Preferential heterodimer formation via undercompensated electrostatic interactions

Direct Calculation of Protein Fitness Landscapes through Computational Protein Design

Naturally selected amino-acid sequences or experimentally derived ones are often the basis for understanding how protein three-dimensional conformation and function are determined by primary structure. Such sequences for a protein family comprise only a small fraction of all possible variants, however, representing the fitness landscape with limited scope. Explicitly sampling and characterizing alternative, unexplored protein sequences would directly identify fundamental reasons for sequence robustness (or variability), and we demonstrate that computational methods offer an efficient mechanism toward this end, on a large scale. The dead-end elimination and A(∗) search algorithms were used here to find all low-energy single mutant variants, and corresponding structures of a G-protein heterotrimer, to measure changes in structural stability and binding interactions to define a protein fitness landscape. We established consistency between these algorithms with known biophysical and evolutionary trends for amino-acid substitutions, and could thus recapitulate known protein side-chain interactions and predict novel ones.

Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity--the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking "hotspots," or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.

Energetic decomposition with the generalized-born and Poisson-Boltzmann solvent models: lessons from association of G-protein components

Continuum electrostatic models have been shown to be powerful tools in providing insight into the energetics of biomolecular processes. While the Poisson-Boltzmann (PB) equation provides a theoretically rigorous approach to computing electrostatic free energies of solution in such a model, computational cost makes its use for large ensembles of states impractical. The generalized-Born (GB) approximation provides a much faster alternative, although with a weaker theoretical framework. While much attention has been given to how GB recapitulates PB energetics for the overall stability of a biomolecule or the affinity of a complex, little attention has been given to how the contributions of individual functional groups are captured by the two methods. Accurately capturing these individual electrostatic components is essential both for the development of a mechanistic understanding of biomolecular processes and for the design of variant sequences and structures with desired properties. Here, we present a detailed comparison of the group-wise decomposition of both PB and GB electrostatic free energies of binding, using association of various components of the heterotrimeric-G-protein complex as a model. We find that, while net binding free energies are strongly correlated in the two models, the correlations of individual group contributions are highly variable; in some cases, strong correlation is seen, while in others, there is essentially none. Structurally, the GB model seems to capture the magnitude of direct, short-range electrostatic interactions quite well but performs more poorly with moderate-range "action-at-a-distance" interactions--GB has a tendency to overestimate solvent screening over moderate distances, and to underestimate the costs of desolvating charged groups somewhat removed from the binding interface. Despite this, however, GB does seem to be quite effective as a predictor of those groups that will be computed to be most significant in a PB-based model.

Thermodynamic basis for promiscuity and selectivity in protein-protein interactions: PDZ domains, a case study

Like other protein-protein interaction domains, PDZ domains are involved in many key cellular processes. These processes often require that specific multiprotein complexes be assembled, a task that PDZ domains accomplish by binding to specific peptide motifs in target proteins. However, a growing number of experimental studies show that PDZ domains (like other protein-protein interaction domains) can engage in a variety of interactions and bind distinct peptide motifs. Such promiscuity in ligand recognition raises intriguing questions about the molecular and thermodynamic mechanisms that can sustain it. To identify possible sources of promiscuity and selectivity underlying PDZ domain interactions, we performed molecular dynamics simulations of 20 to 25 ns on a set of 12 different PDZ domain complexes (for the proteins PSD-95, Syntenin, Erbin, GRIP, NHERF, Inad, Dishevelled, and Shank). The electrostatic, nonpolar, and configurational entropy binding contributions were evaluated using the MM/PBSA method combined with a quasi-harmonic analysis. The results revealed that PDZ domain interactions are characterized by overwhelmingly favorable nonpolar contributions and almost negligible electrostatic components, a mix that may readily sustain promiscuity. In addition, despite the structural similarity in fold and in recognition modes, the entropic and other dynamical aspects of binding were remarkably variable not only across PDZ domains but also for the same PDZ domain bound to distinct ligands. This variability suggests that entropic and dynamical components can play a role in determining selectivity either of PDZ domain interactions with peptide ligands or of PDZ domain complexes with downstream effectors.

