A simple physical model for binding energy hot spots in protein-protein complexes

Computational redesign of protein-protein interaction specificity

We developed a 'computational second-site suppressor' strategy to redesign specificity at a protein-protein interface and applied it to create new specifically interacting DNase-inhibitor protein pairs. We demonstrate that the designed switch in specificity holds in in vitro binding and functional assays. We also show that the designed interfaces are specific in the natural functional context in living cells, and present the first high-resolution X-ray crystallographic analysis of a computer-redesigned functional protein-protein interface with altered specificity. The approach should be applicable to the design of interacting protein pairs with novel specificities for delineating and re-engineering protein interaction networks in living cells.

Computational alanine scanning of protein-protein interfaces

Protein-protein interactions are key components of all signal transduction processes, so methods to alter these interactions promise to become important tools in dissecting function of connectivities in these networks. We have developed a fast computational approach for the prediction of energetically important amino acid residues in protein-protein interfaces (available at http://robetta.bakerlab.org/alaninescan), which we, following Peter Kollman, have termed "computational alanine scanning." The input consists of a three-dimensional structure of a protein-protein complex; output is a list of "hot spots," or amino acid side chains that are predicted to significantly destabilize the interface when mutated to alanine, analogous to the results of experimental alanine-scanning mutagenesis. 79% of hot spots and 68% of neutral residues were correctly predicted in a test of 233 mutations in 19 protein-protein complexes. A single interface can be analyzed in minutes. The computational methodology has been validated by the successful design of protein interfaces with new specificity and activity, and has yielded new insights into the mechanisms of receptor specificity and promiscuity in biological systems.

Computational design of protein–protein interactions

Computational protein design strategies have been developed to reengineer protein-protein interfaces in an automated, generalizable fashion. In the past two years, these methods have been successfully applied to generate chimeric proteins and protein pairs with specificities different from naturally occurring protein-protein interactions. Although there are shortcomings in current approaches, both in the way conformational space is sampled and in the energy functions used to evaluate designed conformations, the successes suggest we are now entering an era in which computational methods can be used to modulate, reengineer and design protein-protein interaction networks in living cells.

ProTherm, version 4.0: thermodynamic database for proteins and mutants

Release 4.0 of ProTherm, thermodynamic database for proteins and mutants, contains approximately 14,500 numerical data (approximately 450% of the first version) of several thermodynamic parameters along with experimental methods and conditions, and structural, functional and literature information. The sequence and structural information of proteins is connected with thermodynamic data through links between entries in Protein Data Bank, Protein Information Resource and SWISS-PROT and the data in ProTherm. We have separated the Gibbs free energy change obtained at extrapolated temperature from the data on denaturation temperature measured by the thermal denaturation method. We have added the statistics of amino acid replacements and links to homologous structures to each protein. Further, we have improved the search and display options to enhance search capability through the web interface. ProTherm is freely available at http://gibk26. bse.kyutech.ac.jp/jouhou/Protherm/protherm.html.

Convergent mechanisms for recognition of divergent cytokines by the shared signaling receptor gp130

Gp130 is a shared cell-surface signaling receptor for at least ten different hematopoietic cytokines, but the basis of its degenerate recognition properties is unknown. We have determined the crystal structure of human leukemia inhibitory factor (LIF) bound to the cytokine binding region (CHR) of gp130 at 2.5 A resolution. Strikingly, we find that the shared binding site on gp130 has an entirely rigid core, while the LIF binding interface diverges sharply in structure and chemistry from that of other gp130 ligands. Dissection of the LIF-gp130 interface, along with comparative studies of other gp130 cytokines, reveal that gp130 has evolved a "thermodynamic plasticity" that is relatively insensitive to ligand structure, to enable crossreactivity. These observations reveal a novel and alternative mechanism for degenerate recognition from that of structural plasticity.

Computational detection of the binding-site hot spot at the remodeled human growth hormone-receptor interface

A hierarchical computational approach is used to identify the engineered binding-site cavity at the remodeled intermolecular interface between the mutants of human growth hormone (hGH) and the extracellular domain of its receptor (hGHbp). Multiple docking simulations are conducted with the remodeled hGH-hGHbp complex for a panel of potent benzimidazole-containing inhibitors that can restore the binding affinity of the wild-type complex, and for a set of known nonactive small molecules that contain different heterocyclic motifs. Structural clustering of ligand-bound conformations and binding free-energy calculations, using the AMBER force field and a continuum solvation model, can rapidly locate and screen numerous ligand-binding modes on the protein surface and detect the binding-site hot spot at the intermolecular interface. Structural orientation of the benzimidazole motif in the binding-site cavity closely mimics the position of the hot spot residue W104 in the crystal structure of the wild-type complex, which is recognized as an important structural requirement for restoring binding affinity. Despite numerous pockets on the protein surface of the mutant hGH-hGHbp complex, the binding-site cavity presents the energetically favorable hot spot for the benzimidazole-containing inhibitors, whereas for a set of nonactive molecules, the lowest energy ligand conformations do not necessarily bind in the engineered cavity. The results reveal a dominant role of the intermolecular van der Waals interactions in providing favorable ligand-protein energetics in the redesigned interface, in agreement with the experimental and computational alanine scanning of the hGH-hGHbp complex.

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.

