Márcio Dorn

Three-dimensional protein structure prediction

A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction.

A Hybrid Method for the Protein Structure Prediction Problem

This article provides the initial results of our effort to develop a hybrid prediction method, combining the principles of de novoand homology modeling, to help solve the protein three-dimensional (3-D) structure prediction problem. A target protein amino acid sequence is fragmented into many short contiguous fragments. Clustered short templates fragments, obtained from experimental protein structures in the Protein Data Bank (PDB), using the NCBI BLASTp program, were used for building an initial conformation, which was further refined by molecular dynamics simulations. We tested our method with the artificially designed alpha helical hairpin (PDB ID: 1ZDD) starting with its amino acids sequence only. The structure obtained with the proposed method is topologically a helical hairpin, with a C( RMSD of ~ 5.0 A with respect to the experimental PDB structure for all 34 amino acids residues, and only ~ 2.0 A when considering amino acids 1 to 22. We discuss further improvements to the method.

APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction.

Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino acid sequence remains as an unsolved problem. We present a new computational approach to predict the native-like three-dimensional structure of proteins. Conformational preferences of amino acid residues and secondary structure information were obtained from protein templates stored in the Protein Data Bank and represented as an Angle Probability List. Two knowledge-based prediction methods based on Genetic Algorithms and Particle Swarm Optimization were developed using this information. The proposed method has been tested with twenty-six case studies selected to validate our approach with different classes of proteins and folding patterns. Stereochemical and structural analysis were performed for each predicted three-dimensional structure. Results achieved suggest that the Angle Probability List can improve the effectiveness of metaheuristics used to predicted the three-dimensional structure of protein molecules by reducing its conformational search space.

Mining the Protein Data Bank with CReF to predict approximate 3-D structures of polypeptides

n this paper we describe CReF, a Central Residue Fragment-based method to predict approximate 3-D structures of polypeptides by mining the Protein Data Bank (PDB). The approximate predicted structures are good enough to be used as starting conformations in refinement procedures employing state-of-the-art molecular mechanics methods such as molecular dynamics simulations. CReF is very fast and we illustrate its efficacy in three case studies of polypeptides whose sizes vary from 34 to 70 amino acids. As indicated by the RMSD values, our initial results show that the predicted structures adopt the expected fold, similar to the experimental ones.

A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides

Tertiary Protein Structure Prediction is one of the most important problems in Structural Bioinformatics. Along the last 20years many algorithms have been proposed as to solve this problem. However, it still remains a challenging issue because of the complexity and of the dimensionality of the protein conformational search space. In this article a first principle method which uses database information for the prediction of the 3-D structure of polypeptides is presented. The technique is based on the Group Method of Data Handling (GMDH) algorithm, implemented by a software tool introduced on this work. GMDH Polynomial Neural Networks have been used with success in many fields such as data mining, knowledge discovery, pattern recognition and prediction. The proposed method was tested with seven protein sequences whose sizes vary from 14 to 54 amino acid residues. Results show that the predicted tertiary structures adopt a fold similar to the experimental structures. RMSD and secondary structure analysis reveal that the proposed method present accurate results in their predictions. The predicted structures can be used as input structures in refinement methods based on molecular mechanics (MM), e.g. molecular dynamics (MD) simulations. The search space is expected to be greatly reduced and the ab initio methods can demand a much reduced computational time to achieve a more accurate polypeptide structure.

A hybrid genetic algorithm for the 3-D protein structure prediction problem using a path-relinking strategy

One of the main research problems in Structural Bioinformatics is related to the prediction of three-dimensional structures (3-D) of polypeptides or proteins. The rate at which amino acid sequences are identified is increasing faster than the 3-D protein structure determination by experimental methods. Computational prediction methods have been developed during the last years, but the problem still remains challenging because of the complexity and high dimensionality of a protein confor-mational search space. In this article we present a hybrid genetic algorithm for the Protein Structure Prediction (PSP) Problem. A genetic algorithm is combined with a structured population, and it is hybridized with a path-relinking procedure that helps the algorithm to scape from local minima. We perform a set of experiments and show that the proposed hybrid genetic algorithm is effective in finding good quality solutions for the PSP Problem.

