Welcome to my publications web page. I am a Msc. in Computer Science and PhD. Student at Pablo de Olavide University (Seville, Spain). I research in Bioinformatics and my investigation are focused in Protein Structure Prediction and Data Mining.
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 in 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, we used a set of 30 physicochemical amino acid properties selected from the AAindex database. 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. We performed several preliminary analysis of the protein physicochemical data distributions using 3D surfaces. Three main pattern types were found in 3D surfaces, thus it is possible to extract rules in order to predict distances between amino acids according to their physicochemical properties. We performed an experimental validation of our method using five non-homologous protein sets and we showed the generality of this method and its prediction quality using the amino acid properties considered. Finally, we included a study of the algorithm efficiency according to the number of most similar fragments considered and we notably improved the precision with the studied proteins sets.
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: -helices.
The results of our proposal will be a set of rules that will identify the
beginning or an end of such a structure.
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: In this paper, we focus on protein contact map prediction.
We describe a method where contact maps are predicted using decision
tree-based model. The algorithm includes the subsequence information
between the couple of analyzed amino acids. In order to evaluate the
method generalization capabilities, we carry out an experiment using
173 non-homologous proteins of known structures. Our results indicate
that the method can assign protein contacts with an average accuracy of
0.34, superior to the 0.25 obtained by the FNETCSS method. This shows
that our algorithm improves the accuracy with respect to the methods
compared, especially with the increase of protein length.
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 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: 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: En este trabajo se ha estudiado c´omo influye la separaci´on
entre amino´acidos de una prote´ına y ciertas caracter´ısticas inherentes a
su naturaleza en la predicci´on de estructuras de prote´ınas mediante un
algoritmo de vecinos m´as cercanos. En el proceso de predicci´on se han
generado todas las subsecuencias posibles de amino´acidos procedentes
de un conjunto de prote´ınas reales. Se demuestra emp´ıricamente que la
separaci´on entre amino´acidos produce peores predicciones que las propiedades
naturales inherentes a los mismos. Esto plantea la hip´otesis de
que la informaci´on que suministra dicha separaci´on se encuentra impl´ıcita
en la informaci´on proporcionada por las propiedades f´ısico-qu´ımicas,
ya que se han obtenido iguales resultados tanto en presencia como en
ausencia del atributo separaci´on.
Abstract: La predicción de la estructura de las proteÃnas consiste en determinar la estructura de las mismas únicamente a partir de su secuencia de aminoácidos. En el presente trabajo se han estudiado las principales aproximaciones algorÃtmicas existentes para llevar a cabo la tarea de predicción de estructuras de proteÃnas. Se ha aportado una solución para este problema de predicción que utiliza el esquema de vecinos más cercanos junto a una selección de propiedades fÃsico-quÃmicas de los aminoácidos. Se han realizado cuatro experimentos y se han analizado los resultados. Finalmente, se han indicado las lÃneas de trabajo abiertas y las conclusiones del trabajo realizado.
