DOCK/PIERR: web server for structure prediction of protein-protein complexes.

In protein docking we aim to find the structure of the complex formed when two proteins interact. Protein-protein interactions are crucial for cell function. Here we discuss the usage of DOCK/PIERR.

Improving ranking of models for protein complexes with side chain modeling and atomic potentials.

An atomically detailed potential for docking pairs of proteins is derived using mathematical programming. A refinement algorithm that builds atomically detailed models of the complex and combines coarse grained and atomic scoring is introduced. The refinement step consists of remodeling the interface side chains of the top scoring decoys from rigid docking followed by a short energy minimization.

Multi-domain GFP-like proteins from two species of marine hydrozoans.

Proteins homologous to Green Fluorescent Protein (GFP) are widely used as genetically encoded fluorescent labels. Many developments of this technology were spurred by discoveries of novel types of GFP-like proteins (FPs) in nature. Here we report two proteins displaying primary structures never before encountered in natural FPs: they consist of multiple GFP-like domains repeated within the same polypeptide chain.

Energy design for protein-protein interactions.

Proteins bind to other proteins efficiently and specifically to carry on many cell functions such as signaling, activation, transport, enzymatic reactions, and more. To determine the geometry and strength of binding of a protein pair, an energy function is required. An algorithm to design an optimal energy function, based on empirical data of protein complexes, is proposed and applied.

PIE-efficient filters and coarse grained potentials for unbound protein-protein docking.

Identifying correct binding modes in a large set of models is an important step in protein-protein docking. We identified protein docking filter based on overlap area that significantly reduces the number of candidate structures that require detailed examination. We also developed potentials based on residue contacts and overlap areas using a comprehensive learning set of 640 two-chain protein complexes with mathematical programming.