Ora Schueler-Furman

Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations

Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 10(5) independent simulations are carried out, and the resulting decoys are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.

Improved side-chain modeling for protein-protein docking.

Success in high‐resolution protein–protein docking requires accurate modeling of side‐chain conformations at the interface. Most current methods either leave side chains fixed in the conformations observed in the unbound protein structures or allow the side chains to sample a set of discrete rotamer conformations. Here we describe a rapid and efficient method for sampling off‐rotamer side‐chain conformations by torsion space minimization during protein–protein docking starting from discrete rotamer libraries supplemented with side‐chain conformations taken from the unbound structures, and show that the new method improves side‐chain modeling and increases the energetic discrimination between good and bad models. Analysis of the distribution of side‐chain interaction energies within and between the two protein partners shows that the new method leads to more native‐like distributions of interaction energies and that the neglect of side‐chain entropy produces a small but measurable increase in the number of residues whose interaction energy cannot compensate for the entropic cost of side‐chain freezing at the interface. The power of the method is highlighted by a number of predictions of unprecedented accuracy in the recent CAPRI (Critical Assessment of PRedicted Interactions) blind test of protein–protein docking methods.

Rosetta Predictions in CASP5: Successes, Failures, and Prospects for Complete Automation

We describe predictions of the structures of CASP5 targets using Rosetta. The Rosetta fragment insertion protocol was used to generate models for entire target domains without detectable sequence similarity to a protein of known structure and to build long loop insertions (and N‐and C‐terminal extensions) in cases where a structural template was available. Encouraging results were obtained both for the de novo predictions and for the long loop insertions; we describe here the successes as well as the failures in the context of current efforts to improve the Rosetta method. In particular, de novo predictions failed for large proteins that were incorrectly parsed into domains and for topologically complex (high contact order) proteins with swapping of segments between domains. However, for the remaining targets, at least one of the five submitted models had a long fragment with significant similarity to the native structure. A fully automated version of the CASP5 protocol produced results that were comparable to the human‐assisted predictions for most of the targets, suggesting that automated genomic‐scale, de novo protein structure prediction may soon be worthwhile. For the three targets where the human‐assisted predictions were significantly closer to the native structure, we identify the steps that remain to be automated. Proteins 2003;53:457–468.

Protein-protein docking predictions for the CAPRI experiment.

We predicted structures for all seven targets in the CAPRI experiment using a new method in development at the time of the challenge. The technique includes a low‐resolution rigid body Monte Carlo search followed by high‐resolution refinement with side‐chain conformational changes and rigid body minimization. Decoys (∼106 per target) were discriminated using a scoring function including van der Waals and solvation interactions, hydrogen bonding, residue–residue pair statistics, and rotamer probabilities. Decoys were ranked, clustered, manually inspected, and selected. The top ranked model for target 6 predicted the experimental structure to 1.5 Å RMSD and included 48 of 65 correct residue–residue contacts. Target 7 was predicted at 5.3 Å RMSD with 22 of 37 correct residue–residue contacts using a homology model from a known complex structure. Using a preliminary version of the protocol in round 1, target 1 was predicted within 8.8 Å although few contacts were correct. For targets 2 and 3, the interface locations and a small fraction of the contacts were correctly identified. Proteins 2003;52:118–122.

Progress in protein–protein docking: Atomic resolution predictions in the CAPRI experiment using RosettaDock with an improved treatment of side‐chain flexibility

