Modulating calmodulin binding specificity through computational protein design

In silico design of misfolding resistant proteins: the role of structural similarity of a competing conformational ensemble in the optimization of frustration

Most state-of-the-art in silico design methods fail due to misfolding of designed sequences to a conformation other than the target. Thus, a method to design misfolding resistant proteins will provide a better understanding of the misfolding phenomenon and will also increase the success rate of in silico design methods. In this work, we optimize the conformational ensemble to be selected for negative design purposes based on the similarity of the conformational ensemble to the target. Five ensembles with different degrees of similarity to the target are created and destabilized and the target is stabilized while designing sequences using mean field theory and Monte Carlo simulation methods. The results suggest that the degree of similarity of the non-native conformations to the target plays a prominent role in designing misfolding resistant protein sequences. The design procedures that destabilize the conformational ensemble with moderate similarity to the target have proven to be more promising. Incorporation of either highly similar or highly dissimilar conformations to the target conformation into the non-native ensemble to be destabilized may lead to sequences with a higher misfolding propensity. This will significantly reduce the conformational space to be considered in any protein design procedure. Interestingly, the results suggest that a sequence with higher frustration in the target structure does not necessarily lead to a misfold prone sequence. A successful design method may purposefully choose a frustrated sequence in the target conformation if that sequence is even more frustrated in the competing non-native conformations.

Immobilization of azide-functionalized proteins to micro- and nanoparticles directly from cell lysate

Immobilization of proteins and enzymes on solid supports has been utilized in a variety of applications, from improved protein stability on supported catalysts in industrial processes to fabrication of biosensors, biochips, and microdevices. A critical requirement for these applications is facile yet stable covalent conjugation between the immobilized and fully active protein and the solid support to produce stable, highly bio-active conjugates. Here, we report functionalization of solid surfaces (gold nanoparticles and magnetic beads) with bio-active proteins using site-specific and biorthogonal labeling and azide-alkyne cycloaddition, a click chemistry. Specifically, we recombinantly express and selectively label calcium-dependent proteins, calmodulin and calcineurin, and cAMP-dependent protein kinase A (PKA) with N-terminal azide-tags for efficient conjugation to nanoparticles and magnetic beads. We successfully immobilized the proteins on to the solid supports directly from the cell lysate with click chemistry, forgoing the step of purification. This approach is optimized to yield low particle aggregation and high levels of protein activity post-conjugation. The entire process enables streamlined workflows for bioconjugation and highly active conjugated proteins.

Engineering Ca2+-Dependent DNA Polymerase Activity

Advancements in synthetic biology have provided new opportunities in biosensing, with applications ranging from genetic programming to diagnostics. Next generation biosensors aim to expand the number of accessible environments for measurements, increase the number of measurable phenomena, and improve the quality of the measurement. To this end, an emerging area in the field has been the integration of DNA as an information storage medium within biosensor outputs, leveraging nucleic acids to record the biosensor state over time. However, slow signal transduction steps, due to the time scales of transcription and translation, bottleneck many sensing-DNA recording approaches. DNA polymerases (DNAPs) have been proposed as a solution to the signal transduction problem by operating as both the sensor and responder, but there is presently a lack of DNAPs with functional sensitivity to many desirable target ligands. Here, we engineer components of the Pol δ replicative polymerase complex of Saccharomyces cerevisiae to sense and respond to Ca2+, a metal cofactor relevant to numerous biological phenomena. Through domain insertion and binding site grafting to Pol δ subunits, we demonstrate functional allosteric sensitivity to Ca2+. Together, this work provides an important foundation for future efforts in the development of DNAP-based biosensors.

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Optimizing the Specificity Window of Biomolecular Receptors Using Structure-Switching and Allostery

To ensure maximum specificity (i.e., minimize cross-reactivity with structurally similar analogues of the desired target), most bioassays invoke "stringency", the careful tuning of the conditions employed (e.g., pH, ionic strength, or temperature). Willingness to control assay conditions will fall, however, as quantitative, single-step biosensors begin to replace multistep analytical processes. This is especially true for sensors deployed in vivo, where the tuning of such parameters is not just inconvenient but impossible. In response, we describe here the rational adaptation of two strategies employed by nature to tune the affinity of biomolecular receptors so as to optimize the placement of their specificity "windows" without the need to alter measurement conditions: structure-switching and allosteric control. We quantitatively validate these approaches using two distinct, DNA-based receptors: a simple, linear-chain DNA suitable for detecting a complementary DNA strand and a structurally complex DNA aptamer used for the detection of a small-molecule drug. Using these models, we show that, without altering assay conditions, structure-switching and allostery can tune the concentration range over which a receptor achieves optimal specificity over orders of magnitude, thus optimally matching the specificity window with the range of target concentrations expected to be seen in a given application.

