Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods

Computer-aided rational design strategy based on protein surface charge to improve the thermal stability of a novel esterase from Geobacillus jurassicus

OBJECTIVES

Although Geobacillus are significant thermophilic bacteria source, there are no reports of thermostable esterase gene in Geobacillus jurassicus or rational design strategies to increase the thermal stability of esterases.

RESULTS

Gene gju768 showed a highest similarity of 15.20% to esterases from Geobacillus sp. with detail enzymatic properties. Using a combination of Gibbs Unfolding Free Energy (∆∆G) calculator and the distance from the mutation site to the catalytic site (DsCα-Cα) to screen suitable mutation sites with elimination of negative surface charge, the mutants (D24N, E221Q, and E253Q) displayed stable mutants with higher thermal stability than the wild-type (WT). Mutant E253Q exhibited the best thermal stability, with a half-life (T1/2) at 65 °C of 32.4 min, which was 1.8-fold of the WT (17.9 min).

CONCLUSION

Cloning of gene gju768 and rational design based on surface charge engineering contributed to the identification of thermostable esterase from Geobacillus sp. and the exploration of evolutionary strategies for thermal stability.

Identification, Characterization, and Computer-Aided Rational Design of a Novel Thermophilic Esterase from Geobacillus subterraneus, and Application in the Synthesis of Cinnamyl Acetate

Investigation of a novel thermophilic esterase gene from Geobacillus subterraneus DSMZ 13552 indicated a high amino acid sequence similarity of 25.9% to a reported esterase from Geobacillus sp. A strategy that integrated computer-aided rational design tools was developed to select mutation sites. Six mutants were selected from four criteria based on the simulated saturation mutation (including 19 amino acid residues) results. Of these, the mutants Q78Y and G119A were found to retain 87% and 27% activity after incubation at 70 °C for 20 min, compared with the 19% activity for the wild type. Subsequently, a double-point mutant (Q78Y/G119A) was obtained and identified with optimal temperature increase from 65 to 70 °C and a 41.51% decrease in Km. The obtained T1/2 values of 42.2 min (70 °C) and 16.9 min (75 °C) for Q78Y/G119A showed increases of 340% and 412% compared with that in the wild type. Q78Y/G119A was then employed as a biocatalyst to synthesize cinnamyl acetate, for which the conversion rate reached 99.40% with 0.3 M cinnamyl alcohol at 60 °C. The results validated the enhanced enzymatic properties of the mutant and indicated better prospects for industrial application as compared to that in the wild type. This study reported a method by which an enzyme could evolve to achieve enhanced thermostability, thereby increasing its potential for industrial applications, which could also be expanded to other esterases.

Modular peptide binders - development of a predictive technology as alternative for reagent antibodies

Current biomedical research and diagnostics critically depend on detection agents for specific recognition and quantification of protein molecules. Monoclonal antibodies have been used for this purpose over decades and facilitated numerous biological and biomedical investigations. Recently, however, it has become apparent that many commercial reagent antibodies lack specificity or do not recognize their target at all. Thus, synthetic alternatives are needed whose complex designs are facilitated by multidisciplinary approaches incorporating experimental protein engineering with computational modeling. Here, we review the status of such an engineering endeavor based on the modular armadillo repeat protein scaffold and discuss challenges in its implementation.

An integrated computational pipeline for designing high-affinity nanobodies with expanded genetic codes

Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson's r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody's structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

Structural design principles for specific ultra-high affinity interactions between colicins/pyocins and immunity proteins

The interactions of the antibiotic proteins colicins/pyocins with immunity proteins is a seminal model system for studying protein–protein interactions and specificity. Yet, a precise and quantitative determination of which structural elements and residues determine their binding affinity and specificity is still lacking. Here, we used comparative structure-based energy calculations to map residues that substantially contribute to interactions across native and engineered complexes of colicins/pyocins and immunity proteins. We show that the immunity protein α1–α2 motif is a unique structurally-dissimilar element that restricts interaction specificity towards all colicins/pyocins, in both engineered and native complexes. This motif combines with a diverse and extensive array of electrostatic/polar interactions that enable the exquisite specificity that characterizes these interactions while achieving ultra-high affinity. Surprisingly, the divergence of these contributing colicin residues is reciprocal to residue conservation in immunity proteins. The structurally-dissimilar immunity protein α1–α2 motif is recognized by divergent colicins similarly, while the conserved immunity protein α3 helix interacts with diverse colicin residues. Electrostatics thus plays a key role in setting interaction specificity across all colicins and immunity proteins. Our analysis and resulting residue-level maps illuminate the molecular basis for these protein–protein interactions, with implications for drug development and rational engineering of these interfaces.

