Pablo de Olavide University. Building 44. Lab. 44.B.01. Seville. Spain.
amarcha@upo.es
Welcome to my publications web page. I am a Msc. in Computer Science and PhD. Student at Pablo de Olavide University (Seville, Spain). My researching areas are Bioinformatics, Protein Structure Prediction, Data Mining and Evolutionary Computation.
Abstract: In this study, a novel residue-residue contacts prediction approach based on evolutionary computation is presented. The prediction is based on four amino acids properties. In particular, we consider the hydrophobicity, the polarity, the charge and residues size. The prediction model consists of a set of rules that identifies contacts between amino acids.
Abstract: Protein structure prediction is one of the main challenges in Bioinformatics. An useful representation for protein 3D structure is the protein contact map. In this work, we propose an evolutionary approach for contact map prediction based on amino acids physicochemical properties. The evolutionary algorithm produces a set of rules that identifies contacts between amino acids. The rules obtained by the algorithm imposes a set of conditions on four amino acid properties in order to predict contacts. Results obtained confirm the validity of the proposal.
Abstract: We have developed an evolutionary computation approach to predict
secondary structure motifs using some main amino acid
physical-chemical properties. The prediction model will consist of
rules that predict both the beginning and the end of the regions
corresponding to a secondary structure state conformation
alpha-helix or beta-strand). A study about propensities of
each pair of amino acids in capping regions of alpha-helix and
beta-strand are also performed with a data set of 12,830
non-homologous and non-redundant protein sequences.
Abstract: Protein tertiary structure prediction consists of determining the three-dimensional conformation of a protein 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. Several existing protein tertiary structure prediction methods produce contact maps as their output. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. In addition, many existing approaches use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, we used three different physicochemical properties of amino acids obtained from the literature. Using this method, we performed tertiary structure predictions on 63 viral capsid proteins with a maximum identity of 30% obtained from the Protein Data Bank. We achieved a precision of 0.75 with an 8-angstrom cut-off and a minimum sequence separation of 7 amino acids. Thus, for the studied proteins, our results provide a notable improvement over those of other methods.
Abstract: 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.
Abstract: 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.
Abstract: 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 approach considers various amino acids properties in order to predict the secondary structure of a protein. In particular, we will consider the hydrophobicity, the polarity and the charge of amino acids.
In this study, we focus on predicting a particular kind of secondary structure: alpha-helices.
The results of our proposal will be a set of rules that will identify the beginning or an end of such a structure.
