Maciej Milostan

Application of tabu search strategy for finding low energy structure of protein

OBJECTIVE Understanding protein functionality would mean understanding the basics of life. This functionality follows a three-dimensional structure of proteins. Unfortunately till now it is not possible to obtain these structures artificially. This article offers a survey on the use of meta-heuristic methods in context of simplified models of protein folding. METHODS Tabu search (TS) strategy is one of the most successful meta-heuristics that has been applied for large number of optimization problems. In the paper, the application of TS for finding low energy conformations of proteins in a simplified lattice model has been proposed. RESULTS The algorithm has been extensively tested and the tests showed its good performance. It compares well with the other heuristic approaches. CONCLUSIONS The approach presented is competitive as compared with other methods and due to its low computation time can be used as a complementary tool for an analysis of the three-dimensional protein structures.

CASP6 Data Processing and Automatic Evaluation at the Protein Structure Prediction Center

We present a short overview of the system governing data processing and automatic evaluation of predictions in CASP6, implemented at the Livermore Protein Structure Prediction Center. The system incorporates interrelated facilities for registering participants, collecting prediction targets from crystallographers and NMR spectroscopists and making them available to the CASP6 participants, accepting predictions and providing their preliminary evaluation, and finally, storing and visualizing results. We have automatically evaluated predictions submitted to CASP6 using criteria and methods developed over the successive CASP experiments. Also, we have tested a new evaluation technique based on non‐rigid‐body type superpositions. Approximately the same number of predictions has been submitted to CASP6 as to all previous CASPs combined, making navigation through and understanding of the data particularly challenging. To facilitate this, we have substantially modernized all data handling procedures, including implementation of a dedicated relational database. An overview of our redesigned website is also presented (http://predictioncenter.org/casp6/). Proteins 2005;Suppl 7:19–23.

New tools and expanded data analysis capabilities at the protein structure prediction center

We outline the main tasks performed by the Protein Structure Prediction Center in support of the CASP7 experiment and provide a brief review of the major measures used in the automatic evaluation of predictions. We describe in more detail the software developed to facilitate analysis of modeling success over and beyond the available templates and the adopted Java‐based tool enabling visualization of multiple structural superpositions between target and several models/templates. We also give an overview of the CASP infrastructure provided by the Center and discuss the organization of the results web pages available through http://predictioncenter.org. Proteins 2007.

Some operations research methods for analyzing protein sequences and structures

Abstract.Operations Research is probably one of the most successful fields of applied mathematics used in Economics, Physics, Chemistry, almost everywhere one has to analyze huge amounts of data. Lately, these techniques were introduced in biology, especially in the protein analysis area to support biologists. The fast growth of protein data makes operations research an important issue in bioinformatics, a science which lays on the border between computer science and biology. This paper gives a short overview of the operations research techniques currently used to support structural and functional analysis of proteins.

StructAnalyzer - a tool for sequence versus structure similarity analysis.

In the world of RNAs and proteins, similarities at the level of primary structures of two comparable molecules usually correspond to structural similarities at the tertiary level. In other words, measures of sequence and structure similarities are in general correlated - a high value of sequence similarity imposes a high value of structural similarity. However, important exceptions that stay in contrast to this general rule can be identified. It is possible to find similar structures with very different sequences, as well as similar sequences with very different structures. In this paper, we focus our attention on the latter case and propose a tool, called StructAnalyzer, supporting analysis of relations between the sequence and structure similarities. Recognition of tertiary structure diversity of molecules with very similar primary structures may be the key for better understanding of mechanisms influencing folding of RNAs or proteins, and as a result for better understanding of their function. StructAnalyzer allows exploration and visualization of structural diversity in relation to sequence similarity. We show how this tool can be used to screen RNA structures in Protein Data Bank (PDB) for sequences with structural variants.

