F
Federico Morán
Researcher at Complutense University of Madrid
Publications - 90
Citations - 3302
Federico Morán is an academic researcher from Complutense University of Madrid. The author has contributed to research in topics: Circular dichroism & Artificial neural network. The author has an hindex of 22, co-authored 88 publications receiving 3152 citations. Previous affiliations of Federico Morán include NASA Astrobiology Institute & Spanish National Research Council.
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Evaluation of secondary structure of proteins from UV circular dichroism spectra using an unsupervised learning neural network
TL;DR: An optimized self-organizing map algorithm has been used to obtain protein topological (proteinotopic) maps and analysis of the proteinotopic map reveals that the network extracts the main secondary structure features even with the small number of examples used.
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Reductive genome evolution in Buchnera aphidicola
Roeland C. H. J. van Ham,Judith Kamerbeek,Carmen Palacios,Carolina Rausell,Federico Abascal,Ugo Bastolla,José M. Fernández,Luis Jiménez,Marina Postigo,Francisco J. Silva,Javier Tamames,Enrique Viguera,Amparo Latorre,Alfonso Valencia,Federico Morán,Andrés Moya +15 more
TL;DR: A computational study of protein folding predicts that proteins in Buchnera, as well as proteins of other intracellular bacteria, are generally characterized by smaller folding efficiency compared with proteins of free living bacteria.
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Low-resolution structures of proteins in solution retrieved from X-ray scattering with a genetic algorithm.
TL;DR: Small-angle x-ray solution scattering (SAXS) is analyzed with a new method to retrieve convergent model structures that fit the scattering profiles, and the low-resolution solution structure of lysozyme has been directly modeled from its experimental SAXS profile.
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Proteinotopic feature maps
TL;DR: A system based on Kohonen's SOM (Self-Organizing Map) for protein classification according to Circular Dichroism (CD) spectra is described, and proteins with different secondary structures are clearly separated through a completely unsupervised training process.
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Reconstruction of protein form with X-ray solution scattering and a genetic algorithm.
TL;DR: A new genetic algorithm is designed which gradually explores a discrete search space and evolves convergent models made of several hundred beads best fitting the scattering profile upon Debye calculation, without geometrical constraints or penalty for loose beads.