1250480
Understanding protein dispensability through machine-learning analysis of high-throughput data
MOTIVATION
Protein dispensability is fundamental to the understanding of gene function and evolution. Recent advances in generating high-throughput data such as genomic sequence data, protein-protein interaction data, gene-expression data and growth-rate data of mutants allow us to investigate protein dispensability systematically at the genome scale.


RESULTS
In our studies, protein dispensability is represented as a fitness score that is measured by the growth rate of gene-deletion mutants. By the analyses of high-throughput data in yeast Saccharomyces cerevisiae, we found that a protein's dispensability had significant correlations with its evolutionary rate and duplication rate, as well as its connectivity in protein-protein interaction network and gene-expression correlation network. Neural network and support vector machine were applied to predict protein dispensability through high-throughput data. Our studies shed some lights on global characteristics of protein dispensability and evolution.


AVAILABILITY
The original datasets for protein dispensability analysis and prediction, together with related scripts, are available at http://digbio.missouri.edu/~ychen/ProDispen/


CONTACT
xudong@missouri.edu.
8023010
PROSPECT-PSPP: an automatic computational pipeline for protein structure prediction
Knowledge of the detailed structure of a protein is crucial to our understanding of the biological functions of that protein. The gap between the number of solved protein structures and the number of protein sequences continues to widen rapidly in the post-genomics era due to long and expensive processes for solving structures experimentally. Computational prediction of structures from amino acid sequence has come to play a key role in narrowing the gap and has been successful in providing useful information for the biological research community. We have developed a prediction pipeline, PROSPECT-PSPP, an integration of multiple computational tools, for fully automated protein structure prediction. The pipeline consists of tools for (i) preprocessing of protein sequences, which includes signal peptide prediction, protein type prediction (membrane or soluble) and protein domain partition, (ii) secondary structure prediction, (iii) fold recognition and (iv) atomic structural model generation. The centerpiece of the pipeline is our threading-based program PROSPECT. The pipeline is implemented using SOAP (Simple Object Access Protocol), which makes it easier to share our tools and resources. The pipeline has an easy-to-use user interface and is implemented on a 64-node dual processor Linux cluster. It can be used for genome-scale protein structure prediction. The pipeline is accessible at http://csbl.bmb.uga.edu/protein_pipeline.
17270276
ProteinDBS: a real-time retrieval system for protein structure comparison
We have developed a web server (ProteinDBS) for the life science community to search for similar protein tertiary structures in real time. This system applies computer visualization techniques to extract the predominant visual patterns encoded in two-dimensional distance matrices generated from the three-dimensional coordinates of protein chains. When meaningful contents, represented in a multi-dimensional feature space, have been extracted from distance matrices, an advanced indexing structure, Entropy Balanced Statistical (EBS) k-d tree, is utilized to index the data. Our system is able to return search results in ranked order from a database with 46 075 chains in seconds, exhibiting a reasonably high degree of precision. To our knowledge, this is the first real-time search engine for protein structure comparison. ProteinDBS provides two types of query method: query by Protein Data Bank protein chain ID and by new structures uploaded by users. The system is hosted at http://ProteinDBS.rnet.missouri.edu.
424382
An Efficient Computational Method for Globally Optimal Threading
Computational recognition of native-like folds of an anonymous amino acid sequence from a protein fold database is considered to be a promising approach to the three-dimensional (3D) fold prediction of the amino acid sequence. We present a new method for protein fold recognition through optimally aligning an amino acid sequence and a protein fold template (protein threading). The fitness of aligning an amino acid sequence with a fold template is measured by (1) the singleton fitness, representing the compatibility of substituting one amino acid by another and the combined preference of secondary structure and solvent accessibility for a particular amino acid, (2) the pairwise interaction, representing the contact preference between a pair of amino acids, and (3) alignment gap penalties. Though a protein threading problem so defined is known to be NP-hard in the most general sense, our algorithm runs efficiently if we place a cutoff distance on the pairwise interactions, as many of the existing threading programs do. For an amino acid sequence of size n and a fold template of size m with M core secondary structures, the algorithm finds an optimal alignment in O (Mn1.5C + 1 + mnC + 1) time and O (MnC + 1) space, where C is a (small) nonnegative integer, determined by a particular mathematical property of the pairwise interactions. As a case study, we have demonstrated that C is less than or equal to 4 for about 75% of the 293 unique folds in our protein database, when pairwise interactions are restricted to amino acids < or = 7 A apart (measured between their beta carbon atoms). An approximation scheme is developed for fold templates with C > 4, when threading requires too much memory and time to be practical on a typical workstation.
887820
A computational method for assessing peptide-identification reliability in tandem mass spectrometry analysis with SEQUEST