Uncovering the basis of protein-protein interaction specificity with a combinatorially complete library

Protein-protein interaction specificity is often encoded at the primary sequence level. However, the contributions of individual residues to specificity are usually poorly understood and often obscured by mutational robustness, sequence degeneracy, and epistasis. Using bacterial toxin-antitoxin systems as a model, we screened a combinatorially complete library of antitoxin variants at three key positions against two toxins. This library enabled us to measure the effect of individual substitutions on specificity in hundreds of genetic backgrounds. These distributions allow inferences about the general nature of interface residues in promoting specificity. We find that positive and negative contributions to specificity are neither inherently coupled nor mutually exclusive. Further, a wild-type antitoxin appears optimized for specificity as no substitutions improve discrimination between cognate and non-cognate partners. By comparing crystal structures of paralogous complexes, we provide a rationale for our observations. Collectively, this work provides a generalizable approach to understanding the logic of molecular recognition.

Peptide design by optimization on a data-parameterized protein interaction landscape

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein-peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-x_L, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-x_L, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2-binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

Formation and properties of amyloid fibrils of prion protein

Amyloid fibrils formed from prion protein (PrP) are associated with prion diseases. In this review we discuss a number of extrinsic and intrinsic experimental factors related to the formation of PrP amyloid fibrils in vitro. We first examined the effects of ultrasonic power on the induction of amyloid fibrillation from PrP. The most important conclusion drawn from the results is that an applied ultrasonic power of approximately 2 W enhanced the nucleation of amyloid fibrils efficiently but that more powerful ultrasonication led to retardation of growth. We also reviewed evidence on the amyloidogenic regions of PrP based on peptide screening throughout the polypeptide sequence. These results showed that helix 2 (H2) peptides of PrP were capable of both the fibrillation and propagation of straight, long fibrils. Moreover, the conformation of preformed H2 fibrils changed reversibly depending on the pH of the solution, implying that interactions between side-chains modulated the conformation of amyloid fibrils. The evidence discussed in this review relates specifically to PrP but may be relevant to other amyloidogenic proteins.

Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design

Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.

Combining in silico evolution and nonlinear dimensionality reduction to redesign responses of signaling networks

The rational design of network behavior is a central goal of synthetic biology. Here, we combine in silico evolution with nonlinear dimensionality reduction to redesign the responses of fixed-topology signaling networks and to characterize sets of kinetic parameters that underlie various input-output relations. We first consider the earliest part of the T cell receptor (TCR) signaling network and demonstrate that it can produce a variety of input-output relations (quantified as the level of TCR phosphorylation as a function of the characteristic TCR binding time). We utilize an evolutionary algorithm (EA) to identify sets of kinetic parameters that give rise to: (i) sigmoidal responses with the activation threshold varied over 6 orders of magnitude, (ii) a graded response, and (iii) an inverted response in which short TCR binding times lead to activation. We also consider a network with both positive and negative feedback and use the EA to evolve oscillatory responses with different periods in response to a change in input. For each targeted input-output relation, we conduct many independent runs of the EA and use nonlinear dimensionality reduction to embed the resulting data for each network in two dimensions. We then partition the results into groups and characterize constraints placed on the parameters by the different targeted response curves. Our approach provides a way (i) to guide the design of kinetic parameters of fixed-topology networks to generate novel input-output relations and (ii) to constrain ranges of biological parameters using experimental data. In the cases considered, the network topologies exhibit significant flexibility in generating alternative responses, with distinct patterns of kinetic rates emerging for different targeted responses.

