A survey of current trends in computational predictions of protein-protein interactions

Proteomics become an important research area of interests in life science after the completion of the human genome project. This scientific is to study the characteristics of proteins at the large-scale data level, and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level. A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies. Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era, such as protein-protein interactions (PPIs). In this review, we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects. First, we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources. Second, we describe the stateof-the-art computational methods recently proposed on this topic. Finally, we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics.     

Integrating library modules into Pascal programs

We present the results of our experience in introducing modularity into the programming language Pascal in order to aid the creation and use of library modules. Our system performs the symbolic linking of source language modules producing a single Pascal text ready for compilation; performing the link phase before compilation anticipates interface consistency checks, and suggests a possible improvement of program development systems. Our extension is implemented in a preprocessor which ensures a complete compatibility with any standard Pascal compiler. In this paper we examine the main features of some high-level programming languages which support modularization and data abstraction and some experiences in introducing modularity into Pascal; on this basis we describe our choice in detail. The design and implementation details are discussed and some examples are presented.     

dxTuber: detecting protein cavities, tunnels and clefts based on protein and solvent dynamics

Empty space in a protein structure can provide valuable insight into protein properties such as internal hydration, structure stabilization, substrate translocation, storage compartments or binding sites. This information can be visualized by means of cavity analysis. Numerous tools are available depicting cavities directly or identifying lining residues. So far, all available techniques base on a single conformation neglecting any form of protein and cavity dynamics. Here we report a novel, grid-based cavity detection method that uses protein and solvent residence probabilities derived from molecular dynamics simulations to identify (I) internal cavities, (II) tunnels or (III) clefts on the protein surface. Driven by a graphical user interface, output can be exported in PDB format where cavities are described as individually selectable groups of adjacent voxels representing regions of high solvent residence probability. Cavities can be analyzed in terms of solvent density, cavity volume and cross-sectional area along a principal axis. To assess dxTuber performance we performed test runs on a set of six example proteins representing the three main classes of protein cavities and compared our findings to results obtained with SURFNET, CAVER and PyMol.     

基于朴素贝叶斯分类器的蛋白质界面残基识别(英文)

蛋白质相互作用中界面残基的识别在药物设计与生物体的新陈代谢等方面有着广泛应用。基于朴素贝叶斯分类器对属性条件独立性的要求,构建了由蛋白质序列谱和溶剂可及表面积组成的蛋白质相互作用特征模型。在一个具有代表性的蛋白质异源复合物组成的数据集中取得了68.1%的准确率、0.201 的相关系数、40.2%的特异度和 49.9%的灵敏度,取得了比其他方法更优的结果,且远优于随机的实验结果。通过一个三维可视化的结果更好地验证了方法的有效性。     

Sensor Network Structure Recognition Based on P-law

A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node. This method which builds on unit patulousness and core node signal propagation (called p-law) can rapidly and effectively achieve the global structure identification of a sensor graph network. Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering, the proposed method reveals the natural evolution process and law of graph network subgroup generation. Experimental results confirm the effectiveness, accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.     

Text,Images and Graphics Management System for Editorial Environment*

This paper describes the architecture of a system, a prototype of which is being developed by the authors at IBM Rome Scientific Centre, providing an interactive approach to the printing process, starting from the input of the basic components (images, texts and graphics) up to the definition and modification of page layout and finally to driving high resolution output devices. The system manages collections of pages, called “documents”; in turn each page is defined as a spatial composition of elementary boxes distributed on different levels. The main characteristics of the system architecture are interactivity, modularity and non-procedurality (the user has only to describe “what” has to be done, rather “how” to do it).     

