  Protein Structure Prediction (PSP) is an unsolved problem in the field of
computational biology. The problem of protein structure prediction is about
predicting the native conformation of a protein, while its sequence of amino
acids is known. Regarding processing limitations of current computer systems,
all-atom simulations for proteins are typically unpractical; several reduced
models of proteins have been proposed. Additionally, due to intrinsic hardness
of calculations even in reduced models, many computational methods mainly based
on artificial intelligence have been proposed to solve the problem. Agent-based
modeling is a relatively new method for modeling systems composed of
interacting items. In this paper we proposed a new approach for protein
structure prediction by using agent-based modeling (ABM) in two dimensional
hydrophobic-hydrophilic model. We broke the whole process of protein structure
prediction into two steps: the first step, which was introduced in our previous
paper, is about biasing the linear sequence to gain a primary energy, and the
next step, which will be explained in this paper, is about using ABM with a
predefined set of rules, to find the best conformation in the least possible
amount of time and steps. This method was implemented in NETLOGO. We have
tested this algorithm on several benchmark sequences ranging from 20 to 50-mers
in two dimensional Hydrophobic-Hydrophilic lattice models. Comparing to the
result of the other algorithms, our method is capable of finding the best known
conformations in a significantly shorter time. A major problem in PSP
simulation is that as the sequence length increases the time consumed to
predict a valid structure will exponentially increase. In contrast, by using
MAS2HP the effect of increase in sequence length on spent time has changed from
exponentially to linear.