Engineering Specificity from Broad to Narrow: Design of a β-Lactamase Inhibitory Protein (BLIP) Variant That Exclusively Binds and Detects KPC β-Lactamase

The β-lactamase inhibitory protein (BLIP) binds and inhibits a wide range of class A β-lactamases including the TEM-1 β-lactamase (K_i = 0.5 nM), which is widely present in Gram-negative bacteria, and the KPC-2 β-lactamase (K_i = 1.2 nM), which hydrolyzes virtually all clinically useful β-lactam antibiotics. The extent to which the specificity of a protein that binds a broad range of targets can be modified to display narrow specificity was explored in this study by engineering BLIP to bind selectively to KPC-2 β-lactamase. A genetic screen for BLIP function in Escherichia coli was used to narrow the binding specificity of BLIP by identifying amino acid substitutions that retain affinity for KPC-2 while losing affinity for TEM-1 β-lactamase. The combination of single substitutions yielded the K74T:W112D BLIP variant, which was shown by inhibition assays to retain high affinity for KPC-2 with a K_i of 0.4 nM, while drastically losing affinity for TEM-1 with a K_i > 10 μM. The K74T:W112D mutant therefore binds KPC-2 β-lactamase 3 times more tightly while binding TEM-1 > 20000-fold more weakly than wild-type BLIP. The K74T:W112D BLIP variant also exhibited low affinity (K_i > 10 μM) for other class A β-lactamases. The high affinity and narrow specificity of BLIP K74T:W112D for KPC-2 β-lactamase suggest it could be a useful sensor for the presence of this enzyme in multidrug-resistant bacteria. This was demonstrated with an assay employing BLIP K74T:W112D conjugated to a bead to specifically pull-down and detect KPC-2 β-lactamase in lysates from clinical bacterial isolates containing multiple β-lactamases.

Protein stability: computation, sequence statistics, and new experimental methods

Calculating protein stability and predicting stabilizing mutations remain exceedingly difficult tasks, largely due to the inadequacy of potential functions, the difficulty of modeling entropy and the unfolded state, and challenges of sampling, particularly of backbone conformations. Yet, computational design has produced some remarkably stable proteins in recent years, apparently owing to near ideality in structure and sequence features. With caveats, computational prediction of stability can be used to guide mutation, and mutations derived from consensus sequence analysis, especially improved by recent co-variation filters, are very likely to stabilize without sacrificing function. The combination of computational and statistical approaches with library approaches, including new technologies such as deep sequencing and high throughput stability measurements, point to a very exciting near term future for stability engineering, even with difficult computational issues remaining.

Designing helical peptide inhibitors of protein-protein interactions

Short helical peptides combine characteristics of small molecules and large proteins and provide an exciting area of opportunity in protein design. A growing number of studies report novel helical peptide inhibitors of protein-protein interactions. New techniques have been developed for peptide design and for chemically stabilizing peptides in a helical conformation, which frequently improves protease resistance and cell permeability. We summarize advances in peptide crosslinking chemistry and give examples of peptide design studies targeting coiled-coil transcription factors, Bcl-2 family proteins, MDM2/MDMX, and HIV gp41, among other targets.

Protein Engineering by Combined Computational and In Vitro Evolution Approaches

Two alternative strategies are commonly used to study protein-protein interactions (PPIs) and to engineer protein-based inhibitors. In one approach, binders are selected experimentally from combinatorial libraries of protein mutants that are displayed on a cell surface. In the other approach, computational modeling is used to explore an astronomically large number of protein sequences to select a small number of sequences for experimental testing. While both approaches have some limitations, their combination produces superior results in various protein engineering applications. Such applications include the design of novel binders and inhibitors, the enhancement of affinity and specificity, and the mapping of binding epitopes. The combination of these approaches also aids in the understanding of the specificity profiles of various PPIs.

Locating Herpesvirus Bcl-2 Homologs in the Specificity Landscape of Anti-Apoptotic Bcl-2 Proteins