RosettaDock uses real‐space Monte Carlo minimization (MCM) on both rigid‐body and side‐chain degrees of freedom to identify the lowest free energy docked arrangement of 2 protein structures. An improved version of the method that uses gradient‐based minimization for off‐rotamer side‐chain optimization and includes information from unbound structures was used to create predictions for Rounds 4 and 5 of CAPRI. First, large numbers of independent MCM trajectories were carried out and the lowest free energy docked configurations identified. Second, new trajectories were started from these lowest energy structures to thoroughly sample the surrounding conformation space, and the lowest energy configurations were submitted as predictions. For all cases in which there were no significant backbone conformational changes, a small number of very low‐energy configurations were identified in the first, global search and subsequently found to be close to the center of the basin of attraction in the free energy landscape in the second, local search. Following the release of the experimental coordinates, it was found that the centers of these free energy minima were remarkably close to the native structures in not only the rigid‐body orientation but also the detailed conformations of the side‐chains. Out of 8 targets, the lowest energy models had interface root‐mean‐square deviations (RMSDs) less than 1.1 Å from the correct structures for 6 targets, and interface RMSDs less than 0.4 Å for 3 targets. The predictions were top submissions to CAPRI for Targets 11, 12, 14, 15, and 19. The close correspondence of the lowest free energy structures found in our searches to the experimental structures suggests that our free energy function is a reasonable representation of the physical chemistry, and that the real space search with full side‐chain flexibility to some extent solves the protein–protein docking problem in the absence of significant backbone conformational changes. On the other hand, the approach fails when there are significant backbone conformational changes as the steric complementarity of the 2 proteins cannot be modeled without incorporating backbone flexibility, and this is the major goal of our current work. Proteins 2005;60:187–194.

Conserved residue clustering and protein structure prediction

Protein residues that are critical for structure and function are expected to be conserved throughout evolution. Here, we investigate the extent to which these conserved residues are clustered in three‐dimensional protein structures. In 92% of the proteins in a data set of 79 proteins, the most conserved positions in multiple sequence alignments are significantly more clustered than randomly selected sets of positions. The comparison to random subsets is not necessarily appropriate, however, because the signal could be the result of differences in the amino acid composition of sets of conserved residues compared to random subsets (hydrophobic residues tend to be close together in the protein core), or differences in sequence separation of the residues in the different sets. In order to overcome these limits, we compare the degree of clustering of the conserved positions on the native structure and on alternative conformations generated by the de novo structure prediction method Rosetta. For 65% of the 79 proteins, the conserved residues are significantly more clustered in the native structure than in the alternative conformations, indicating that the clustering of conserved residues in protein structures goes beyond that expected purely from sequence locality and composition effects. The differences in the spatial distribution of conserved residues can be utilized in de novo protein structure prediction: We find that for 79% of the proteins, selection of the Rosetta generated conformations with the greatest clustering of the conserved residues significantly enriches the fraction of close‐to‐native structures. Proteins 2003;52:225–235.

The structure, dynamics, and energetics of protein adsorption—lessons learned from adsorption of statherin to hydroxyapatite

Proteins are found to be involved in interaction with solid surfaces in numerous natural events. Acidic proteins that adsorb to crystal faces of a biomineral to control the growth and morphology of hard tissue are only one example. Deducing the mechanisms of surface recognition exercised by proteins has implications to osteogenesis, pathological calcification and other proteins functions at their adsorbed state. Statherin is an enamel pellicle protein that inhibits hydroxyapatite nucleation and growth, lubricates the enamel surface, and is recognized by oral bacteria in periodontal diseases. Here, we highlight some of the insights we obtained recently using both thermodynamic and solid state NMR measurements to the adsorption process of statherin to hydroxyapatite. We combine macroscopic energy characterization with microscopic structural findings to present our views of protein adsorption mechanisms and the structural changes accompanying it and discuss the implications of these studies to understanding the functions of the protein adsorbed to the enamel surfaces. Copyright

RosettaDock in CAPRI rounds 6–12

A challenge in protein–protein docking is to account for the conformational changes in the monomers that occur upon binding. The RosettaDock method, which incorporates sidechain flexibility but keeps the backbone fixed, was found in previous CAPRI rounds (4 and 5) to generate docking models with atomic accuracy, provided that conformational changes were mainly restricted to protein sidechains. In the recent rounds of CAPRI (6–12), large backbone conformational changes occur upon binding for several target complexes. To address these challenges, we explicitly introduced backbone flexibility in our modeling procedures by combining rigid‐body docking with protein structure prediction techniques such as modeling variable loops and building homology models. Encouragingly, using this approach we were able to correctly predict a significant backbone conformational change of an interface loop for Target 20 (12 Å rmsd between those in the unbound monomer and complex structures), but accounting for backbone flexibility in protein–protein docking is still very challenging because of the significantly larger conformational space, which must be surveyed. Motivated by these CAPRI challenges, we have made progress in reformulating RosettaDock using a “fold‐tree” representation, which provides a general framework for treating a wide variety of flexible‐backbone docking problems. Proteins 2007.