NIAS-Server: Neighbors Influence of Amino acids and Secondary Structures in Proteins.

The exponential growth in the number of experimentally determined three-dimensional protein structures provide a new and relevant knowledge about the conformation of amino acids in proteins. Only a few of probability densities of amino acids are publicly available for use in structure validation and prediction methods. NIAS (Neighbors Influence of Amino acids and Secondary structures) is a web-based tool used to extract information about conformational preferences of amino acid residues and secondary structures in experimental-determined protein templates. This information is useful, for example, to characterize folds and local motifs in proteins, molecular folding, and can help the solution of complex problems such as protein structure prediction, protein design, among others. The NIAS-Server and supplementary data are available at http://sbcb.inf.ufrgs.br/nias .

A Memetic Algorithm for 3-D Protein Structure Prediction Problem.

Memetic Algorithms are population-based metaheuristics intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the Memetic Algorithms. In this paper, we present a Memetic Algorithm to tackle the three-dimensional protein structure prediction problem. The method uses a structured population and incorporates a Simulated Annealing algorithm as a local search strategy, as well as ad-hoc crossover and mutation operators to deal with the problem. It takes advantage of structural knowledge stored in the Protein Data Bank, by using an Angle Probability List that helps to reduce the search space and to guide the search strategy. The proposed algorithm was tested on 19 protein sequences of amino acid residues, and the results show the ability of the algorithm to find native-like protein structures. Experimental results have revealed that the proposed algorithm can find good solutions regarding root-mean-square deviation and global distance total score test in comparison with the experimental protein structures. We also show that our results are comparable in terms of folding organization with state-of-the-art prediction methods, corroborating the effectiveness of our proposal.

MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates

The prediction and analysis of the three- dimensional (3D) structure of proteins is a key research problem in Structural Bioinformatics. The 1990’s Genome Projects resulted in a large increase in the number of available protein sequences. However, the number of identified 3D protein structures have not followed the same growth trend. Currently, the number of available protein sequences greatly exceeds the number of known 3D structures. Many computational methodologies, systems and algorithms have been proposed to address the protein structure prediction problem. However, the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. The most significant progress in the last Critical Assessment of protein Structure Prediction was achieved by methods that use database information. Nevertheless, a major challenge remains in the development of better strategies for template identification and representation. This article describes a computational strategy to acquire and represent structural information of experimentally determined 3D protein structures. A clustering strategy was combined with artificial neural networks in order to extract structural information from experimental protein structure templates. In the proposed strategy, the main efforts focus on the acquisition of useful and accurate structural information from 3D protein templates stored in the Protein Data Bank (PDB). The proposed method was tested in twenty protein sequences whose sizes vary from 14 to 70 amino acid residues. Our results show that the proposed method is a good way to extract and represent valuable information obtained from the PDB and also significantly reduce the 3D protein conformational search space.

A knowledge-based genetic algorithm to predict three-dimensional structures of polypeptides

Three-dimensional (3-D) protein structure determination has become an important area of research in structural bioinformatics. Proteins are responsible for the execution of different functions in the cell. Understanding the 3-D structure provides important information about the protein function. Many computational methodologies for the protein structure prediction were developed along the last 20 years, but the problem still challenges researchers because the complexity and high dimensionality of its large search space. In this article we present a strategy for reducing the search space explored by heuristic methods for solving the problem taken into consideration previous occurrences of amino acid residues in a well known protein database (PDB). We propose a genetic algorithm that takes advantages of this kind of information, reducing considerable the search space, allowing the algorithm to save time with less promising solutions. A simple Local Search operator helps the GA to intensify the search of the 3-D protein conformational space. We demonstrate the effectiveness of the strategy with a set of experimental results.