Viral homologs of the anti-apoptotic Bcl-2 proteins are highly diverged from their mammalian counterparts, yet they perform overlapping functions by binding and inhibiting BH3 (Bcl-2 homology 3)-motif-containing proteins. We investigated the BH3 binding properties of the herpesvirus Bcl-2 homologs KSBcl-2, BHRF1, and M11, as they relate to those of the human Bcl-2 homologs Mcl-1, Bfl-1, Bcl-w, Bcl-xL, and Bcl-2. Analysis of the sequence and structure of the BH3 binding grooves showed that, despite low sequence identity, M11 has structural similarities to Bcl-xL, Bcl-2, and Bcl-w. BHRF1 and KSBcl-2 are more structurally similar to Mcl-1 than to the other human proteins. Binding to human BH3-like peptides showed that KSBcl-2 has similar specificity to Mcl-1, and BHRF1 has a restricted binding profile; M11 binding preferences are distinct from those of Bcl-xL, Bcl-2, and Bcl-w. Because KSBcl-2 and BHRF1 are from human herpesviruses associated with malignancies, we screened computationally designed BH3 peptide libraries using bacterial surface display to identify selective binders of KSBcl-2 or BHRF1. The resulting peptides bound to KSBcl-2 and BHRF1 in preference to Bfl-1, Bcl-w, Bcl-xL, and Bcl-2 but showed only modest specificity over Mcl-1. Rational mutagenesis increased specificity against Mcl-1, resulting in a peptide with a dissociation constant of 2.9nM for binding to KSBcl-2 and >1000-fold specificity over other Bcl-2 proteins, as well as a peptide with >70-fold specificity for BHRF1. In addition to providing new insights into viral Bcl-2 binding specificity, this study will inform future work analyzing the interaction properties of homologous binding domains and designing specific protein interaction partners.

Redesigning protein cavities as a strategy for increasing affinity in protein-protein interaction: interferon- γ receptor 1 as a model

Combining computational and experimental tools, we present a new strategy for designing high affinity variants of a binding protein. The affinity is increased by mutating residues not at the interface, but at positions lining internal cavities of one of the interacting molecules. Filling the cavities lowers flexibility of the binding protein, possibly reducing entropic penalty of binding. The approach was tested using the interferon-γ receptor 1 (IFNγR1) complex with IFNγ as a model. Mutations were selected from 52 amino acid positions lining the IFNγR1 internal cavities by using a protocol based on FoldX prediction of free energy changes. The final four mutations filling the IFNγR1 cavities and potentially improving the affinity to IFNγ were expressed, purified, and refolded, and their affinity towards IFNγ was measured by SPR. While individual cavity mutations yielded receptor constructs exhibiting only slight increase of affinity compared to WT, combinations of these mutations with previously characterized variant N96W led to a significant sevenfold increase. The affinity increase in the high affinity receptor variant N96W+V35L is linked to the restriction of its molecular fluctuations in the unbound state. The results demonstrate that mutating cavity residues is a viable strategy for designing protein variants with increased affinity.

Mapping the BH3 Binding Interface of Bcl-xL, Bcl-2, and Mcl-1 Using Split-Luciferase Reassembly

The recognition of helical BH3 domains by Bcl-2 homology (BH) receptors plays a central role in apoptosis. The residues that determine specificity or promiscuity in this interactome are difficult to predict from structural and computational data. Using a cell free split-luciferase system, we have generated a 276 pairwise interaction map for 12 alanine mutations at the binding interface for three receptors, Bcl-xL, Bcl-2, and Mcl-1, and interrogated them against BH3 helices derived from Bad, Bak, Bid, Bik, Bim, Bmf, Hrk, and Puma. This panel, in conjunction with previous structural and functional studies, starts to provide a more comprehensive portrait of this interactome, explains promiscuity, and uncovers surprising details; for example, the Bcl-xL R139A mutation disrupts binding to all helices but the Bad-BH3 peptide, and Mcl-1 binding is particularly perturbed by only four mutations of the 12 tested (V220A, N260A, R263A, and F319A), while Bcl-xL and Bcl-2 have a more diverse set of important residues depending on the bound helix.

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.

Increasing the affinity of selective bZIP-binding peptides through surface residue redesign

The coiled-coil dimer is a prevalent protein interaction motif that is important for many cellular processes. The basic leucine-zipper (bZIP) transcription factors are one family of proteins for which coiled-coil mediated dimerization is essential for function, and misregulation of bZIPs can lead to disease states including cancer. This makes coiled coils attractive protein-protein interaction targets to disrupt using engineered molecules. Previous work designing peptides to compete with native coiled-coil interactions focused primarily on designing the core residues of the interface to achieve affinity and specificity. However, folding studies on the model bZIP GCN4 show that coiled-coil surface residues also contribute to binding affinity. Here we extend a prior study in which peptides were designed to bind tightly and specifically to representative members of each of 20 human bZIP families. These "anti-bZIP" peptides were designed with an emphasis on target-binding specificity, with contributions to design-target specificity and affinity engineered considering only the coiled-coil core residues. High-throughput testing using peptide arrays indicated many successes. We have now measured the binding affinities and specificities of anti-bZIPs that bind to FOS, XBP1, ATF6, and CREBZF in solution and tested whether redesigning the surface residues can increase design-target affinity. Incorporating residues that favor helix formation into the designs increased binding affinities in all cases, providing low-nanomolar binders of each target. However, changes in surface electrostatic interactions sometimes changed the binding specificity of the designed peptides.

