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. In DOCK/PIERR, a uniformly discrete sampling of orientations of one protein with respect to the other, are scored, followed by clustering, refinement, and reranking of structures. The novelty of this method lies in the scoring functions used. These are obtained by examining hundreds of millions of correctly and incorrectly docked structures, using an algorithm based on mathematical programming, with provable convergence properties.

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. The refined models are then re-ranked using a combination of coarse grained and atomic potentials. The docking algorithm including the refinement and re-ranking, is compared favorably to other leading docking packages like ZDOCK, Cluspro, and PATCHDOCK, on the ZLAB 3.0 Benchmark and a test set of 30 novel complexes. A detailed analysis shows that coarse grained potentials perform better than atomic potentials for realistic unbound docking (where the exact structures of the individual bound proteins are unknown), probably because atomic potentials are more sensitive to local errors. Nevertheless, the atomic potential captures a different signal from the residue potential and as a result a combination of the two scores provides a significantly better prediction than each of the approaches alone.

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. A two-domain green FP (abeGFP) and a four-domain orange-fluorescent FP (Ember) were isolated from the siphonophore Abylopsis eschscholtzii and an unidentified juvenile jellyfish (order Anthoathecata), respectively. Only the most evolutionary ancient domain of Ember is able to synthesize an orange-emitting chromophore (emission at 571 nm), while the other three are purely green (emission at 520 nm) and putatively serve to maintain the stability and solubility of the multidomain protein. When expressed individually, two of the green Ember domains form dimers and the third one exists as a monomer. The low propensity for oligomerization of these domains would simplify their adoption as in vivo labels. Our results reveal a previously unrecognized direction in which natural FPs have diversified, suggesting new avenues to look for FPs with novel and potentially useful features.

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. Emphasis is made on negative design in which incorrect geometries are presented to the algorithm that learns to avoid them. For the docking problem the search for plausible geometries can be performed exhaustively. The possible geometries of the complex are generated on a grid with the help of a fast Fourier transform algorithm. A novel formulation of negative design makes it possible to investigate iteratively hundreds of millions of negative examples while monotonically improving the quality of the potential. Experimental structures for 640 protein complexes are used to generate positive and negative examples for learning parameters. The algorithm designed in this work finds the correct binding structure as the lowest energy minimum in 318 cases of the 640 examples. Further benchmarks on independent sets confirm the significant capacity of the scoring function to recognize correct modes of interactions.

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. Our potential showed substantially better recognition capacity compared to other publicly accessible protein docking potentials in discriminating between native and nonnative binding modes on a large test set of 84 complexes independent of our training set. We were able to rank a near-native model on the top in 43 cases and within top 10 in 51 cases. We also report an atomic potential that ranks a near-native model on the top in 46 cases and within top 10 in 58 cases. Our filter+potential is well suited for selecting a small set of models to be refined to atomic resolution.