Incorporating multiple genomic features with the utilization of interacting domain patterns to improve the prediction of protein-protein interactions

Protein-protein interaction (PPI) networks play an outstanding role in the organization of life. Parallel to the growth of experimental techniques on determining PPIs, the emergence of computational methods has greatly accelerated the time needed for the identification of PPIs on a wide genomic scale. Although experimental approaches have limitations that can be complemented by the computational methods, the results from computational methods still suffer from high false positive rates which contribute to the lack of solid PPI information. Our study introduces the PPI-Filter; a computational framework aimed at improving PPI prediction results. It is a post-prediction process which involves filtration, using information based on three different genomic features; (i) gene ontology annotation (GOA), (ii) homologous interactions and (iii) protein families (PFAM) domain interactions. In the study, we incorporated a protein function prediction method, based on interacting domain patterns, the protein function predictor or PFP (), for the purpose of aiding the GOA. The goal is to improve the robustness of predicted PPI pairs by removing the false positive pairs and sustaining as much true positive pairs as possible, thus achieving a high confidence level of PPI datasets. The PPI-Filter has been proven to be applicable based on the satisfactory results obtained using signal-to-noise ratio (SNR) and strength measurements that were applied on different computational PPI prediction methods.     

Biometric authentication

In this age of digital impersonation, biometric techniques are being used increasingly as a hedge against identity theft. The premise is that a biometric - a measurable physical characteristic or behavioral trait - is a more reliable indicator of identity than legacy systems such as passwords and PINs. There are three general ways to identify yourself to a computer system, based on what you know, what you have, or who you are. Biometrics belong to the "who you are" class and can be subdivided into behavioral and physiological approaches. Behavioral approaches include signature recognition, voice recognition, keystroke dynamics, and gait analysis. Physiological approaches include fingerprints; iris and retina scans; hand, finger, face, and ear geometry; hand vein and nail bed recognition; DNA; and palm prints. In this article, we focus on the two most popular biometric techniques: fingerprints and iris scans.     

Beyond particle systems: Operator networks

Particle systems are used for simulating non-linear dynamics of complex systems. They are computationally attractive, because the models are simple difference equations. The difference equations, however, constitute a closed system lacking scalability and intentionality; it is hard to “reverse engineer” the equations, to understand the relations of the variables and coefficients to the dynamics displayed by the simulation. Consequently, much of the modeling work goes into finding workarounds. In this paper, we study a potential solution. As the main contribution, we formalize particle system computations as mathematical operator networks, to gain intentionality and modularity. Operators also support the inclusion of processes outside the mathematical domain of difference equations. We illustrate the use of operator networks by simulating the construction and dynamics of an hourglass.     

Extending Prolog with modularity,concurrency and meta-rules

This paper presents the main features of an extension to Prolog toward modularity and concurrency—calledCommunicating Prolog Units (CPU)—whose main aim is to allow logic programming to be used as an effective tool for system programming and prototyping. While Prolog supports only a single set of clauses and sequential computations, CPU allows programmers to define different theories (P-unis) and parallel processes interacting via P-units, according to a model very similar to Linda’s generative communication. The possibility of expressingmeta-rules to specify where and how object-level (sub)golas have to be proved, not only enhances modularity, but also increases the expressive power and flexibility of CPU systems.     

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.     

A methodology for auto/manual logic in a computer controlled process

The usual methods of implementing the auto/manual logic in complex process oontrol systems hamper modularity and often lack a unified structure. In this paper we develop a language for describing the required logical procedures, based on the nature of the interdependencies which can exist between different control loops or functions in a system. A software structure is presented which implements the auto logic while retaining the autonomy and modularity of the control functions, and a simple example of its application is discussed.     

A formal approach to the specification and the behavior validation of real-time systems based on rewriting logic

Design of real time and concurrent systems requires formal approaches in order to facilitate verification and validation at each step. Methods based on formal logic have been previously suggested but they often work only in a specific domain and are generally only possible with specialized users. In an attempt to overcome these two restrictions, this paper proposes a method based on rewriting logic. A grounding in theory is not a prerequisite for users. The method integrates modularity and abstraction and follows the main principles of an object-oriented approach. Different tools are available: a graphical editor for the specification of the structure and the behavior of the objects, an inference engine for rule validation and a generator of prototypes.     

New integrability conditions for differential constraints

This paper discusses differential-form-based integrability conditions for dynamic constraints using the Frobenius theorem. The conditions can be used for the classification of holonomic and nonholonomic constraints. Some of the previous conditions used for this purpose are only sufficient. The conditions presented here are both necessary and sufficient.