Using anchoring motifs for the computational design of protein-protein interactions

The computer-based design of PPIs (protein-protein interactions) is a challenging problem because large desolvation and entropic penalties must be overcome by the creation of favourable hydrophobic and polar contacts at the target interface. Indeed, many computationally designed interactions fail to form when tested in the laboratory. In the present article, we highlight strategies our laboratory has been pursuing to make interface design more tractable. Our general approach has been to make use of structural motifs found in native proteins that are predisposed to interact with a particular binding geometry, and then further bolster these anchor points with favourable hydrophobic contacts. We describe the use of three different anchor points, i.e. β-strand pairing, metal binding and the docking of α-helix into a groove, to successfully design new interfaces. In several cases, high-resolution crystal structures show that the design models closely match the experimental structure. In addition, we have tested the use of buried hydrogen-bond networks as a source of affinity and specificity at interfaces. In these cases, the designed complexes did not form, highlighting the challenges associated with designing buried polar interactions.

Predicting the Impact of Missense Mutations on Protein-Protein Binding Affinity

The crucial prerequisite for proper biological function is the protein's ability to establish highly selective interactions with macromolecular partners. A missense mutation that alters the protein binding affinity may cause significant perturbations or complete abolishment of the function, potentially leading to diseases. The availability of computational methods to evaluate the impact of mutations on protein-protein binding is critical for a wide range of biomedical applications. Here, we report an efficient computational approach for predicting the effect of single and multiple missense mutations on protein-protein binding affinity. It is based on a well-tested simulation protocol for structure minimization, modified MM-PBSA and statistical scoring energy functions with parameters optimized on experimental sets of several thousands of mutations. Our simulation protocol yields very good agreement between predicted and experimental values with Pearson correlation coefficients of 0.69 and 0.63 and root-mean-square errors of 1.20 and 1.90 kcal mol^-1 for single and multiple mutations, respectively. Compared with other available methods, our approach achieves high speed and prediction accuracy and can be applied to large datasets generated by modern genomics initiatives. In addition, we report a crucial role of water model and the polar solvation energy in estimating the changes in binding affinity. Our analysis also reveals that prediction accuracy and effect of mutations on binding strongly depends on the type of mutation and its location in a protein complex.

Protein engineering and the use of molecular modeling and simulation: the case of heterodimeric Fc engineering

Computational and structure guided methods can make significant contributions to the development of solutions for difficult protein engineering problems, including the optimization of next generation of engineered antibodies. In this paper, we describe a contemporary industrial antibody engineering program, based on hypothesis-driven in silico protein optimization method. The foundational concepts and methods of computational protein engineering are discussed, and an example of a computational modeling and structure-guided protein engineering workflow is provided for the design of best-in-class heterodimeric Fc with high purity and favorable biophysical properties. We present the engineering rationale as well as structural and functional characterization data on these engineered designs.

Increasing affinity of interferon-γ receptor 1 to interferon-γ by computer-aided design

We describe a computer-based protocol to design protein mutations increasing binding affinity between ligand and its receptor. The method was applied to mutate interferon-γ receptor 1 (IFN-γ-Rx) to increase its affinity to natural ligand IFN-γ, protein important for innate immunity. We analyzed all four available crystal structures of the IFN-γ-Rx/IFN-γ complex to identify 40 receptor residues forming the interface with IFN-γ. For these 40 residues, we performed computational mutation analysis by substituting each of the interface receptor residues by the remaining standard amino acids. The corresponding changes of the free energy were calculated by a protocol consisting of FoldX and molecular dynamics calculations. Based on the computed changes of the free energy and on sequence conservation criteria obtained by the analysis of 32 receptor sequences from 19 different species, we selected 14 receptor variants predicted to increase the receptor affinity to IFN-γ. These variants were expressed as recombinant proteins in Escherichia coli, and their affinities to IFN-γ were determined experimentally by surface plasmon resonance (SPR). The SPR measurements showed that the simple computational protocol succeeded in finding two receptor variants with affinity to IFN-γ increased about fivefold compared to the wild-type receptor.