Orientation and oligomerization specificity of the Bcr coiled-coil oligomerization domain

The Bcr oligomerization domain, from the Bcr-Abl oncoprotein, is an attractive therapeutic target for treating leukemias because it is required for cellular transformation. The domain homodimerizes via an antiparallel coiled coil with an adjacent short, helical swap domain. Inspection of the coiled-coil sequence does not reveal obvious determinants of helix-orientation specificity, raising the possibility that the antiparallel orientation preference and/or the dimeric oligomerization state are due to interactions of the swap domains. To better understand how structural specificity is encoded in Bcr, coiled-coil constructs containing either an N- or C-terminal cysteine were synthesized without the swap domain. When cross-linked to adopt exclusively parallel or antiparallel orientations, these showed similar circular dichroism spectra. Both constructs formed coiled-coil dimers, but the antiparallel construct was approximately 16 degrees C more stable than the parallel to thermal denaturation. Equilibrium disulfide-exchange studies confirmed that the isolated coiled-coil homodimer shows a very strong preference for the antiparallel orientation. We conclude that the orientation and oligomerization preferences of Bcr are not caused by the presence of the swap domains, but rather are directly encoded in the coiled-coil sequence. We further explored possible determinants of structural specificity by mutating residues in the d position of the coiled-coil core. Some of the mutations caused a change in orientation specificity, and all of the mutations led to the formation of higher-order oligomers. In the absence of the swap domain, these residues play an important role in disfavoring alternate states and are especially important for encoding dimeric oligomerization specificity.

Specificity versus stability in computational protein design

Protein-protein interactions can be designed computationally by using positive strategies that maximize the stability of the desired structure and/or by negative strategies that seek to destabilize competing states. Here, we compare the efficacy of these methods in reengineering a protein homodimer into a heterodimer. The stability-design protein (positive design only) was experimentally more stable than the specificity-design heterodimer (positive and negative design). By contrast, only the specificity-design protein assembled as a homogenous heterodimer in solution, whereas the stability-design protein formed a mixture of homodimer and heterodimer species. The experimental stabilities of the engineered proteins correlated roughly with their calculated stabilities, and the crystal structure of the specificity-design heterodimer showed most of the predicted side-chain packing interactions and a main-chain conformation indistinguishable from the wild-type structure. These results indicate that the design simulations capture important features of both stability and structure and demonstrate that negative design can be critical for attaining specificity when competing states are close in structure space.

Comparison of in vivo selection and rational design of heterodimeric coiled coils

To investigate how electrostatic interactions restrict the associations of coiled coils, we improved a heterodimeric coiled coil (WinZip-A1B1) by in vivo selection and, alternatively, by rational design. Selection from libraries encoding variable edge (g and e) residues enriched g/e' ion pairs, but the optimum selected heterodimers unexpectedly retained two predicted repulsive g/e' pairs. The best genetically selected heterodimer displayed similar thermodynamic stability and specificity as a rationally designed dimer with predicted ion pairs at all edge positions. This rationally designed pair, however, was less effective than the best genetically selected pair in mediating dimerization in vivo. Thus, the effects of predicted charge pairs depend on sequence context, and complementary charges at the edge positions rationalize only a fraction of the sequences that form stable, specific coiled coils.

Identification of a functional site on the type I TGF-beta receptor by mutational analysis of its ectodomain

Six charged amino acid residues located in the ectodomain of the full-length type I transforming growth factor (TGF)-beta receptor were individually mutated to alanine. Mutation of residues D47, D98, K102 and E104 resulted in functionally impaired receptors as demonstrated by a marked decrease in ligand-dependent signaling and ligand internalization relative to the wild-type receptor. The other two mutants (K39A and K87A) exhibited wild-type-like activity. Molecular modeling indicates that the four functionally important residues are located on the convex face of the ectodomain structure. Since mutation of these four residues affects signaling and ligand internalization but not ligand binding, we propose that this functional site is an interacting site between type I and II receptors.

Computational protein design

The field of computational protein design is reaching its adolescence. Protein design algorithms have been applied to design or engineer proteins that fold, fold faster, catalyze, catalyze faster, signal, and adopt preferred conformational states. Further developments of scoring functions, sampling strategies, and optimization methods will expand the range of applicability of computational protein design to larger and more varied systems, with greater incidence of success. Developments in this field are beginning to have significant impact on biotechnology and chemical biology.