Enabling proteomics discovery through visual analysis

This article presents the motivation for developing visual analysis tools for proteomic data and demonstrates their application to proteomics research with a visualization tool named Peptide Permutation and Protein Prediction, or PQuad, a functioning visual analytic tool for the study of systems biology, is in operation at the Pacific Northwest National Laboratory (PNNL). PQuad supports the exploration of proteins identified by proteomic techniques in the context of supplemental biological information. In particular, PQuad supports differential proteomics by simplifying the comparison of peptide sets from different experimental conditions as well as different proteins identification or confidence scoring techniques. Finally, PQuad supports data validation and quality control by providing a variety of resolutions for huge amounts of data to reveal errors undetected by other methods.     

随机游走模型识别蛋白质网络复合物算法

识别出蛋白质复合物是理解蛋白质相互作用的重要方法。针对蛋白质网络中存在假阴性和假阳性问题,通过随机游走模型,有针对性对假阴性和假阳性数据进行有效筛选,定义 HP-complex图模型来识别具有生物意义的蛋白质复合物;利用GO本体计算蛋白质复合物之间的语义相似性,最终确定蛋白质复合物。实验证明,提出的基于随机游走模型的蛋白质复合物识别算法对输入参数不敏感,算法能够识别出有效的蛋白质复合物。     

A model organism for genomic and postgenomic studies

In an era of automated DNA sequencing and revolutionary advances in DNA sequence analysis, the attention of many researchers is now shifting away from the study of single genes or small gene clusters to whole genome analyses. Knowing the complete sequence of a genome is only the first step in understanding how the myriad of information contained within the genes is transcribed and ultimately translated into functional proteins. In the postgenomic era, the goals are to obtain an image of the dynamic cell through functional genomic and proteomic studies. With its prominent position as the first eukaryotic genome to be completely sequenced and annotated, it is not surprising that research into the yeast Saccharomyces cerevisiae is leading the way in the development of biological and computational tools for genomic and postgenomic research. This article describes how databases concerning Saccharomyces cerevisiae can be mined to obtain important information on gene/genome expression and regulation under a variety of experimental conditions as well as the type, location, and function of proteins encoded by these genes     

Key challenges in proteomics and proteoinformatics

In this article, a survey on experimental and computational approaches related to proteomics is presented. Considered broadly, proteomics includes: techniques for identifying proteins in a sample, detecting posttranslational modifications (changes to proteins after translation), predicting the structure and function of proteins from sequence data, and integrating information about protein sequences from different databases. The paper focuses on the ways in which recent biological findings complicate the mapping from genes to RNA to protein. The authors argue that the challenges encountered in proteomics provide a valuable lesson on the complexity of life itself, as live organisms always contradict oversimplified models of biological information flow. In this overview, a snapshot of contemporary issues in proteomics is shown.     

Does protein structure influence trypsin miscleavage?

In this article, a new database that relates structural information from proteins in protein data bank to closely related protein sequences in humans was developed. Because the match criteria are extremely stringent, the structure of proteins in other species to infer characteristics of the human proteins was used. As a demonstration of the approach, this database has been applied to the problem of identifying likely trypsin miscleavage sites, a significant problem in proteomics. However, the approach is very general, and can be used to answer many kinds of structural questions (including questions related to posttranslational modifications). The study found that both the surface area and the secondary structure of cleavage sites have highly statistically significant effects on trypsin cleavage. The results of this analysis do not, however, suggest that surface area or secondary structure properties of particular peptides can be used to predict miscleavage sites, at least at a global level. This analysis of cleavage sites demonstrates the general power of homology-based techniques, in which the characteristics of a single protein that has a structure that has been solved can be used to infer properties of other proteins. We expect that our database of related proteins, structures, and sequences and our ability to query experimentally determined sets of peptides against this database will allow us to answer many other questions relation to global protein expression and modification.     

Distribution analysis of the photon correlation spectroscopy of discrete numbers of dye molecules conjugated to DNA

The formation of protein complexes with other proteins and nucleic acids is critical to biological function. Although it is relatively easy to identify the components present in these complexes, it is often difficult to determine their exact stoichiometry and obtain information about the homogeneity of the sample from bulk measurements. We demonstrate the use of single molecule photon-pair correlation spectroscopy to distinguish between discrete numbers of molecules in biological complexes. Fluorescence photon antibunching is observed from a single molecule by employing time-correlated single photon counting in combination with a Hanbury-Brown and Twiss coincidence setup. In addition, pulsed laser excitation and time-tagged time-resolved data collection allow for the measurement of photon arrival times with nanosecond time resolution. The interphoton time distribution between consecutively arriving photons can be calculated and provides a measure of the second-order temporal correlation function. Analysis of this function yields an absolute measure of the number of molecules, N, present in a given complex. It is this ability to measure N that renders this technique powerful for determining stoichiometries in complex biological systems at the single molecule level. We investigate the counting efficiency and statistics of photon antibunching of specifically designed biological samples labeled with multiple copies of the same fluorescent dye and derive conclusions about its use in the analytical evaluation of complex biological samples.     