The paper's main interest is on differential constraints for under-actuated mechanical systems. Different from many discussions in classical mechanics that deal with mostly on kinematics constraints, the constraints discussed here are from the Lagrange equations, which correspond to unactuated part of the system dynamics.     

加权Fast Newman模块化算法在人脑结构网络中的应用

针对二值人脑结构网络的模块化方法不足以反映复杂的人脑生理特征这一问题,提出一种基于Fast Newman二值算法的加权脑网络模块化算法。该算法以凝聚节点的层次聚类思想为基础,以脑网络中单个脑区节点的权重值和脑网络总权重值为主要依据构建加权模块度评价指标,并将其增量作为度量值来确定加权脑网络中节点的合并从而实现模块划分。将该算法应用于60个健康人的组平均数据中的实验结果显示,与二值人脑网络模块化结果相对比,所提算法得到的模块度提高了28%,并且模块内部和模块外部的特征区分更加明显,所得到的人脑模块也更符合已知的人脑生理特性;而与现有的两种加权模块化算法实验对比结果表明,所提算法在合理划分人脑网络模块结构的同时也小幅提高了模块度。     

Sample preparation strategies for targeted proteomics via proteotypic peptides in human blood using liquid chromatography tandem mass spectrometry

The simultaneous quantification of protein concentrations via proteotypic peptides in human blood by liquid chromatography coupled to quadrupole MS/MS is an important field of bioanalytical research with a high potential for routine diagnostic applications. This review summarizes currently available sample preparation procedures and trends for absolute protein quantification in blood using LC-MS/MS. It discusses approaches of transferring established qualitative protocols to a quantitative analysis regarding their reliability and reproducibility. Techniques used to enhance method sensitivity such as the depletion of high-abundant proteins or the immunoaffinity enrichment of proteins and peptides are described. Furthermore, workflows for (i) protein denaturation, (ii) disulfide bridge reduction and (iii) thiol alkylation as well as (iv) enzymatic digestion for absolute protein quantification are presented. The main focus is on the tryptic digestion as a bottleneck of protein quantification via proteotypic peptides. Conclusively, requirements for a high-throughput application are discussed.     

Modular Properties of Composable Term Rewriting Systems

In this paper we prove several new modularity results for unconditional and conditional term rewriting systems. Most of the known modularity results for the former systems hold for disjoint or constructor-sharing combinations. Here we focus on a more general kind of combination: so-called composable systems. As far as conditional term rewriting systems are concerned, all known modularity result but one apply only to disjoint systems. Here we investigate conditional systems which may share constructors. Furthermore, we refute a conjecture of Middeldorp (1990, 1993).     

A molecular dynamics and knowledge-based computational strategy to predict native-like structures of polypeptides

One of the main research problems in structural bioinformatics is the prediction of three-dimensional structures (3-D) of polypeptides or proteins. The current rate at which amino acid sequences are identified increases much faster than the 3-D protein structure determination by experimental methods, such as X-ray diffraction and NMR techniques. The determination of protein structures is both experimentally expensive and time consuming. Predicting the correct 3-D structure of a protein molecule is an intricate and arduous task. The protein structure prediction (PSP) problem is, in computational complexity theory, an NP-complete problem. In order to reduce computing time, current efforts have targeted hybridizations between ab initio and knowledge-based methods aiming at efficient prediction of the correct structure of polypeptides. In this article we present a hybrid method for the 3-D protein structure prediction problem. An artificial neural network knowledge-based method that predicts approximated 3-D protein structures is combined with an ab initio strategy. Molecular dynamics (MD) simulation is used to the refinement of the approximated 3-D protein structures. In the refinement step, global interactions between each pair of atoms in the molecule (including non-bond interactions) are evaluated. The developed MD protocol enables us to correct polypeptide torsion angles deviation from the predicted structures and improve their stereo-chemical quality. The obtained results shows that the time to predict native-like 3-D structures is considerably reduced. We test our computational strategy with four mini proteins whose sizes vary from 19 to 34 amino acid residues. The structures obtained at the end of 32.0 nanoseconds (ns) of MD simulation were comparable topologically to their correspondent experimental structures.