36,501 research outputs found
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 AndalucÃa P07-TIC-02611Ministerio de Ciencia y TecnologÃa TIN2007-68084-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
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 AndalucÃa P07-TIC-02611Ministerio de Educación y Ciencia TIN2011-28956-C02-0
New evolutionary approaches to protein structure prediction
Programa de doctorado en BiotecnologÃa y TecnologÃa QuÃmicaThe 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
Inter-protein sequence co-evolution predicts known physical interactions in bacterial ribosomes and the trp operon
Interaction between proteins is a fundamental mechanism that underlies
virtually all biological processes. Many important interactions are conserved
across a large variety of species. The need to maintain interaction leads to a
high degree of co-evolution between residues in the interface between partner
proteins. The inference of protein-protein interaction networks from the
rapidly growing sequence databases is one of the most formidable tasks in
systems biology today. We propose here a novel approach based on the
Direct-Coupling Analysis of the co-evolution between inter-protein residue
pairs. We use ribosomal and trp operon proteins as test cases: For the small
resp. large ribosomal subunit our approach predicts protein-interaction
partners at a true-positive rate of 70% resp. 90% within the first 10
predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all
predictions. In the trp operon, it assigns the two largest interaction scores
to the only two interactions experimentally known. On the level of residue
interactions we show that for both the small and the large ribosomal subunit
our approach predicts interacting residues in the system with a true positive
rate of 60% and 85% in the first 20 predictions. We use artificial data to show
that the performance of our approach depends crucially on the size of the joint
multiple sequence alignments and analyze how many sequences would be necessary
for a perfect prediction if the sequences were sampled from the same model that
we use for prediction. Given the performance of our approach on the test data
we speculate that it can be used to detect new interactions, especially in the
light of the rapid growth of available sequence data
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