New evolutionary approaches to protein structure prediction

Programa de doctorado en Biotecnología y Tecnología QuímicaThe problem of Protein Structure Prediction (PSP) is one of the principal topics in Bioinformatics. Multiple approaches have been developed in order to predict the protein structure of a protein. Determining the three dimensional structure of proteins is necessary to understand the functions of molecular protein level. An useful, and commonly used, representation for protein 3D structure is the protein contact map, which represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. This thesis work, includes a compilation of the soft computing techniques for the protein structure prediction problem (secondary and tertiary structures). A novel evolutionary secondary structure predictor is also widely described in this work. Results obtained confirm the validity of our proposal. Furthermore, we also propose a multi-objective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties in order to predict contacts. Results obtained by our approach on four different protein data sets are also presented. Finally, a statistical study was performed to extract valid conclusions from the set of prediction rules generated by our algorithm.Universidad Pablo de Olavide. Centro de Estudios de Postgrad

Building Transcriptional Association Networks in Cytoscape with RegNetC

The Regression Network plugin for Cytoscape (RegNetC) implements the RegNet algorithm for the inference of transcriptional association network from gene expression profiles. This algorithm is a model tree-based method to detect the relationship between each gene and the remaining genes simultaneously instead of analyzing individually each pair of genes as correlation-based methods do. Model trees are a very useful technique to estimate the gene expression value by regression models and favours localized similarities over more global similarity, which is one of the major drawbacks of correlation-based methods. Here, we present an integrated software suite, named RegNetC, as a Cytoscape plugin that can operate on its own as well. RegNetC facilitates, according to user-defined parameters, the resulted transcriptional gene association network in .sif format for visualization, analysis and interoperates with other Cytoscape plugins, which can be exported for publication figures. In addition to the network, the RegNetC plugin also provides the quantitative relationships between genes expression values of those genes involved in the inferred network, i.e., those defined by the regression modelsMinisterio de Ciencia y Tecnología TIN2007-68084-C00Junta de Andalucía P11-TIC-752

An Efficient Nearest Neighbor Method for Protein Contact Prediction

A variety of approaches for protein inter-residue contact pre diction have been developed in recent years. However, this problem is far from being solved yet. In this article, we present an efficient nearest neigh bor (NN) approach, called PKK-PCP, and an application for the protein inter-residue contact prediction. The great strength of using this approach is its adaptability to that problem. Furthermore, our method improves considerably the efficiency with regard to other NN approaches. Our NN-based method combines parallel execution with k-d tree as search algorithm. The input data used by our algorithm is based on structural features and physico-chemical properties of amino acids besides of evo lutionary information. Results obtained show better efficiency rates, in terms of time and memory consumption, than other similar approaches.Ministerio de Educación y Ciencia TIN2011-28956-C02-0

Prediction of protein distance maps by assembling fragments according to physicochemical similarities

The prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein consists of determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, a set of 30 physicochemical amino acid properties selected from the AAindex database were used. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. The results of experiments with several non-homologous protein sets demonstrate the generality of this method and its prediction quality using the amino acid properties considered

An efficient decision rule-based system for the protein residue-residue contact prediction

Protein structure prediction remains one of the most important challenges in molecular biology. Contact maps have been extensively used as a simplified representation of protein structures. In this work, we propose a multi-objective evolutionary approach for contact map prediction. The proposed method bases the prediction on a set of physico-chemical prop erties and structural features of the amino acids, as well as evolutionary information in the form of an amino acid position specific scoring matrix (PSSM). The proposed technique produces a set of decision rules that identify contacts between amino acids. Results obtained by our approach are presented and confirm the validity of our proposal.Junta de Andalucía P07-TIC-02611Ministerio de Educación y Ciencia TIN2011-28956-C02-0

Evolutionary decision rules for predicting protein contact maps

Protein structure prediction is currently one of the main open challenges in Bioinformatics. The protein contact map is an useful, and commonly used, represen tation for protein 3D structure and represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. In this work, we propose a multi objective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties to predict contacts. We present results obtained by our approach on four different protein data sets. A statistical study was also performed to extract valid conclusions from the set of prediction rules generated by our algorithm. Results obtained confirm the validity of our proposal

Alpha Helix Prediction Based on Evolutionary Computation

Multiple approaches have been developed in order to predict the protein secondary structure. In this paper, we propose an approach to such a problem based on evolutionary computation. The proposed ap proach considers various amino acids properties in order to predict the secondary structure of a protein. In particular, we will consider the hy drophobicity, the polarity and the charge of amino acids. In this study, we focus on predicting a particular kind of secondary structure: α-helices. The results of our proposal will be a set of rules that will identify the beginning or the end of such a structure.Junta de Andalucía P07-TIC-02611Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0

An Evolutionary Approach for Protein Contact Map Prediction

In this study, we present a residue-residue contact prediction approach based on evolutionary computation. Some amino acid properties are employed according to their importance in the folding process: hydrophobicity, polarity, charge and residue size. Our evolutionary algorithm provides a set of rules which determine different cases where two amino acids are in contact. A rule represents two windows of three amino acids. Each amino acid is characterized by these four properties. We also include a statistical study for the propensities of contacts between each pair of amino acids, according to their types, hydrophobicity and polarity. Different experiments were also performed to determine the best selection of properties for the structure prediction among the cited properties.Junta de Andalucía P07-TIC-02611Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0

Improving the Efficiency of MECoMaP: A Protein Residue-Residue Contact Predictor

This work proposes an improvement of the multi-objective evolutionary method for the protein residue-residue contact prediction called MECoMaP. This method bases its prediction on physico chemical properties of amino acids, structural features and evolutionary information of the proteins. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. These decision rules generated by the algorithm represent a set of conditions to predict residue-residue contacts. A new encoding used, a fast evaluation of the examples from the training data set and a treatment of unbalanced classes of data were considered to improve the the efficiency of the algorithm

Short-Range Interactions and Decision Tree-Based Protein Contact Map Predictor

In this paper, we focus on protein contact map prediction, one of the most important intermediate steps of the protein folding prob lem. The objective of this research is to know how short-range interac tions can contribute to a system based on decision trees to learn about the correlation among the covalent structures of a protein residues. We propose a solution to predict protein contact maps that combines the use of decision trees with a new input codification for short-range in teractions. The method’s performance was very satisfactory, improving the accuracy instead using all information of the protein sequence. For a globulin data set the method can predict contacts with a maximal accu racy of 43%. The presented predictive model illustrates that short-range interactions play the predominant role in determining protein structur