A maximum likelihood framework for protein design

Background: The aim of protein design is to predict amino-acid sequences compatible with a given target structure. Traditionally envisioned as a purely thermodynamic question, this problem can also be understood in a wider context, where additional constraints are captured by learning the sequence patterns displayed by natural proteins of known conformation. In this latter perspective, however, we still need a theoretical formalization of the question, leading to general and efficient learning methods, and allowing for the selection of fast and accurate objective functions quantifying sequence/structure compatibility. Results: We propose a formulation of the protein design problem in terms of model-based statistical inference. Our framework uses the maximum likelihood principle to optimize the unknown parameters of a statistical potential, which we call an inverse potential to contrast with classical potentials used for structure prediction. We propose an implementation based on Marko...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Claudia L. Kleinman, Nicolas Rodrigue, Cécile Bonnard, Hervé Philippe, Nicolas Lartillot
• Keywords: BMCBI 2006, Markov Chain Monte Carlo, Model-based Statistical Inference, Protein Design