Reciprocal Attachment Graph Models with Applications to Protein Folding

• Activity Number: 568
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Amy Wagaman*+
• Companies: Amherst College
• Keywords: small-world graphs ; graph generation ; contact distribution

Abstract

Many graph generation methods exist to create graphs satisfying a variety of properties. These include models for scale-free graphs, small-world graphs, and other more complicated models. However, when modeling protein conformational networks (the three dimensional structure of a folded protein), the current models do not accurately capture some key properties of the graphs, including their contact distribution. In particular, the issues are related to dealing with neighborhoods of the nodes brought together in long-range connections. In this article, we propose a reciprocal attachment method to generate graphs where the relevant neighborhoods are merged appropriately upon the addition of long-range connections. The resulting model improves upon existing models and generates graphs that resemble protein conformational graphs by successfully capturing their contact distribution. We demonstrate the method and show the improvements via simulations and with comparison to real protein graphs. The reciprocal attachment model may also be applied to social networks, with some limitations. Applications to social networks are still ongoing.