Biological complexity: ant colony meta-heuristic optimization algorithm for protein folding

Ant colony meta-heuristic optimization (ACO) is one of the few algorithms that can help to gain an atomic level insight into the conformation of protein folding states, intermediate weights and pheromones present along the protein folding pathway. These are analysed by nodes (amino acids), and these nodes depend upon the probability of next optimized node (amino acids). Nodes have conformational degrees of freedom as well as depend upon the natural factors and collective behaviour of biologically important molecules like temperature, volume, pressure and other ensembles. This biological quantum complexity can be resolved using ACO algorithm. Ants are visually blind and important behaviour of communication among individuals or colony of ant environment is based on chemicals (pheromones) deposited by the ants. Just like ants, proteins are also a group of colony; amino acids are node (amino acid) attached to each others with the help of bonds. This paper is aimed to determine the factors affecting protein folding pattern using ant colony algorithm. Protein occurs structurally in a compact form and determining the ways of protein folding is called NP hard (non-deterministic polynomial-time hard) problem. Using the ACO, we have developed an algorithm for protein folding. It is interesting to note that based on ants ability to find new shorter path between the nest and the food, proteins can also be optimized for shorter path between one node to another node and the folding pattern can be predicted for an unknown protein (ab initio). We have developed an application based on ACO in Perl language (PFEBRT) for determining optimized folding path of proteins.