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The performance of
methods for protein fold prediction strongly depends on two components: an energy function that
is expected to have a stable minimum near the native structure and a method for finding this minimum through the protein
conformational space. Monte Carlo replica exchange methods provided a major advance to the latter point, outperforming widely
used methods such as the original Monte Carlo Simulated annealing in terms of sampling low-energy states. The Hamiltonian
Replica Exchange is one of the most promising among these novel methods. It is accepted that homologous proteins have
similar native structures, thus their energy landscapes and therefore their hamiltonians must be similar, at least nearby such
conformation. This allowed us to develop, starting from a standard ab-initio protocol implemented using the Rosetta modeling
suite, a Replica Exchange method in which the hamiltonians of homologous proteins are used to characterize the replicas. In
a pilot study on 40 different domains selected from the SCOP database, we observe that our method is able to improve the
exploration of energy landscapes thanks to the exchange of homology information during the Monte Carlo simulation and to
reach better solutions in terms of both lower energy and distance from the native structure. These preliminary results show that
our novel approach to use homology information can greatly improve the performance of
methods for protein structure
prediction, both overcoming errors and approximation in the energy functions, improving the ability of the Monte Carlo method
to avoid spurious local minima and allowing for a more efficient search of the conformational space.
Alfredo Iacoangeli is a Ph.D student at the Molecular Medicine department of the University of Rome, "La Sapienza," Italy. He obtained both his Bachelor's
degree (2010) in Physics and his Master's Degree (2012) in BioPhysics at the University of Rome, "La Sapienza," Italy. During his studies he spent one
year in the UK at the University of Birmingham, joining an exchange programme, and one year in Spain at the University of Grana
da as a visiting student.
During the final year of his Master's Degree (Sept-2011) he joined the Biocomputing Group (www.biocomputing.it) of Prof. Anna Tramontano, in which he
is still working as a Ph.D student (since Nov-2012).
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