SuccSite:Incorporating Amino Acid Composition and Informative k-spaced Amino Acid Pairs to Identify Protein Succinylation Sites

Protein succinylation is a biochemical reaction in which a succinyl group(-CO-CH2-CH2-CO-)is attached to the lysine residue of a protein molecule.Lysine succinylation plays important regulatory roles in living cells.However,studies in this field are limited by the difficulty in experimentally identifying the substrate site specificity of lysine succinylation.To facilitate this process,several tools have been proposed for the computational identification of succinylated lysine sites.In this study,we developed an approach to investigate the substrate specificity of lysine succinylated sites based on amino acid composition.Using experimentally verified lysine succinylated sites collected from public resources,the significant differences in position-specific amino acid composition between succinylated and non-succinylated sites were represented using the Two Sample Logo program.These findings enabled the adoption of an effective machine learning method,support vector machine,to train a predictive model with not only the amino acid composition,but also the composition of k-spaced amino acid pairs.After the selection of the best model using a ten-fold crossvalidation approach,the selected model significantly outperformed existing tools based on an independent dataset manually extracted from published research articles.Finally,the selected model was used to develop a web-based tool,SuccSite,to aid the study of protein succinylation.Two proteins were used as case studies on the website to demonstrate the effective prediction of succinylation sites.We will regularly update SuccSite by integrating more experimental datasets.SuccSite is freely accessible at http://csb.cse.yzu.edu.tw/SuccSite/.

嗜热酶的耐热机理

酶蛋白在高温下的不稳定性是影响其广泛应用的主要瓶颈,嗜热酶因为独特的性质而被作为热稳定研究的极好材料。了解嗜热酶的热稳定性机制,对于采用酶工程定向设计、改造酶具有重要的意义。嗜热酶的热稳定性并不是由单一因素决定的,氨基酸组成、氢键、离子对、二硫键等都是影响嗜热酶热稳定性的重要因素。相对于嗜温酶,嗜热酶更多地采用寡聚体的形式。

基于三类特征融合的O-糖基化位点预测

糖基化是蛋白质翻译后的主要修饰,O-糖基化的固定模式未知,高精度识别O-糖基化位点是机器学习面临的挑战性问题.以迄今最大的人O-糖基化位点Steentoft数据集为基础,本文首次提出了基于位置的卡方差表特征χ^2pos,融合伪氨基酸序列进化信息Pse PSSM以及无方向的k间隔氨基酸对组分Undirected-CKSAAP表征序列,构建5个正负样本均衡的支持向量机分类器,经加权投票,独立测试准确率、Matthew相关系数及ROC曲线下面积,分别达到了89.62%、0.79、0.96,明显优于文献报道结果.χ^2pos、Pse PSSM与Undirected-CKSAAP三种特征的融合在蛋白质糖基化、磷酸化等位点预测中有广泛应用前景.

基于多标签直推学习的抗菌肽及其抗菌功能预测

抗菌肽是广泛存在于生物体内的一类具有广谱抗菌作用的天然多肽,因其不易导致细菌耐药性,已成为医药界开发新型抗菌制剂的主要选择,识别出更多的抗菌肽并预测其抗菌功能具有重要意义.提出了一种基于多标签直推学习的抗菌肽及其抗菌功能的预测方法,该方法利用K-spaced氨基酸对组成方法提取多肽特征,采用多标签学习框架和加权近邻图构建直推预测模型,通过对有标签训练样本和无标签待测样本的共同学习来提升预测性能.该方法不仅能够识别多肽是否为抗菌肽,还能同时预测出抗菌肽所具有的单种或多种抗菌功能,且适用于对多效抗菌肽和普通抗菌肽的预测.数值实验表明,与已有的iAMP-2L预测方法相比,所提方法在全局预测精度和多标签预测性能上均有较大提升.

改进CKSAAP结合RFE算法预测蛋白质棕榈酰化位点

蛋白质棕榈酰化是一种可逆的蛋白质翻译后修饰,在蛋白质稳定性和亚细胞定位等方面发挥重要作用。构建了一种预测蛋白质棕榈酰化位点的新模型(PSSM-CKSAAP-RFE)。采用蕴含进化信息的k-spaced氨基酸对组分方法表征蛋白质序列,通过递归特征消除法进行特征选择;基于上述特征训练支持向量机分类器,并采用夹克刀交叉验证法测试模型性能。研究结果显示,训练集和独立测试集的预测准确率、马修斯相关系数、特异性、敏感性和受试者工作特征曲线下面积分别为98.44%、0.94、98.95%、95.65%和0.990,以及98.41%、0.93、99.39%、92.31%和0.994,优于文献中报道的相关方法,为蛋白质棕榈酰化位点的预测提供了一种新模型。

SIMCA法用于从非同源蛋白一级序列预测其结构类

蛋白质结构类的正确识别对于其三级结构预测具有十分重要的意义,有必要引入先进的算法提高预测精度。使用SIM-CA 法处理氨基酸组成、自相关系数提取的特征参数以及氨基酸对含量,进行了蛋白质结构类的预测。采用Miyazawa 和Jerni-gan 的疏水值时,All-α、All-β、αβ类的自检验的精度为89%、91%、89%,它检验的精度分别为74%、87%、91%;引入氨基酸对含量后,All-α、All-β、αβ类自检验精度为86%、89%、90%,它检验的精度为77%、88%、93%。SIMCA 的预测结果好于Bayes-ian 识别函数法,氨基酸对的引入可以提高预测精度。

GO molecular function coding based protein subcellular localization prediction

Subcellular localization is an important feature of proteins which is closely correlated to their function. In this work,we tried to develop a new coding method of using those location predictive molecular function terms of protein as the input for the prediction of subcellular localization. Combined with the amino acid pair composition of the sequence,this coding system is proved to be efficient for support vector machine (SVM) and to have satisfied performance when tested on the RH dataset. Meanwhile,the model also shows robustness against N-terminal uncertainties in sequences.