Integrative data mining: the new direction in bioinformatics

Biological research is becoming increasingly database driven, motivated, in part, by the advent of large-scale functional genomics and proteomics experiments such as those comprehensively measuring gene expression. These provide a wealth of information on each of the thousands of proteins encoded by a genome. Consequently, a challenge in bioinformatics is integrating databases to connect this disparate information as well as performing large-scale studies to collectively analyze many different data sets. This approach represents a paradigm shift away from traditional single-gene biology, and it often involves statistical analyses focusing on the occurrence of particular features (e.g., folds, functions, interactions, pseudogenes, or localization) in a large population of proteins. Moreover, the explicit application of machine learning techniques can be used to discover trends and patterns in the underlying data. In this article, we give several examples of these techniques in a genomic context: clustering methods to organize microarray expression data, support vector machines to predict protein function, Bayesian networks to predict subcellular localization, and decision trees to optimize target selection for high-throughput proteomics     

Bioinformatics

Computational methods are becoming an increasingly important aspect of the evaluation and analysis of experimental data in molecular biology. The use of computational methods towards solving problems in biology is known as bioinformatics. The field of bioinformatics is constantly redefining itself as methods for collecting biological data are developed and refined. While the future directions of the field are impossible to predict, one conclusion seems to be evident: computational techniques have changed the way in which biologists collect and analyze experimental data. Computation will continue to be a prominent component of biochemistry and molecular biology research for the foreseeable future. While early studies developed the techniques necessary to sequence entire genomes, scientists are now investigating the interacting mechanisms that control the expression of genes. Ambitious new efforts are underway to identify the complex biological pathways of interaction between genes, the proteins for which they code, and the various metabolic intermediates acted upon by these proteins. Advances in understanding these sorts of large scale biological problems bear enormous promise for improving the human condition.     

Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks

Graph theoretical analysis of structural and functional connectivity MRI data (ie. diffusion tractography or cortical volume correlation and resting-state or task-related (effective) fMRI, respectively) has provided new measures of human brain organization in vivo. The most striking discovery is that the whole-brain network exhibits “small-world” properties shared with many other complex systems (social, technological, information, biological). This topology allows a high efficiency at different spatial and temporal scale with a very low wiring and energy cost. Its modular organization also allows for a high level of adaptation. In addition, degree distribution of brain networks demonstrates highly connected hubs that are crucial for the whole-network functioning. Many of these hubs have been identified in regions previously defined as belonging to the default-mode network (potentially explaining the high basal metabolism of this network) and the attentional networks. This could explain the crucial role of these hub regions in physiology (task-related fMRI data) as well as in pathophysiology. Indeed, such topological definition provides a reliable framework for predicting behavioral consequences of focal or multifocal lesions such as stroke, tumors or multiple sclerosis. It also brings new insights into a better understanding of pathophysiology of many neurological or psychiatric diseases affecting specific local or global brain networks such as epilepsy, Alzheimer’s disease or schizophrenia. Graph theoretical analysis of connectivity MRI data provides an outstanding framework to merge anatomical and functional data in order to better understand brain pathologies.     

Engineering in genomics

This work addresses two principles that will be integral to the post-genomic or proteomic era (i.e., after sequencing). The first is that any analysis of data from or related to the Human Genome Project will need to be designed with high-throughput in mind. Just the sequence information will encompass some 3 billion nucleotides, and that does not include information about introns, exons, promoters, and many other features of interest. The volume of information that must be synthesized is even larger than the genome itself, and it is diverse in nature. It includes sequence, structural, functional, and localization information for each gene, and each of those constituents has its own levels of organization as well (e.g., functional information for a protein can be obtained at the molecular, cellular, and organismal levels). Computational analysis must be able to handle all these data in a reasonable amount of time. The second principle, which has been alluded to here, is that analysis techniques must incorporate data from a variety of sources. Archiving and indexing of sequence data, for example, must include sequences from multiple organisms and from diseased and healthy states to be maximally useful. The other levels of information, including structure, function, and localization will need to be similarly organized     

Electro-Chemical Modeling Challenges of Biological Ion Pumps

We outline the basic operational, structural and functional features of ion motive ATPases: trans-membrane proteins central to biological functions of all animal cells. As an example we discuss the modeling problems associated with the operation of the surface membrane Na⁺,K⁺-ATPase and skeletal muscle sarcoplasmic reticulum Ca²⁺-ATPase and focus on the frameworks required for their solution. There are three basic problems: identification of the pathway for ion permeation, prediction of ion binding rate coefficients and affinities based on the structure of the protein, and prediction of conformational changes of protein structure and the associated movement of charges within the membrane dielectric. A solution strategy useful in approaching the first two problems and preliminary results obtained using molecular dynamics simulations are also presented.Supported by NIH grant NS22979.     