High throughput protein identification in mass spectrometry is predominantly achieved by first identifying tryptic peptides using SEQUEST and then by combining the peptide hits for protein identification. Peptide identification is typically carried out by selecting SEQUEST hits above a specified threshold, the value of which is typically chosen empirically in an attempt to separate true identifications from the false ones. These SEQUEST scores are not normalized with respect to the composition, length and other parameters of the peptides. Furthermore, there is no rigorous reliability estimate assigned to the protein identifications derived from these scores. Hence the interpretation of SEQUEST hits generally requires human involvement, making it difficult to scale up the identification process for genome-scale applications. To overcome these limitations, we have developed a method, which combines a neural network and a statistical model, for "normalizing" SEQUEST scores, and also for providing a reliability estimate for each SEQUEST hit. This method improves the sensitivity and specificity of peptide identification compared to the standard filtering procedure used in the SEQUEST package, and provides a basis for estimating the reliability of protein identifications.
12991047
The Application of Adaptive Partitioned Random Search in Feature Selection Problem
Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.
16311820
Optimal Provisioning of Elastic Service Availability
Service availability is one of the most closely scrutinized metrics in offering network services. The network vendor can earn more revenue from the customers by guaranteeing higher service availability at the cost of higher operational expense. It is important to cost-effectively provision a managed and differentiated network with various service availability guarantees under a unified platform. In this paper, we establish the framework of provisioning elastic service availability through network utility maximization, and propose an optimal and distributed solution using differentiated failure recovery schemes. First, we develop a utility function with configurable parameters to represent the satisfaction perceived by a user upon service availability as well as its allowed source rate. Second, adopting quality of protection [1] and shared path protection, we transform optimal provisioning of elastic service availability into a convex optimization problem. The desirable service availability and source rate for each user can be achieved using a price-based distributed algorithm. Finally, we numerically show the tradeoff between the throughput and the service availability obtained by users in various network topologies. Several quantitative observations are made from this investigation. For example, indiscriminately provisioning service availabilities for different kinds of users within one network leads to noteworthy sub-optimality in total network utility. The profile of bandwidth usage also illustrates that provisioning high service availability exclusively for critical applications leads to significant waste in bandwidth resource.
6904624
A threat-driven approach to modeling and verifying secure software
This paper presents a formal approach to threat-driven modeling and verification of secure software using aspect-oriented Petri nets. Based on the behavior model of intended functions, we identify and build formal models of security threats, which are potential misuses and anomalies of the intended functions that violate security goals. Threat mitigations are further modeled in an aspect-oriented paradigm. Taking Petri nets as a formal basis for modeling behaviors, threats, and mitigations as a whole, we verify properties of and consistency between behaviors and threats, and absence of identified threats from the integrated model of functions and threat mitigations. This makes it possible to achieve a design that is provably resistant to the anticipated threats and thus reduce significant design-level vulnerabilities.
205336
A strategy for persistent object service under CORBA and Web environment
With the rapid development of the Internet, the integrated applications of Web, database, CORBA and Java have offered the three-tiered client/server application models a good computing environment and implementation strategy. This paper briefly introduces this kind of client/server application model based on CORBA and Web, and discusses a persistent object service for it. The paper describes in detail the persistent object service's architecture model, interactive process of components, main interface descriptions and some considerations when implementing this strategy. Finally, a summary is supplied for the persistent object service.
7079460
A Formal Architectural Model for Logical Agent Mobility
The process of agent migration is the major difference between logical code mobility of software agents and physical mobility of mobile nodes in ad hoc networks. Without considering agent transfer, it would make little sense to mention the modeling of strong code mobility, which aims to make a migrated agent restarted exactly from the state when it was stopped before migration. From the perspective of system's architecture, this paper proposes a two-layer approach for the formal modeling of logical agent mobility (LAM) using predicate/transition (PrT) nets. We view a mobile agent system as a set of agent spaces and agents could migrate from one space to another. Each agent space is explicitly abstracted to be a component, consisting of an environmental part and an internal connector dynamically binding agents with their environment. We use a system net, agent nets, and a connector net to model the environment, agents, and the connector, respectively. In particular, agent nets are packed up as parts of tokens in system nets, so that agent transfer and location change are naturally captured by transition firing (token game) in Petri nets. Agent nets themselves are active only at specific places and disabled at all the other places in a system net. The semantics of such a two-layer LAM model is defined by transforming it into a PrT net. This facilitates the analysis of several properties about location, state, and connection. In addition, this paper also presents a case study of modeling and analyzing an information retrieval system with mobile agents.