LCS-TA to identify similar fragments in RNA 3D structures

BackgroundIn modern structural bioinformatics, comparison of molecular structures aimed to identify and assess similarities and differences between them is one of the most commonly performed procedures. It gives the basis for evaluation of in silico predicted models. It constitutes the preliminary step in searching for structural motifs. In particular, it supports tracing the molecular evolution. Faced with an ever-increasing amount of available structural data, researchers need a range of methods enabling comparative analysis of the structures from either global or local perspective.ResultsHerein, we present a new, superposition-independent method which processes pairs of RNA 3D structures to identify their local similarities. The similarity is considered in the context of structure bending and bonds’ rotation which are described by torsion angles. In the analyzed RNA structures, the method finds the longest continuous segments that show similar torsion within a user-defined threshold. The length of the segment is provided as local similarity measure. The method has been implemented as LCS-TA algorithm (Longest Continuous Segments in Torsion Angle space) and is incorporated into our MCQ4Structures application, freely available for download from http://www.cs.put.poznan.pl/tzok/mcq/.ConclusionsThe presented approach ties torsion-angle-based method of structure analysis with the idea of local similarity identification by handling continuous 3D structure segments. The first method, implemented in MCQ4Structures, has been successfully utilized in RNA-Puzzles initiative. The second one, originally applied in Euclidean space, is a component of LGA (Local-Global Alignment) algorithm commonly used in assessing protein models submitted to CASP. This unique combination of concepts implemented in LCS-TA provides a new perspective on structure quality assessment in local and quantitative aspect. A series of computational experiments show the first results of applying our method to comparison of RNA 3D models. LCS-TA can be used for identifying strengths and weaknesses in the prediction of RNA tertiary structures.

Modeling of the catalytic core of Arabidopsis thaliana Dicer-like 4 protein and its complex with double-stranded RNA

Plant Dicer-like proteins (DCLs) belong to the Ribonuclease III (RNase III) enzyme family. They are involved in the regulation of gene expression and antiviral defense through RNA interference pathways. A model plant, Arabidopsis thaliana encodes four DCL proteins (AtDCL1-4) that produce different classes of small regulatory RNAs. Our studies focus on AtDCL4 that processes double-stranded RNAs (dsRNAs) into 21 nucleotide trans-acting small interfering RNAs. So far, little is known about the structures of plant DCLs and the complexes they form with dsRNA. In this work, we present models of the catalytic core of AtDCL4 and AtDCL4-dsRNA complex constructed by computational methods. We built a homology model of the catalytic core of AtDCL4 comprising Platform, PAZ, Connector helix and two RNase III domains. To assemble the AtDCL4-dsRNA complex two modeling approaches were used. In the first method, to establish conformations that allow building a consistent model of the complex, we used Normal Mode Analysis for both dsRNA and AtDCL4. The second strategy involved template-based approach for positioning of the PAZ domain and manual arrangement of the Connector helix. Our results suggest that the spatial orientation of the Connector helix, Platform and PAZ relative to the RNase III domains is crucial for measuring dsRNA of defined length. The modeled complexes provide information about interactions that may contribute to the relative orientations of these domains and to dsRNA binding. All these information can be helpful for understanding the mechanism of AtDCL4-mediated dsRNA recognition and binding, to produce small RNA of specific size.

System for accepting server predictions in CASP6

We describe the new CASP system for collecting and verifying predictions generated by servers. The system was developed to ensure reliable execution of the server assessment part of CASP, with particular emphasis on data consistency. Following the principle that predictions should not be modified by anyone but their authors and to allow a later meaningful assessment, submissions are now verified for correctness of format and contents within the strict 48 hour CASP deadlines for this type of submission. This article also provides an overview of the rules governing server participation in CASP6 and some statistics pertaining to servers in CASP6. Proteins 2005;Suppl 7:24–26.

DomGen-Graph based method for protein domain delineation

The role of a protein depends heavily on its 3D shape, which is composed of semi-independent three-dimensional blocks called domains. Domains fold independently and constitute units of evolution. Most proteins contain multiple domains that are associated with a particular functions; moreover, the same domain can be found in different proteins. Automated recognition of domains can make prediction of proteins function easier and can support the analysis of proteins. Here, we propose a novel algorithm designed for domain recognition by identification of domain boundaries in the protein structure. The proposed algorithm uses a contact graph and an iterative approach to find meaningful clusters corresponding to the protein domains. The distinctive feature of the method is its effective complexity, that improves over other well-known methods, while holding a comparable level of correct domain assignments.

Application of the Complex Event Processing system for anomaly detection and network monitoring

Protection of infrastructures for e-science, including grid environments and NREN facilities, requires the use of novel techniques for anomaly detection and network monitoring. The aim is to raise situational awareness and provide early warning capabilities. The main operational problem that most network operators face is integrating and processing data from multiple sensors and systems placed at critical points of the infrastructure. From a scientific point of view, there is a need for the efficient analysis of large data volumes and automatic reasoning while minimizing detection errors. In this article, we describe two approaches to Complex Event Processing used for network monitoring and anomaly detection and introduce the ongoing SECOR project (Sensor Data Correlation Engine for Attack Detection and Support of Decision Process), supported by examples and test results. The aim is to develop methodology that allows for the construction of next-generation IDS systems with artificial intelligence, capable of performing signature-less intrusion detection.