Weighted SVM在蛋白质磷酸化位点预测中的应用

Weighted SVM是标准SVM针对非均衡样本的改进。首次将Weighted SVM用于蛋白质磷酸化位点的预测,在最新版的蛋白质磷酸化数据集PhosphoBase上,取得了目前为止最好的分类精度。k-fold交叉验证和独立测试集实验的结果表明,通过对样本数相对较少的正样本赋予较大的惩罚参数,Weighted SVM有效地改善了分类器向负样本方向的“偏斜”,提高了总的预测正确率以及(正样本)查全率。     

基于AdaBoost方法的蛋白质磷酸化修饰规则抽取

磷酸化是最重要的蛋白质翻译后修饰之一,随着蛋白质磷酸化数据的增加,利用已有数据对蛋白质磷酸化修饰进行规律挖掘和预测的条件日益成熟.设计新的基于AdaBoost(adaptive boost)分类器的规则抽取算法和利用修饰位点附近氨基酸性质作为特征并采用AdaBoost方法进行特征选择和分类器训练的磷酸化修饰位点预测方法AproPhos(using amino acid properties for phosphorylation sites prediction),使其在具有较高预测精度的同时可以对预测结果进行可理解的规则解释,规则抽取还有助于发现新的磷酸化修饰氨基酸性质分布规律,对揭示生命活动规律和药物开发有着重要意义.     

基于蛋白质互作网络挖掘结直肠癌致病基因

结直肠癌是消化系统常见的恶性肿瘤之一,死亡率居发达国家恶性肿瘤死亡率的第3位。本文通过生物分析进行结直肠癌致病基因的识别。首先,基于GEO中GSE9348基因表达数据集,利用R语言的LIMMA包筛选出P<0.05,Fold change>2的结直肠癌差异基因339个;其次,基于OMIM数据库中已知结直肠癌的致病基因和STRING数据库,获得差异表达基因与致病基因的蛋白质互作网络;接着利用Cytoscape软件的ClusterONE插件进行蛋白质互作网络模块分析,获得一个含有53个基因的子网络;最后,通过对子网络的拓扑分析,获得了FOS、CCND1、CEBPB、EGR1和NOS3等5个新结直肠癌致病基因。同时,通过功能富集分析和文献挖掘对新发现的致病基因进行验证。     

鹿茸多肽的药效分析

文章探讨了鹿茸多肽对β淀粉样蛋白诱导脊髓神经元细胞凋亡的保护作用。     

多肽与蛋白质对接的研究进展

多肽类药物具有生理活性强、免疫原性低、疗效高等优点,在临床治疗上有着广阔的应用前景.随着固相肽合成和组合肽库等技术的发展,人们已经可以快速合成大量结构不同的多肽用于药物筛选和研发,但随之而来的问题是传统随机筛选方法盲目性大,实验成本高,因此多肽的理性设计方法也日益被人们所关注.一些经典的药物对接软件如DOCK、AutoDOCK等也被用于模拟多肽与其靶标蛋白形成的复合物结构,或被用于多肽虚拟库的筛选.然而相对于小分子化合物,多肽分子柔性较大,而且随着肽链的延长其计算复杂度呈几何级数增长,多肽与蛋白质对接的算法和精度还有待进一步提高.本文重点介绍当前应用于多肽-蛋白质对接的一些方法,例举一些文献报道的应用实例,以促进计算机辅助手段在多肽类药物理性设计中的应用.     

蛋白质复合物预测方法分析与比较*

分析和比较了五种蛋白质复合物预测的典型计算方法,并讨论了该领域一些有希望的研究方向。实验结果显示各种计算方法预测出的复合物能较好地匹配真实的复合物。如果恰当地考虑蛋白质相互作用数据的质量而将数据源的噪声最小化并将各种生物特征结合到预测过程中去,计算方法的性能将进一步改善。     

蛋白质系统发育分析并行计算方法研究

在目前系统发育学研究中,多数系统发育分析工具不能在GPU架构上分析蛋白质序列。为此,提出一种大规模系统发育分析方法 tgpMC~3。以添加虚字符的形式重新构造条件似然概率矩阵,降低由于多线程分支发散导致的时间消耗。设计粒度适中的半任务间并行策略,增加流多处理器上活跃的线程块数量。通过简单的键值对应方法传输含有模糊状态的转移概率矩阵,实现数据访问速度的提升。实验结果表明,与MrBayes v3. 1. 2串行版本方法相比,该方法最高可实现117的加速比,与taMC~3方法相比,该方法的并行分析性能更好。     

结合多数据源预测蛋白质复合物

蛋白质相互作用数据具有较高的假阳性率和假阴性率,这直接导致计算方法从中预测蛋白质复合物会产生较大的误差。为了弥补数据的这种先天性不足,通过结合多数据源,一种新的蛋白质复合物预测算法被提出。匹配分析和GO功能富集分析被用于评估算法的性能。测试结果表明,新算法远优于以前的其他算法。     