High-throughput amenable fluorescence-assays to screen for calmodulin-inhibitors

The KRAS gene is highly mutated in human cancers and the focus of current Ras drug development efforts. Recently the interface between the C-terminus of K-Ras and calmodulin (CaM) was proposed as a target site to block K-Ras driven cancer cell stemness. We therefore aimed at developing a high-throughput amenable screening assay to identify novel CaM-inhibitors as potential K-Ras stemness-signaling disruptors. A modulated time-resolved Förster resonance energy transfer (mTR-FRET)-assay was developed and benchmarked against an identically designed fluorescence anisotropy (FA)-assay. In both assays, two CaM-binding peptides were labeled with Eu(III)-chelate or fluorescein and used as single-label reporter probes that were displaced from CaM upon competitor binding. Thus, peptidic and small molecule competitors with nanomolar to micromolar affinities to CaM could be detected, including a peptide that was derived from the C-terminus of K-Ras. In order to detect CaM-residue specific covalent inhibitors, a cell lysate-based Förster resonance energy transfer (FRET)-assay was furthermore established. This assay enabled us to measure the slow, residue-specific, covalent inhibition by ophiobolin A in the presence of other endogenous proteins. In conclusion, we have developed a panel of fluorescence-assays that allows identification of conventional and covalent CaM-inhibitors as potential disruptors of K-Ras driven cancer cell stemness.

Integrating linear optimization with structural modeling to increase HIV neutralization breadth

Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody.

In this article, we report a new approach for protein design, which combines traditional structural modeling with machine learning and integer programming. Using this method, we are able to design antibodies that are predicted to bind large panels of antigenically diverse HIV variants. The combination of methods from these fields allows us to surpass protein design limitations that have been seen up to this point. We predict that if we tested these modified antibodies against HIV variants they would have greater neutralization breadth than any antibodies seen to this point.

Bootstrapped Biocatalysis: Biofilm-Derived Materials as Reversibly Functionalizable Multienzyme Surfaces

Cell-free biocatalysis systems offer many benefits for chemical manufacturing, but their widespread applicability is hindered by high costs associated with enzyme purification, modification, and immobilization on solid substrates, in addition to the cost of the material substrates themselves. Herein, we report a "bootstrapped" biocatalysis substrate material that is produced directly in bacterial culture and is derived from biofilm matrix proteins, which self-assemble into a nanofibrous mesh. We demonstrate that this material can simultaneously purify and immobilize multiple enzymes site specifically and directly from crude cell lysates by using a panel of genetically programmed, mutually orthogonal conjugation domains. We further demonstrate the utility of the technique in a bienzymatic stereoselective reduction coupled with a cofactor recycling scheme. The domains allow for several cycles of selective removal and replacement of enzymes under mild conditions to regenerate the catalyst system.

Scratching the Surface: Resurfacing Proteins to Endow New Properties and Function

Protein engineering is an emerging discipline that dovetails modern molecular biology techniques with high-throughput screening, laboratory evolution technologies, and computational approaches to modify sequence, structure, and, in some cases, function and properties of proteins. The ultimate goal is to develop new proteins with improved or designer functions for use in biotechnology, medicine, and basic research. One way to engineer proteins is to change their solvent-exposed regions through focused or random "protein resurfacing." In this review we explain what protein resurfacing is, and discuss recent examples of how this strategy is used to generate proteins with altered or broadened recognition profiles, improved stability, solubility, and expression, cell-penetrating ability, and reduced immunogenicity. Additionally we comment on how these properties can be further improved using chemical resurfacing approaches. Protein resurfacing will likely play an increasingly important role as more biologics enter clinical use, and we present some arguments to support this view.

Searching for the Pareto frontier in multi-objective protein design

The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

A critical analysis of computational protein design with sparse residue interaction graphs

Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies.

Computational structure-based protein design algorithms have successfully redesigned proteins to fold and bind target substrates in vitro, and even in vivo. Because the complexity of a computational design increases dramatically with the number of mutable residues, many design algorithms employ cutoffs (distance or energy) to neglect some pairwise residue interactions, thereby reducing the effective search space and computational cost. However, the energies neglected by such cutoffs can add up, which may have nontrivial effects on the designed sequence and its function. To study the effects of using cutoffs on protein design, we computed the optimal sequence both with and without cutoffs, and showed that neglecting long-range interactions can significantly change the computed conformation and sequence. Designs on proteins with experimentally measured thermostability showed the benefits of computing the optimal sequences (and their conformations), both with and without cutoffs, efficiently and accurately. Therefore, we also showed that a provable, ensemble-based algorithm can efficiently compute the optimal conformation and sequence, both with and without applying cutoffs, by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine cutoffs with provable, ensemble-based algorithms to reap the computational efficiency of cutoffs while avoiding their potential inaccuracies.

