以序列特征值预测酶和非酶蛋白及内含肽

利用生物信息学快速准确鉴别酶、非酶蛋白及内含肽能极大提高实验效率,而测序数量的指数型增长使酶、非酶蛋白及内含肽的自动分类尤显重要。本文获取了同一性小于25%的序列共计3853条,采用Z标度的伪氨基酸组成和氨基酸组成分布提取序列特征值识别酶、非酶蛋白及内含肽。结果表明,该特征值提取方法经参数优化后,即当λ=5,w=0.15时,以支持向量机为分类器,其10倍交叉验证的精度可达81.3%,ROC曲线下面积为0.83;其精度高于其它方法0.5%到12.9%不等;独立样本测试的预测精度可达71.2%,ROC曲线下面积为0.782,其精度高于其它方法0.4%到6.4%不等,效果均优于其它常见的序列特征值方法。本文结果说明从序列出发判断其归属是可行的,3种不同功能的分子在序列特征上存在一定的差异,所建立的Z标度的伪氨基酸组成和氨基酸组成分布法可用于其它类似的生物信息学问题。建立了从序列出发预测酶、非酶蛋白及内含肽的新方法。     

An integrated microfluidic system for fast, automatic detection of C-reactive protein

C-reactive protein (CRP) is a well-known inflammation marker in human beings. This study reports a new microfluidic system for fast, automatic detection of CRP. It contains pneumatic micropumps, a vortex-type micromixer, a pneumatic micro-injector and several microvalves to automatically perform the entire protocol for CRP detection. This includes sample/reagent transportation, incubation between the target CRP and a CRP-specific aptamer, washing processes, and the chemiluminescence development process. In addition, the chemiluminescence signal is measured by using a custom-made optical system which consists of a photomultiplier tube, a portable air compressor and eight electronic magnet valves to quantify the concentration of CRP. When compared to previous works, not only can this new microfluidic system automatically perform the entire process via a new integrated micro-injector and new micropumps, but a new CRP-specific DNA aptamer with a higher affinity and specificity is also used for CRP measurement. Experimental data show that the developed system can automatically complete the entire protocol within 30 min with a detection limit of 0.0125 mg/L, which is superior to previous published results. Moreover, this study also measures CRP concentration from clinical samples to verify the performance of the developed microfluidic system. The results indicate that the measured CRP concentrations from human serums are consistent with those using a benchtop system. The developed system can also detect CRP concentrations from human whole blood without any external sample pretreatment process. This microfluidic system may be promising for point-of-care applications for CRP detection in the future.     

Exploiting knowledge ontology and software agents for PPI network analysis

One major goal of functional genomics has been to identify and analyze molecular interactions in a cellular context to better understand the underlying design principles and mechanisms. To investigate into a PPI network from both topological and functional points of view, this work proposes a methodology that exploits ontology-based biological knowledge for network analysis. To speed up the procedure, an agent-based framework is also presented for supporting distributed computing. The preliminary results show that through the knowledge obtained from gene ontology, our work in analyzing building blocks of PPI networks can give a higher resolution than that of previous ones. Also our agent-based framework can successfully speed up the task of network analysis in an adaptive manner.     

基于模式驱动和模糊查询的模式发现方法

在蛋白质序列的比对研究中,拥有相似模式的蛋白质常常具有相似的功能.通过已知的蛋白质序列模式可以很方便地对新蛋白质序列的功能结构进行研究和确认.蛋白质序列的发现已成为一个很有意义的题目.对基于模式驱动Pratt算法进行改进以提高其效率,在原来基础上引入模糊查询方法,能够更为快捷地从互不相关的蛋白质序列集合中找出最具代表性的蛋白质模式.     

