Towards Structural Classification of Proteins based on Contact Map Overlap

A multitude of measures have been proposed to quantify the similarity between protein 3-D structure. Among these measures, contact map overlap (CMO) maximization deserved sustained attention during past decade because it offers a fine estimation of the natural homology relation between proteins. Despite this large involvement of the bioinformatics and computer science community, the performance of known algorithms remains modest. Due to the complexity of the problem, they got stuck on relatively small instances and are not applicable for large scale comparison. This paper offers a clear improvement over past methods in this respect. We present a new integer programming model for CMO and propose an exact B &B algorithm with bounds computed by solving Lagrangian relaxation. The efficiency of the approach is demonstrated on a popular small benchmark (Skolnick set, 40 domains). On this set our algorithm significantly outperforms the best existing exact algorithms, and yet provides lower and upper bounds of better quality. Some hard CMO instances have been solved for the first time and within reasonable time limits. From the values of the running time and the relative gap (relative difference between upper and lower bounds), we obtained the right classification for this test. These encouraging result led us to design a harder benchmark to better assess the classification capability of our approach. We constructed a large scale set of 300 protein domains (a subset of ASTRAL database) that we have called Proteus 300. Using the relative gap of any of the 44850 couples as a similarity measure, we obtained a classification in very good agreement with SCOP. Our algorithm provides thus a powerful classification tool for large structure databases

Lagrangian Approaches for a class of Matching Problems in Computational Biology

This paper presents efficient algorithms for solving the problem of aligning a protein structure template to a query amino-acid sequence, known as protein threading problem. We consider the problem as a special case of graph matching problem. We give formal graph and integer programming models of the problem. After studying the properties of these models, we propose two kinds of Lagrangian relaxation for solving them. We present experimental results on real life instances showing the efficiency of our approaches

Parallel evolution strategy for protein threading.

A protein-sequence folds into a specific shape in order to function in its aqueous state. If the primary sequence of a protein is given, what is its three dimensional structure? This is a long-standing problem in the field of molecular biology and it has large implication to drug design and cure. Among several proposed approaches, protein threading represents one of the most promising technique. The protein threading problem (PTP) is the problem of determining the three-dimensional structure of a given but arbitrary protein sequence from a set of known structures of other proteins. This problem is known to be NP-hard and current computational approaches to threading are time-consuming and data-intensive. In this thesis, we proposed an evolution strategy (ES) based approach for protein threading (EST). We also developed two parallel approaches for the PTP problem and both are parallelizations of our novel EST. The first method, we call SQST-PEST (Single Query Single Template Parallel EST) threads a single query against a single template. We use ES to find the best alignment between the query and the template, and ES is parallelized. The second method, we call SQMT-PEST (Single Query Multiple Templates Parallel EST) to allow for threading a single query against multiple templates within reasonable time. We obtained better results than current comparable approaches, as well as significant reduction in execution time.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .I85. Source: Masters Abstracts International, Volume: 44-03, page: 1403. Thesis (M.Sc.)--University of Windsor (Canada), 2005

A Parallel Branch and Bound Algorithm for the Maximum Labelled Clique Problem

The maximum labelled clique problem is a variant of the maximum clique problem where edges in the graph are given labels, and we are not allowed to use more than a certain number of distinct labels in a solution. We introduce a new branch-and-bound algorithm for the problem, and explain how it may be parallelised. We evaluate an implementation on a set of benchmark instances, and show that it is consistently faster than previously published results, sometimes by four or five orders of magnitude.Comment: Author-final version. Accepted to Optimization Letter

