Skip to Main content Skip to Navigation
New interface
Journal articles

Predicting Multi-component Protein Assemblies Using an Ant Colony Approach

Vishwesh Venkatraman 1 David Ritchie 1 
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Many biological processes are governed by large assemblies of protein molecules. However, it is often very difficult to determine the three-dimensional structures of these assemblies using experimental biophysical techniques. Hence there is a need to develop computational approaches to fill this gap. This article presents an ant colony optimization approach to predict the structure of large multi-component protein complexes. Starting from pair-wise docking predictions, a multi-graph consisting of vertices representing the component proteins and edges representing candidate interactions is constructed. This allows the assembly problem to be expressed in terms of searching for a minimum weight spanning tree. However, because the problem remains highly combinatorial, the search space cannot be enumerated exhaustively and therefore heuristic optimisation techniques must be used. The utility of the ant colony based approach is demonstrated by re-assembling known protein complexes from the Protein Data Bank. The algorithm is able to identify near-native solutions for five of the six cases tested. This demonstrates that the ant colony approach provides a useful way to deal with the highly combinatorial multi-component protein assembly problem.
Complete list of metadata
Contributor : David Ritchie Connect in order to contact the contributor
Submitted on : Friday, November 23, 2012 - 5:34:06 PM
Last modification on : Thursday, August 4, 2022 - 5:18:44 PM

Links full text



Vishwesh Venkatraman, David Ritchie. Predicting Multi-component Protein Assemblies Using an Ant Colony Approach. International Journal of Swarm Intelligence Research, 2012, 3, pp.19-31. ⟨10.4018/jsir.2012070102⟩. ⟨hal-00756807⟩



Record views