A compact hybrid feature vector for an accurate secondary structure prediction

Amino acid propensity score is one of the earliest successful methods used in protein secondary structure prediction. However, the score performs poorly on small-sized datasets and low-identity protein sequences. Based on current in silico method, secondary structure can be predicted from local folds or local protein structure. In biology, the evolution of secondary structure produces local protein structure with different lengths. To precisely predict secondary structures, we propose a derivative feature vector, DPS that utilizes the optimal length of the local protein structure. DPS is the unification of amino acid propensity score and dihedral angle score. This new feature vector is further normalized to level the edges. Prediction is performed by support vector machines (SVM) over the DPS feature vectors with class labels generated by secondary structure assignment method (SSAM) and secondary structure prediction method (SSPM). All experiments are carried out on RS126 sequences. The results from this proposed method also highlight the overall accuracy of our method compared to other state-of-the-art methods. The performance of our method was acceptable specifically in dealing with low number and low identity sequences.     

新型核序列自相关函数用于非同源蛋白质结构类预测

从天然氨基酸的50个性质参数中经主成分分析得出1种新的氨基酸描述子:氨基酸特征性质得分.并在此基础上通过定义基于向量形式的自相关函数以及引入Mercer核技术将该函数运算空间进行非线性变换,最终提出了1种新的蛋白质序列表征方法:核序列自相关函数.采用该函数对632个已知晶体结构的非同源蛋白分类研究结果表明:KSACF能...     

A Constraint-Based Approach for Deriving 3-D Structures of Cyclic Polypeptides

This paper presents an hybrid algorithm for deriving 3-D structures of cyclic polypeptides. The algorithm combines constraint-based techniques with the most widely used methods for non-cyclic polypeptides. The empirical results demonstrate that the proposed hybrid algorithm outperforms traditional methods especially with respect to running times.     

Genetic Threading

The biological function of proteins is dependent, to a large extent, on their native three dimensional conformation. Thus, it is important to know the structure of as many proteins as possible. Since experimental methods for structure determination are very tedious, there is a significant effort to calculate the structure of a protein from its linear sequence. Direct methods of calculating structure from sequence are not available yet. Thus, an indirect approach to predict the conformation of protein, called threading, is discussed. In this approach, known structures are used as constraints, to restrict the search for the native conformation. Threading requires finding good alignments between a sequence and a structure, which is a major computational challenge and a practical bottleneck in applying threading procedures. The Genetic Algorithm paradigm, an efficient search method that is based on evolutionary ideas, is used to perform sequence to structure alignments. A proper representation is discussed in which genetic operators can be effectively implemented. The algorithm performance is tested for a set of six sequence/structure pairs. The effects of changing operators and parameters are explored and analyzed.     

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.     

Proteomics integrated with Escherichia coli vector-based vaccines and antigen microarrays reveals the immunogenicity of a surface sialidase-like protein of Propionibacterium acnes

Proteomics is a powerful tool for the identification of proteins, which provides a basis for rational vaccine design. However, it is still a highly technical and time‐consuming task to examine a protein's immunogenicity utilizing traditional approaches. Here, we present a platform for effectively evaluating protein immunogenicity and antibody detection. A tetanus toxin C fragment (Tet‐c) was used as a representative antigen to establish this platform. A cell wall‐anchoring sialidase‐like protein (SLP) of Propionibacterium acnes was utilized to assess the efficacy of this platform. We constructed an Escherichia coli vector‐based vaccine by overexpressing Tet‐c or SLP in E. coli and utilized an intact particle of E. coli itself as a vaccine (E. coli Tet‐c or SLP vector). After ultraviolet (UV) irradiation, the E. coli vector‐based vaccines were administered intranasally into imprinting control region mice without adding exogenous adjuvants. For antibody detection, we fabricated antigen microarrays by printing with purified recombinant proteins including Tet‐c and SLP. Our results demonstrated that detectable antibodies were elicited in mice 6 weeks after intranasal administration of UV‐irradiated E. coli vector‐based vaccines. The antibody production of Tet‐c and SLP was significantly elevated after boosting. Notably, the platform with main benefits of using E. coli itself as a vaccine carrier provides a critical template for applied proteomics aimed at screening novel vaccine targets. In addition, the novel immunogenic SLP potentially serves as an antigen candidate for the development of vaccines targeting P. acnes‐associated diseases.