Finding maximum k-cliques faster using lazy global domination

No abstract available

Protein Threading for Genome-Scale Structural Analysis

Protein structure prediction is a necessary tool in the field of bioinformatic analysis. It is a non-trivial process that can add a great deal of information to a genome annotation. This dissertation deals with protein structure prediction through the technique of protein fold recognition and outlines several strategies for the improvement of protein threading techniques. In order to improve protein threading performance, this dissertation begins with an outline of sequence/structure alignment energy functions. A technique called Violated Inequality Minimization is used to quickly adapt to the changing energy landscape as new energy functions are added. To continue the improvement of alignment accuracy and fold recognition, new formulations of energy functions are used for the creation of the sequence/structure alignment. These energies include a formulation of a gap penalty which is dependent on sequence characteristics different from the traditional constant penalty. Another proposed energy is dependent on conserved structural patterns found during threading. These structural patterns have been employed to refine the sequence/structure alignment in my research. The section on Linear Programming Algorithm for protein structure alignment deals with the optimization of an alignment using additional residue-pair energy functions. In the original version of the model, all cores had to be aligned to the target sequence. Our research outlines an expansion of the original threading model which allows for a more flexible alignment by allowing core deletions. Aside from improvements in fold recognition and alignment accuracy, there is also a need to ensure that these techniques can scale for the computational demands of genome level structure prediction. A heuristic decision making processes has been designed to automate the classification and preparation of proteins for prediction. A graph analysis has been applied to the integration of different tools involved in the pipeline. Analysis of the data dependency graph allows for automatic parallelization of genome structure prediction. These different contributions help to improve the overall performance of protein threading and help distribute computations across a large set of computers to help make genome scale protein structure prediction practically feasible

Predicting Protein Contact Map By Bagging Decision Trees

Proteins\u27 function and structure are intrinsically related. In order to understand proteins\u27 functionality, it is essential for medical and biological researchers to deter- mine proteins\u27 three-dimensional structure. The traditional method using NMR spectroscopy or X-ray crystallography are inefficient compared to computational methods. Fortunately, substantial progress has been made in the prediction of protein structure in bioinformatics. Despite these achievements, the computational complexity of protein folding remains a challenge. Instead, many methods aim to predict a protein contact map from protein sequence using machine learning algorithms. In this thesis, we introduce a novel ensemble method for protein contact map prediction based on bagging multiple decision trees. A random sampling method is used to address the large class imbalance in contact maps. To generalize the feature space, we further clustered the amino acid alphabet from twenty to ten. A software is also developed to view protein contact map at certain threshold and separation. The parameters used in decision trees are determined experimentally, and the overall results for the first L, L/2 and L/5 predictions for protein of length L are evaluated

Protein Structure Prediction: Is It Useful?

Computationally predicted three-dimensional structure of protein molecules has demonstrated the usefulness in many areas of biomedicine, ranging from approximate family assignments to precise drug screening. For nearly 40 years, however, the accuracy of the predicted models has been dictated by the availability of close structural templates. Progress has recently been achieved in refining low-resolution models closer to the native ones; this has been made possible by combining knowledge-based information from multiple sources of structural templates as well as by improving the energy funnel of physics-based force fields. Unfortunately, there has been no essential progress in the development of techniques for detecting remotely homologous templates and for predicting novel protein structures

Exact, constraint-based structure prediction in simple protein models

Die Arbeit untersucht die exakte Vorhersage der Struktur von Proteinen in dreidimensionalen, abstrakten Proteinmodellen; insbesondere wird ein exakter Ansatz zur Strukturvorhersage in den HP-Modellen (Lau und Dill, ACS, 1989) des kubischen und kubisch-flächenzentrierten Gitters entwickelt und diskutiert. Im Gegensatz zu heuristischen Methoden liefert das vorgestellte exakte Verfahren beweisbar korrekte Strukturen. HP-Modelle (Hydrophob, Polar) repräsentieren die Rückgratkonformation eines Proteins durch Gitterpunkte und berücksichti\-gen ausschließlich die hydrophobe Wechselwirkung als treibende Kraft bei der Ausbildung der Proteinstruktur. Wesentlich für die erfolgreiche Umsetzung des vorgestellten Verfahrens ist die Verwendung von constraint-basierten Techniken. Im Zentrum steht die Berechnung und Anwendung hydrophober Kerne für die Strukturvorhersage