF-11-1-0251)Defense Threat Reduction Agency,USA(HDTRA1-11-1-0019)Office of Naval Research,USA(N00014-08-1-1211)Semiconductor Research Corporation,USA(P10419)