Calculation of mutational free energy changes in transition states for protein folding

Recent advances in experimental and computational methods have made it possible to determine with considerable accuracy the structures whose formation is rate limiting for the folding of some small proteins-the transition state ensemble, or TSE. We present a method to analyze and validate all-atom models of such structures. The method is based on the comparison of experimental data with the computation of the change in free energy of the TSE resulting from specific mutations. Each mutation is modeled individually in all members of an ensemble of transition state structures using a method originally developed to predict mutational changes in the stability of native proteins. We first apply this method to six proteins for which we have determined the TSEs with a technique that uses experimental mutational data (Phi-values) as restraints in the structure determination and find a highly significant correlation between the calculated free energy changes and those derived from experimental kinetic data. We then use the procedure to analyze transition state structures determined by molecular dynamics simulations of unfolding, again finding a high correlation. Finally, we use the method to estimate changes in folding rates of several hydrophobic core mutants of Fyn SH3. Taken together, these results show that the procedure developed here is a tool of general validity for analyzing, assessing, and improving the quality of the structures of transition states for protein folding.

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 (approximately 10(6) 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 A RMSD and included 48 of 65 correct residue-residue contacts. Target 7 was predicted at 5.3 A 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 A although few contacts were correct. For targets 2 and 3, the interface locations and a small fraction of the contacts were correctly identified.

Protein-protein interactions: structurally conserved residues distinguish between binding sites and exposed protein surfaces

Polar residue hot spots have been observed at protein-protein binding sites. Here we show that hot spots occur predominantly at the interfaces of macromolecular complexes, distinguishing binding sites from the remainder of the surface. Consequently, hot spots can be used to define binding epitopes. We further show a correspondence between energy hot spots and structurally conserved residues. The number of structurally conserved residues, particularly of high ranking energy hot spots, increases with the binding site contact size. This finding may suggest that effectively dispersing hot spots within a large contact area, rather than compactly clustering them, may be a strategy to sustain essential key interactions while still allowing certain protein flexibility at the interface. Thus, most conserved polar residues at the binding interfaces confer rigidity to minimize the entropic cost on binding, whereas surrounding residues form a flexible cushion. Furthermore, our finding that similar residue hot spots occur across different protein families suggests that affinity and specificity are not necessarily coupled: higher affinity does not directly imply greater specificity. Conservation of Trp on the protein surface indicates a highly likely binding site. To a lesser extent, conservation of Phe and Met also imply a binding site. For all three residues, there is a significant conservation in binding sites, whereas there is no conservation on the exposed surface. A hybrid strategy, mapping sequence alignment onto a single structure illustrates the possibility of binding site identification around these three residues.

Symmetry recognizing asymmetry: analysis of the interactions between the C-type lectin-like immunoreceptor NKG2D and MHC class I-like ligands

Engagement of diverse protein ligands (MIC-A/B, ULBP, Rae-1, or H60) by NKG2D immunoreceptors mediates elimination of tumorigenic or virally infected cells by natural killer and T cells. Three previous NKG2D-ligand complex structures show the homodimeric receptor interacting with the monomeric ligands in similar 2:1 complexes, with an equivalent surface on each NKG2D monomer binding intimately to a total of six distinct ligand surfaces. Here, the crystal structure of free human NKG2D and in silico and in vitro alanine-scanning mutagenesis analyses of the complex interfaces indicate that NKG2D recognition degeneracy is not explained by a classical induced-fit mechanism. Rather, the divergent ligands appear to utilize different strategies to interact with structurally conserved elements of the consensus NKG2D binding site.

An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes

Hydrogen bonding is a key contributor to the specificity of intramolecular and intermolecular interactions in biological systems. Here, we develop an orientation-dependent hydrogen bonding potential based on the geometric characteristics of hydrogen bonds in high-resolution protein crystal structures, and evaluate it using four tests related to the prediction and design of protein structures and protein-protein complexes. The new potential is superior to the widely used Coulomb model of hydrogen bonding in prediction of the sequences of proteins and protein-protein interfaces from their structures, and improves discrimination of correctly docked protein-protein complexes from large sets of alternative structures.

Converging free energy estimates: MM-PB(GB)SA studies on the protein-protein complex Ras-Raf

Estimating protein-protein interaction energies is a very challenging task for current simulation protocols. Here, absolute binding free energies are reported for the complex H-Ras/C-Raf1 using the MM-PB(GB)SA approach, testing the internal consistency and model dependence of the results. Averaging gas-phase energies (MM), solvation free energies as determined by Generalized Born models (GB/SA), and entropic contributions calculated by normal mode analysis for snapshots obtained from 10 ns explicit-solvent molecular dynamics in general results in an overestimation of the binding affinity when a solvent-accessible surface area-dependent model is used to estimate the nonpolar solvation contribution. Applying the sum of a cavity solvation free energy and explicitly modeled solute-solvent van der Waals interaction energies instead provides less negative estimates for the nonpolar solvation contribution. When the polar contribution to the solvation free energy is determined by solving the Poisson-Boltzmann equation (PB) instead, the calculated binding affinity strongly depends on the atomic radii set chosen. For three GB models investigated, different absolute deviations from PB energies were found for the unbound proteins and the complex. As an alternative to normal-mode calculations, quasiharmonic analyses have been performed to estimate entropic contributions due to changes of solute flexibility upon binding. However, such entropy estimates do not converge after 10 ns of simulation time, indicating that sampling issues may limit the applicability of this approach. Finally, binding free energies estimated from snapshots of the unbound proteins extracted from the complex trajectory result in an underestimate of binding affinity. This points to the need to exercise caution in applying the computationally cheaper "one-trajectory-alternative" to systems where there may be significant changes in flexibility and structure due to binding. The best estimate for the binding free energy of Ras-Raf obtained in this study of -8.3 kcal mol(-1) is in good agreement with the experimental result of -9.6 kcal mol(-1), however, further probing the transferability of the applied protocol that led to this result is necessary.