The scoring of poses in protein-protein docking: current capabilities and future directions

BACKGROUND

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.

RESULTS

We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically.

CONCLUSIONS

All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

Structural basis of a rationally rewired protein-protein interface critical to bacterial signaling

Two-component signal transduction systems typically involve a sensor histidine kinase that specifically phosphorylates a single, cognate response regulator. This protein-protein interaction relies on molecular recognition via a small set of residues in each protein. To better understand how these residues determine the specificity of kinase-substrate interactions, we rationally rewired the interaction interface of a Thermotoga maritima two-component system, HK853-RR468, to match that found in a different two-component system, Escherichia coli PhoR-PhoB. The rewired proteins interacted robustly with each other, but no longer interacted with the parent proteins. Analysis of the crystal structures of the wild-type and mutant protein complexes and a systematic mutagenesis study reveal how individual mutations contribute to the rewiring of interaction specificity. Our approach and conclusions have implications for studies of other protein-protein interactions and protein evolution and for the design of novel protein interfaces.

Modeling catalytic promiscuity in the alkaline phosphatase superfamily

In recent years, it has become increasingly clear that promiscuity plays a key role in the evolution of new enzyme function. This finding has helped to elucidate fundamental aspects of molecular evolution. While there has been extensive experimental work on enzyme promiscuity, computational modeling of the chemical details of such promiscuity has traditionally fallen behind the advances in experimental studies, not least due to the nearly prohibitive computational cost involved in examining multiple substrates with multiple potential mechanisms and binding modes in atomic detail with a reasonable degree of accuracy. However, recent advances in both computational methodologies and power have allowed us to reach a stage in the field where we can start to overcome this problem, and molecular simulations can now provide accurate and efficient descriptions of complex biological systems with substantially less computational cost. This has led to significant advances in our understanding of enzyme function and evolution in a broader sense. Here, we will discuss currently available computational approaches that can allow us to probe the underlying molecular basis for enzyme specificity and selectivity, discussing the inherent strengths and weaknesses of each approach. As a case study, we will discuss recent computational work on different members of the alkaline phosphatase superfamily (AP) using a range of different approaches, showing the complementary insights they have provided. We have selected this particular superfamily, as it poses a number of significant challenges for theory, ranging from the complexity of the actual reaction mechanisms involved to the reliable modeling of the catalytic metal centers, as well as the very large system sizes. We will demonstrate that, through current advances in methodologies, computational tools can provide significant insight into the molecular basis for catalytic promiscuity, and, therefore, in turn, the mechanisms of protein functional evolution.

Energetics of oligomeric protein folding and association

In nature, proteins most often exist as complexes, with many of these consisting of identical subunits. Understanding of the energetics governing the folding and misfolding of such homooligomeric proteins is central to understanding their function and misfunction, in disease or biotechnology. Much progress has been made in defining the mechanisms and thermodynamics of homooligomeric protein folding. In this review, we outline models as well as calorimetric and spectroscopic methods for characterizing oligomer folding, and describe extensive results obtained for diverse proteins, ranging from dimers to octamers and higher order aggregates. To our knowledge, this area has not been reviewed comprehensively in years, and the collective progress is impressive. The results provide evolutionary insights into the development of subunit interfaces, mechanisms of oligomer folding, and contributions of oligomerization to protein stability, function and regulation. Thermodynamic analyses have also proven valuable for understanding protein misfolding and aggregation mechanisms, suggesting new therapeutic avenues. Successful recent designs of novel, functional proteins demonstrate increased understanding of oligomer folding. Further rigorous analyses using multiple experimental and computational approaches are still required, however, to achieve consistent and accurate prediction of oligomer folding energetics. Modeling the energetics remains challenging but is a promising avenue for future advances.

On the binding affinity of macromolecular interactions: daring to ask why proteins interact

Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (K_d), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein–protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure–affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein–protein recognition.

