313 research outputs found
Is protein folding problem really a NP-complete one ? First investigations
To determine the 3D conformation of proteins is a necessity to understand
their functions or interactions with other molecules. It is commonly admitted
that, when proteins fold from their primary linear structures to their final 3D
conformations, they tend to choose the ones that minimize their free energy. To
find the 3D conformation of a protein knowing its amino acid sequence,
bioinformaticians use various models of different resolutions and artificial
intelligence tools, as the protein folding prediction problem is a NP complete
one. More precisely, to determine the backbone structure of the protein using
the low resolution models (2D HP square and 3D HP cubic), by finding the
conformation that minimize free energy, is intractable exactly. Both the proof
of NP-completeness and the 2D prediction consider that acceptable conformations
have to satisfy a self-avoiding walk (SAW) requirement, as two different amino
acids cannot occupy a same position in the lattice. It is shown in this
document that the SAW requirement considered when proving NP-completeness is
different from the SAW requirement used in various prediction programs, and
that they are different from the real biological requirement. Indeed, the proof
of NP completeness and the predictions in silico consider conformations that
are not possible in practice. Consequences of this fact are investigated in
this research work.Comment: Submitted to Journal of Bioinformatics and Computational Biology,
under revie
Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures
The protein-folding problem has been extensively studied during the last
fifty years. The understanding of the dynamics of global shape of a protein and the influence
on its biological function can help us to discover new and more effective
drugs to deal with diseases of pharmacological relevance. Different computational approaches
have been developed by different researchers in order to foresee the threedimensional
arrangement of atoms of proteins from their sequences. However, the
computational complexity of this problem makes mandatory the search for new models,
novel algorithmic strategies and hardware platforms that provide solutions in a
reasonable time frame. We present in this revision work the past and last tendencies
regarding protein folding simulations from both perspectives; hardware and software.
Of particular interest to us are both the use of inexact solutions to this computationally hard problem as
well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciĂłnSĂ©neca (Agencia Regional de Ciencia y TecnologĂa, RegiĂłn de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.IngenierĂa, Industria y ConstrucciĂł
On the role of metaheuristic optimization in bioinformatics
Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics
From evolutionary computation to the evolution of things
Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems
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