The Framework of Computational Protein Design

Computational protein design (CPD) has established itself as a leading field in basic and applied science with a strong coupling between the two. Proteins are computationally designed from the level of amino acids to the level of a functional protein complex. Design targets range from increased thermo- (or other) stability to specific requested reactions such as protein-protein binding, enzymatic reactions, or nanotechnology applications. The design scheme may encompass small regions of the proteins or the entire protein. In either case, the design may aim at the side-chains or at the full backbone conformation. Herein, the main framework for the process is outlined highlighting key elements in the CPD iterative cycle. These include the very definition of CPD, the diverse goals of CPD, components of the CPD protocol, methods for searching sequence and structure space, scoring functions, and augmenting the CPD with other optimization tools. Taken together, this chapter aims to introduce the framework of CPD.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Bioorthogonal Chemoenzymatic Functionalization of Calmodulin for Bioconjugation Applications

Calmodulin (CaM) is a widely studied Ca(2+)-binding protein that is highly conserved across species and involved in many biological processes, including vesicle release, cell proliferation, and apoptosis. To facilitate biophysical studies of CaM, researchers have tagged and mutated CaM at various sites, enabling its conjugation to fluorophores, microarrays, and other reactive partners. However, previous attempts to add a reactive label to CaM for downstream studies have generally employed nonselective labeling methods or resulted in diminished CaM function. Here we report the first engineered CaM protein that undergoes site-specific and bioorthogonal labeling while retaining wild-type activity levels. By employing a chemoenzymatic labeling approach, we achieved selective and quantitative labeling of the engineered CaM protein with an N-terminal 12-azidododecanoic acid tag; notably, addition of the tag did not interfere with the ability of CaM to bind Ca(2+) or a partner protein. The specificity of our chemoenzymatic labeling approach also allowed for selective conjugation of CaM to reactive partners in bacterial cell lysates, without intermediate purification of the engineered protein. Additionally, we prepared CaM-affinity resins that were highly effective in purifying a representative CaM-binding protein, demonstrating that the engineered CaM remains active even after surface capture. Beyond studies of CaM and CaM-binding proteins, the protein engineering and surface capture methods described here should be translatable to other proteins and other bioconjugation applications.

Molecular mechanisms and design principles for promiscuous inhibitors to avoid drug resistance: lessons learned from HIV-1 protease inhibition

Molecular recognition is central to biology and ranges from highly selective to broadly promiscuous. The ability to modulate specificity at will is particularly important for drug development, and discovery of mechanisms contributing to binding specificity is crucial for our basic understanding of biology and for applications in health care. In this study, we used computational molecular design to create a large dataset of diverse small molecules with a range of binding specificities. We then performed structural, energetic, and statistical analysis on the dataset to study molecular mechanisms of achieving specificity goals. The work was done in the context of HIV‐1 protease inhibition and the molecular designs targeted a panel of wild‐type and drug‐resistant mutant HIV‐1 protease structures. The analysis focused on mechanisms for promiscuous binding to bind robustly even to resistance mutants. Broadly binding inhibitors tended to be smaller in size, more flexible in chemical structure, and more hydrophobic in nature compared to highly selective ones. Furthermore, structural and energetic analyses illustrated mechanisms by which flexible inhibitors achieved binding; we found ligand conformational adaptation near mutation sites and structural plasticity in targets through torsional flips of asymmetric functional groups to form alternative, compensatory packing interactions or hydrogen bonds. As no inhibitor bound to all variants, we designed small cocktails of inhibitors to do so and discovered that they often jointly covered the target set through mechanistic complementarity. Furthermore, using structural plasticity observed in experiments, and potentially in simulations, is suggested to be a viable means of designing adaptive inhibitors that are promiscuous binders. Proteins 2015; 83:351–372. © 2014 Wiley Periodicals, Inc.