蛋白激酶活性及抑制性检测研究进展

蛋白激酶催化蛋白磷酸化是一种非常重要的蛋白质翻译后修饰方式,能够调节细胞内大多数蛋白的功能,在众多生理过程中发挥着至关重要的作用。磷酸化过程的异常以及激酶的过度表达往往与许多疾病密切相关,如癌症、糖尿病和老年痴呆症。因而,测定蛋白激酶的活性及筛选蛋白激酶抑制剂对基础生物医学研究和酶靶药物的开发是非常重要的。近年来,研究工作者们采用电化学法、比色法以及荧光检测法等方法研究了对蛋白激酶活性及抑制性的检测。该文回顾了蛋白激酶活性测定及抑制性检测的研究进展,主要关注了荧光检测法的应用,并对其未来的发展进行展望。     

基于 AlphaFold 数据库分析蛋白质进化中的统计规律

由DeepMind开发的AlphaFold在蛋白质结构预测领域取得了前所未有的巨大突破,对生命科学的研究产生了革命性的影响。基于大规模的结构预测,AlphaFold结构预测数据库得以建立,它包含2亿多种蛋白,并覆盖了数十种物种的完整蛋白质组。该综述介绍了在“后AlphaFold时代”利用统计物理方法研究蛋白质进化问题的一些最新进展。传统的蛋白质进化研究往往关注同一个家族的蛋白质序列或者结构(微观视角),而随着AlphaFold预测的海量蛋白质结构的出现,研究者可以把视角扩展到大量蛋白质的集合,甚至是直接对比不同物种体内的全部蛋白质,从中挖掘统计趋势(宏观视角)。基于AlphaFold数据库,通过对比40多种模式生物体内相似链长的蛋白质,研究者发现了蛋白质分子进化中的统计规律。随着物种复杂性的提高,蛋白质结构将趋向于更高的柔性和模块化程度,蛋白质序列将趋向于出现更显著的亲疏水片段分隔,蛋白质的功能专一性也不断提高。这些基于AlphaFold的统计研究在分子进化和物种进化之间建立了联系,有助于理解生物复杂性的演化。     

The multigene families of guanine nucleotide-binding proteins: Evolutionary and computational analysis

The multigene families of guanine nucleotide-binding proteins (G proteins) regulate numerous cell functions including transmembrane signal transduction and oncogenesis. These gene families have been intensively studied in a wide variety of eukaryotic systems, resulting in rich substrate for computational analysis in an evolutionary and developmental context. Signal transducing GTP-binding proteins are often considered in three general categories: (1) the heterotrimeric G proteins—e.g. G_s and G_i, the stimulatory and inhibitory regulators of adenylyl cyclase that transmit signals from transmembrane receptors to intracellular effectors and contain distinctive 40 kDa α subunits; (2) the family of smaller (∼20 kDa) monomeric proteins related to the oncogene product ras, including rab, rac, ypt, and sec4; and (3) the ADP-ribosylation factors (ARFs). Analysis of these multigene G protein families present instructive examples of the increasing sophistication in computational sequence analysis that will be necessary to keep pace with the expanding number of gene families that will be characterized in the course of the Human Genome Project. Convenient computational analysis is critical to multiple phases of the current work: (1) to design PCR primers for effective cross-species amplification; (2) to analyze the gene structures of sequence obtained; (3) to identify functional domains in protein coding exons and in untranslated regions (UTRs); and (4) to discern evolutionary relationships between cloned sequences.     

Secretome analysis of novel IgE-binding proteins from Penicillium citrinum

The Penicillium genus of fungi is a frequently reported cause of allergic reactions. However, only a limited number of allergens have been reported. In Penicillium spp., many allergens show higher IgE-binding activity in culture filtrate extracts than in cellular extracts. In order to investigate the IgE-reactive profile of mold-sensitized patients, secreted IgE-reactive proteins from Penicillium citrinum were identified by 2-DE, serum immunoblotting, and nanoLC-MS/MS. Among the IgE-reactive spots, one known allergen, Pen c 13, and four novel allergens were identified. The cDNAs coding for Pen c 32 and Pen c 30 were cloned using designed primers based on nanoLC-MS/MS analysis. The amino acid sequences of Pen c 32 and Pen c 30 were, respectively, found to have extensive similarity with those of pectate lyases and catalases from various fungi. Native Pen c 30 was shown to have catalase activity and to bind to serum IgE from 48% of mold-allergic patients and induced immediate type skin reactions in a sensitized patient. Here, we present a proteome approach which resulted in the identification of four novel secreted allergens. These novel allergens might be useful in allergy diagnosis and in the treatment of mold-allergic disorders.     