Computational techniques and pattern recognition

Bioinformatics data come in various forms such as biological sequences, molecular structures, gene and protein expressions, molecular networks, cellular images, and literature. A major aspect of discovering biological knowledge is to search, predict, and model specific patterns of the data that are likely to be associated with an important biological phenomenon or another set of data. One of the major discoveries in bioinformatics is that specific patterns in our genome and proteome are able to decipher our characters and how prone we are for certain diseases. To date, pattern recognition algorithms have been successfully applied or catered to address a wide range of bioinformatics problems. This issue highlights a few such applications that were selected from the presentations at the Third International Association for Pattern Recognition (IAPR) International Conference on Pattern Recognition in Bioinformatics (PRIB 2007), Singapore.     

Multiple-valued computing by dipole-dipole coupled proteins

We demonstrate through a simple model validated by molecular dynamics-based simulations that terahertz-speed, many-valued logic computations, and digital signal processing, functional for several cycles of operations are potentially possible with electric field-effect, dipole-dipole coupled protein architectures with various dipole moment orientations. Many-valued logic can be applied in various areas, such as artificial intelligence, machine learning, and robotics. Furthermore, programmable logic arrays and field programmable gate arrays can also benefit from its implementation. Even top companies like Intel developed circuits based on such logic (eg, StrataFlash and a NOR flash memory). The present study suggests that multivalued logic states can be stored in a protein with the application of proper external electric fields, and digital signal propagation is potentially achievable using dipole-dipole coupled molecules, placed few nanometers (on the order of 10 nm) apart. Furthermore, we propose a Dronpa protein-based ternary logic gate, suitable for universal ternary logic computations. The architectures are potentially operational at room temperature. The proposed operational principle is not restricted to proteins only; it might be applied in case of other types of molecules or artificial structures exhibiting similar behavior.     

Predicting secondary structures of proteins

The article presents the application of a new machine-learning algorithm for the prediction of secondary structures of proteins. The logical analysis of data (LAD) algorithm was applied to recognize which amino acids properties could be analyzed to deliver additional information, independent from protein homology, useful in determining the secondary structure of a protein. The study showed that to get better results, LAD should be used as a first stage of analysis in combination with another method that is able to take into account a more detailed understanding of the physical chemistry of proteins and amino acids.     

功能安全评估技术在防爆电气系统中的使用设想

功能安全评估技术主要通过调查、依据证据来判断一个或者多个E／E／PE安全相关系统、其他技术安全相关系统或外部风险降低措施达到功能安全。应用化工行业中一个实例计算，对此项技术进行推广使用进行说明。     

Quantitative analysis of proteomics using data mining

The paper aims to develop an automated system that would ensure a robust peptide quantification process, which would permit researchers to quantify desired proteins faster and with greater reliability. Because of the uniqueness of the data used, biochemists' expertise and data mining methods were employed in this work. The system includes two main system components: one for the discovery of two quantification peptides and the internal standard peptide and the other for protein quantification in patient samples. If the required input data are available, each subsystem can be run separately. The developed system can be applied to similar problems because our design is flexible, allowing for easy adaptation.     

EMS规划态应用软件的开发与应用

设计开发了能量管理系统(EMS)规划态应用软件系统,该系统具备以下特点:应用多态机制嵌入EMS,可直接利用电网的实际运行数据进行数值仿真;能够以系统单线图的方式,动态地修改电网模型,构建未来规划电网,并基于各种实测数据断面,分析电网在不同规模和运行方式下的系统潮流及设备安全状况.提出了系统总体设计方案,介绍了图形、模型、算法分析、数据访问等子系统的设计方案和实现方法.该系统已成功应用于江苏省调电网,为未来规划电网运行方式的研究提供了一个良好的工具.     

基于功能缺陷文本的电力系统二次设备智能诊断与辅助决策

利用电力系统二次设备功能缺陷文本数据,建立了基于双向长短时记忆网络与条件随机场(BiLSTM-CRF)模型的文本信息抽取模型.在此基础上,为了进一步将数据中蕴含的知识价值应用到电力系统生产、管理过程中,构建了电力系统二次设备功能缺陷知识图谱,将各类数据间所含语义信息融入各类实体间的关系约束,建立了基于BiLSTM-CRF模型与知识图谱的二次设备功能缺陷智能诊断与辅助决策平台.该平台可依据缺陷设备类型与缺陷现象快速诊断设备的缺陷部位及原因,并推荐合理的解决措施.算例分析结果表明,相较于传统的命名实体识别算法、BiLSTM-softmax以及Seq2Seq-Attention模型,所采用BiLSTM-CRF模型的精确率、召回率、F1值这3项评估指标均有较大提升,所建平台能很好地挖掘、应用电力文本数据知识与价值,为电力系统二次设备功能缺陷处理提供有益参考.