Structure-based redesign of the binding specificity of anti-apoptotic Bcl-x(L)

Many native proteins are multi-specific and interact with numerous partners, which can confound analysis of their functions. Protein design provides a potential route to generating synthetic variants of native proteins with more selective binding profiles. Redesigned proteins could be used as research tools, diagnostics or therapeutics. In this work, we used a library screening approach to reengineer the multi-specific anti-apoptotic protein Bcl-x(L) to remove its interactions with many of its binding partners, making it a high-affinity and selective binder of the BH3 region of pro-apoptotic protein Bad. To overcome the enormity of the potential Bcl-x(L) sequence space, we developed and applied a computational/experimental framework that used protein structure information to generate focused combinatorial libraries. Sequence features were identified using structure-based modeling, and an optimization algorithm based on integer programming was used to select degenerate codons that maximally covered these features. A constraint on library size was used to ensure thorough sampling. Using yeast surface display to screen a designed library of Bcl-x(L) variants, we successfully identified a protein with ~1000-fold improvement in binding specificity for the BH3 region of Bad over the BH3 region of Bim. Although negative design was targeted only against the BH3 region of Bim, the best redesigned protein was globally specific against binding to 10 other peptides corresponding to native BH3 motifs. Our design framework demonstrates an efficient route to highly specific protein binders and may readily be adapted for application to other design problems.

Recent advances in rational approaches for enzyme engineering

Enzymes are an attractive alternative in the asymmetric syntheses of chiral building blocks. To meet the requirements of industrial biotechnology and to introduce new functionalities, the enzymes need to be optimized by protein engineering. This article specifically reviews rational approaches for enzyme engineering and de novo enzyme design involving structure-based approaches developed in recent years for improvement of the enzymes’ performance, broadened substrate range, and creation of novel functionalities to obtain products with high added value for industrial applications.

Multistate approaches in computational protein design

Computational protein design (CPD) is a useful tool for protein engineers. It has been successfully applied towards the creation of proteins with increased thermostability, improved binding affinity, novel enzymatic activity, and altered ligand specificity. Traditionally, CPD calculations search and rank sequences using a single fixed protein backbone template in an approach referred to as single-state design (SSD). While SSD has enjoyed considerable success, certain design objectives require the explicit consideration of multiple conformational and/or chemical states. Cases where a "multistate" approach may be advantageous over the SSD approach include designing conformational changes into proteins, using native ensembles to mimic backbone flexibility, and designing ligand or oligomeric association specificities. These design objectives can be efficiently tackled using multistate design (MSD), an emerging methodology in CPD that considers any number of protein conformational or chemical states as inputs instead of a single protein backbone template, as in SSD. In this review article, recent examples of the successful design of a desired property into proteins using MSD are described. These studies employing MSD are divided into two categories--those that utilized multiple conformational states, and those that utilized multiple chemical states. In addition, the scoring of competing states during negative design is discussed as a current challenge for MSD.

Predictive Bcl-2 family binding models rooted in experiment or structure

Proteins of the Bcl-2 family either enhance or suppress programmed cell death and are centrally involved in cancer development and resistance to chemotherapy. BH3 (Bcl-2 homology 3)-only Bcl-2 proteins promote cell death by docking an α-helix into a hydrophobic groove on the surface of one or more of five pro-survival Bcl-2 receptor proteins. There is high structural homology within the pro-death and pro-survival families, yet a high degree of interaction specificity is nevertheless encoded, posing an interesting and important molecular recognition problem. Understanding protein features that dictate Bcl-2 interaction specificity is critical for designing peptide-based cancer therapeutics and diagnostics. In this study, we present peptide SPOT arrays and deep sequencing data from yeast display screening experiments that significantly expand the BH3 sequence space that has been experimentally tested for interaction with five human anti-apoptotic receptors. These data provide rich information about the determinants of Bcl-2 family specificity. To interpret and use the information, we constructed two simple data-based models that can predict affinity and specificity when evaluated on independent data sets within a limited sequence space. We also constructed a novel structure-based statistical potential, called STATIUM, which is remarkably good at predicting Bcl-2 affinity and specificity, especially considering it is not trained on experimental data. We compare the performance of our three models to each other and to alternative structure-based methods and discuss how such tools can guide prediction and design of new Bcl-2 family complexes.