Parallel Clustering of Protein Structures Generated via Stochastic Monte Carlo

The problem of efficient clustering of candidate protein structures into a limited number of groups is addressed. Such clustering can be expensive and is rarely used in practice due to its computational complexity. We present a parallel algorithm for the efficient clustering of proteins into groups. The input consists of thousands of candidate proteins structures that have been stochastically generated Monte-Carlo style. The first step is to make a Root Mean Square Deviation (RMSD) comparison matrix. The second step is to utilize parallel processors to calculate a hierarchal cluster of these proteins based on the RMSD matrix and using the Lance-Williams update algorithm. The final output is a Dendrogram of clusters. We have implemented our algorithm and have found it to be scalable.