  Proteins are essential for maintaining life. For example, knowing the
structure of a protein, cell regulatory mechanisms of organisms can be modeled,
supporting the development of disease treatments or the understanding of
relationships between protein structures and food attributes. However,
discovering the structure of a protein can be a difficult and expensive task,
since it is hard to explore the large search to predict even a small protein.
Template-based methods (coarse-grained, homology, threading etc) depend on
Prior Knowledge (PK) of proteins determined using other methods as X-Ray
Crystallography or Nuclear Magnetic Resonance. On the other hand, template-free
methods (full-atom and ab initio) rely on atoms physical-chemical properties to
predict protein structures. In comparison with other approaches, the Estimation
of Distribution Algorithms (EDAs) can require significant less PK, suggesting
that it could be adequate for proteins of low-level of PK. Finding an EDA able
to handle both prediction quality and computational time is a difficult task,
since they are strong inversely correlated. We developed an EDA specific for
the ab initio Protein Structure Prediction (PSP) problem using full-atom
representation. We developed one univariate and two bivariate probabilistic
models in order to design a proper EDA for PSP. The bivariate models make
relationships between dihedral angles $\phi$ and $\psi$ within an amino acid.
Furthermore, we compared the proposed EDA with other approaches from the
literature. We noticed that even a relatively simple algorithm such as Random
Walk can find the correct solution, but it would require a large amount of
prior knowledge (biased prediction). On the other hand, our EDA was able to
correctly predict with no prior knowledge at all, characterizing such a
prediction as pure ab initio.