Proteomic analysis of pericardial effusion: Characteristics of tuberculosis-related proteins

The aim of this study has been designed to identify the tuberculosis (TB)-related proteins in pericardial effusion by proteomic approaches. TB is one of the major infectious diseases causing pericardial effusion. This study details protein profiles in pericardial effusion from three TB patients and three heart failure patients. Pericardial effusions were analyzed using 2-DE combined with the nano-HPLC-ESI-MS/MS. Eleven protein spots with differential expression in pericardial effusion were identified between the two groups of TB and heart failure patients (the control group). Seven protein spots were upregulated and four were downregulated. The composition of the pericardial effusion proteome may reflect the pathophysiological conditions affecting the progression of tuberculous pericarditis. The proteins in the tuberculous pericardial effusion with differential expression may serve as new and direct indicators of drug treatment. A possible conclusion is indicated that fibrinogen may play an important role for fibrin assembly in tuberculous pericardial effusion.     

Television-based densitometric analysis of proteins separated by two-dimensional gel electrophoresis

A program is described for a computer-aided, television-based system which can quantify the densities of stained proteins on two-dimensional electrophoretic gels. The system is designed to detect and graphically reproduce the edges of spots which are often located in nonuniform levels of background stain and to integrate the density of each spot detected. Tests of the system are described which show that it responds linearly to optical densities encountered on the electrophoretic gels and is sufficiently stable and sensitive to permit quantitative analysis of most, if not all, proteins separated on a gel.     

Proteomic analysis of secretory proteins and vesicles in vascular research

The release of proteins and membrane vesicles in the bloodstream regulates diverse vascular processes, both physiological, such as angiogenesis and haemostasis, and pathological, such as atherosclerosis and atherothrombosis. Proteomics, beside its canonical application for the expression profiling in cells and organs, can be applied to the study of secreted proteins and microvesicles, which play a significant role in the homeostasis of the vasculature, and the development of the atherosclerotic disease.     

Quantitative analysis of the selective pressure exerted on homologous proteins

Evolution analysis is used to locate the regions of a protein that are important for its function or structure. The rate of evolution is generally constant for a given family of homologous sequences. From the starting point of this observation, an algorithm is proposed to establish quantitatively the sequence zones where selective pressure is maximal. A program that computes this pressure has been written in PASCAL. Analysis of results on some sequences validate this theoretical approach, and this knowledge can be used as a starting-point for carrying out site-directed mutagenesis.     

Proteomic analysis for human urinary proteins associated with arsenic intoxication

Arsenic is widely distributed in nature and is mainly transported in the environment by water. Consumption of artesian well water with high levels of arsenic has been associated with genitourinary cancer, especially bladder transitional cell carcinoma (TCC). To search for biomarkers that are specific for arsenic associated with the diagnosis of bladder TCC, proteins in the urine of non-cancer urological patients and patients with either bladder TCC or arsenic-associated bladder TCC were systematically examined by HPLC ESI-MS/MS. Urine specimens were collected by catheterization from patients and age- (within 5?years) and sex-matched non-cancer urological patients. A nano-HPLC-ESI-MS/MS was used to generate proteome patterns from urine specimens obtained from patients with arsenic-associated (n?=?8) and non-arsenic-associated (n?=?8) primary TCC and from sex- and age-matched non-cancer urological patients (n?=?8). Three urinary proteins were found to have significantly altered levels in patients following chronic arsenic exposure. These proteins were a disintegrin and metalloprotease (ADAM) protein, a calpain9, and ring finger protein 20. The large-scale identification of urinary proteomes using HPLC ESI-MS/MS may serve as an ideal and efficient method to establish a panel of potential arsenic-associated TCC biomarkers and may help elucidate the mechanisms involved in bladder cancer induced by chronic arsenic exposure.     

Discovery of stage-related proteins in esophageal squamous cell carcinoma using proteomic analysis

Esophageal squamous cell carcinoma (ESCC) is the major subtype of esophageal cancers in China, and characterized with high morbidity and mortality. So far, the diagnosis of ESCC is mainly dependent on the alterations in esophageal histology, but most cases of ESCC with low stage do not display visible histological abnormalities. Therefore, a deep understanding of the mechanism of ESCC progression and seeking stage-specific molecules might improve the diagnosis and therapy for ESCC. In this study, we used proteomics to analyze ESCC tissues with classification by TNM stage, and determined the proteomic features correlated with ESCC progression (from stages I to III). Proteins that exhibited significantly different expression patterns between ESCC and corresponding normal esophageal tissues were identified using MS. The identified proteins with differentiated expression mainly fell into three protein categories (i.e. cytoskeleton system-associated proteins, metabolism enzymes, and heat shock proteins). In addition, real-time PCR highlighted some molecules that were associated with tumor stages at the mRNA level, such as enolase 1, chromosome 1 ORF 10, elastase inhibitor, α B crystalline, stress-induced phosphoprotein 1, and squamous cell carcinoma antigen 1. Altogether, these data provided further information on ESCC progression and potential drug targets for ESCC clinical therapy.