A protein is identified by a finite sequence of amino acids,
each of them chosen from a set of 20 elements. The Protein
Structure Prediction Problem is the problem of predicting
the 3D native conformation of a protein, when its sequence
of amino acids is known. This problem is fundamental for
biological and pharmaceutical research. All current math-
ematical models of the problem are affected by intrinsic
computational limits. In particular, simulation-based tech-
niques that handle every chemical interaction between all
atoms in the amino acids (and the solvent) are not feasible
due to the huge amount of computations involved. These
programs are typically written in imperative languages and
each approach is based on a particular energy function.
There is no common agreement on which is the most re-
liable energy function to be used.
In this paper we present a novel agent-based framework
for ab-initio simulations. Each amino acid of an input pro-
tein is viewed as an independent agent that communicates
with the others. The framework allows a modular repre-
sentation of the problem and it is easily extensible for fur-
ther refinements and for different energy functions. Sim-
ulations at this level of abstraction allow fast calculation,
distributed on each agent. We provide an implementation
using the Linda package of SICStus Prolog, to show the
feasibility and the power of the method. The code is in-
trinsically concurrent and thus natural to be parallelized.
