Refinement of rigid-body protein–protein docking using backbone and side-chain minimization with a coarse-grained model

Abstract:

Understanding protein–protein recognition is one of the main goals in structural biology. Most of the key biological processes involve the formation of specific protein complexes, for which a detailed structural knowledge is essential to understand the mechanism of protein association and their functional implications. Computational docking methods are currently able to predict the structure of a protein-protein complex with a high degree of accuracy in some cases. However, in the majority of cases, with conformational movements upon binding, we have to go beyond the current rigid-body approach and introduce flexibility. Given the difficulties of using full-atom descriptions during flexible docking, we need to focus our efforts in coarse-grain models. Here, we have implemented and tested a version of the united residue (UNRES) forcefield for protein–protein docking refinement. The results indicate improvement in the geometry of the docking solutions, and better docking energy landscapes, although in general, the scoring did not improve with respect to rigid-body pyDock function. However, as opposed to other scoring algorithms, the UNRES scoring does not seem to be biased towards cases that are over-represented in the structural databases (typically enzyme-inhibitor and antibodyantigen cases). This consistency among all types of complexes suggests its use as a solid basis for developing better unbiased scoring methods.

Keywords::

• molecular recognition
• structural prediction
• protein–protein association
• global energy