Insights into molecular interactions between CaM and its inhibitors from molecular dynamics simulations and experimental data

In order to contribute to the structural basis for rational design of calmodulin (CaM) inhibitors, we analyzed the interaction of CaM with 14 classic antagonists and two compounds that do not affect CaM, using docking and molecular dynamics (MD) simulations, and the data were compared to available experimental data. The Ca(2+)-CaM-Ligands complexes were simulated 20 ns, with CaM starting in the "open" and "closed" conformations. The analysis of the MD simulations provided insight into the conformational changes undergone by CaM during its interaction with these ligands. These simulations were used to predict the binding free energies (ΔG) from contributions ΔH and ΔS, giving useful information about CaM ligand binding thermodynamics. The ΔG predicted for the CaM's inhibitors correlated well with available experimental data as the r(2) obtained was 0.76 and 0.82 for the group of xanthones. Additionally, valuable information is presented here: I) CaM has two preferred ligand binding sites in the open conformation known as site 1 and 4, II) CaM can bind ligands of diverse structural nature, III) the flexibility of CaM is reduced by the union of its ligands, leading to a reduction in the Ca(2+)-CaM entropy, IV) enthalpy dominates the molecular recognition process in the system Ca(2+)-CaM-Ligand, and V) the ligands making more extensive contact with the protein have higher affinity for Ca(2+)-CaM. Despite their limitations, docking and MD simulations in combination with experimental data continue to be excellent tools for research in pharmacology, toward a rational design of new drugs.

Morphological diversity and polymorphism of self-assembling collagen peptides controlled by length of hydrophobic domains

Synthetic collagen mimetic peptides are used to probe the role of hydrophobic forces in mediating protein self-assembly. Higher order association is an integral property of natural collagens, which assemble into fibers and meshes that comprise the extracellular matrix of connective tissues. The unique triple-helix fold fully exposes two-thirds of positions in the protein to solvent, providing ample opportunities for engineering interaction sites. Inclusion of just a few hydrophobic groups in a minimal peptide promotes a rich variety of self-assembly behaviors, resulting in hundred-nanometer to micron size nanodiscs and nanofibers. Morphology depends primarily on the length of hydrophobic domains. Peptide discs contain lipophilic domains capable of sequestering small hydrophobic dyes. Combining multiple peptide types result in composite structures of discs and fibers ranging from stars to plates-on-a-string. These systems provide valuable tools to shed insight into the fundamental principles underlying hydrophobicity-driven higher order protein association that will facilitate the design of self-assembling systems in biomaterials and nanomedical applications.

A "fuzzy"-logic language for encoding multiple physical traits in biomolecules

To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a "fuzzy"-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics.

Epitope discovery for a synthetic polymer nanoparticle: a new strategy for developing a peptide tag

We describe a novel epitope discovery strategy for creating an affinity agent/peptide tag pair. A synthetic polymer nanoparticle (NP) was used as the “bait” to catch an affinity peptide tag. Biotinylated peptide tag candidates of varied sequence and length were attached to an avidin platform and screened for affinity against the polymer NP. NP affinity for the avidin/peptide tag complexes was used to provide insight into factors that contribute NP/tag binding. The identified epitope sequence with an optimized length (tMel-tag) was fused to two recombinant proteins. The tagged proteins exhibited higher NP affinity than proteins without tags. The results establish that a fusion peptide tag consisting of optimized 15 amino acid residues can provide strong affinity to an abiotic polymer NP. The affinity and selectivity of NP/tMel-tag interactions were exploited for protein purification in conjunction with immobilized metal ion/His6-tag interactions to prepare highly purified recombinant proteins. This strategy makes available inexpensive, abiotic synthetic polymers as affinity agents for peptide tags and provides alternatives for important applications where more costly affinity agents are used.

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.

Importance of the interaction protein-protein of the CaM-PDE1A and CaM-MLCK complexes in the development of new anti-CaM drugs

Protein-protein interactions play central roles in physiological and pathological processes. The bases of the mechanisms of drug action are relevant to the discovery of new therapeutic targets. This work focuses on understanding the interactions in protein-protein-ligands complexes, using proteins calmodulin (CaM), human calcium/calmodulin-dependent 3',5'-cyclic nucleotide phosphodiesterase 1A active human (PDE1A), and myosin light chain kinase (MLCK) and ligands αII-spectrin peptide (αII-spec), and two inhibitors of CaM (chlorpromazine (CPZ) and malbrancheamide (MBC)). The interaction was monitored with a fluorescent biosensor of CaM (hCaM M124C-mBBr). The results showed changes in the affinity of CPZ and MBC depending on the CaM-protein complex under analysis. For the Ca(2+) -CaM, Ca(2+) -CaM-PDE1A, and Ca(2+) -CaM-MLCK complexes, CPZ apparent dissociation constants (Kds ) were 1.11, 0.28, and 0.55 μM, respectively; and for MBC Kds were 1.43, 1.10, and 0.61 μM, respectively. In competition experiments the addition of calmodulin binding peptide 1 (αII-spec) to Ca(2+) -hCaM M124C-mBBr quenched the fluorescence (Kd  = 2.55 ± 1.75 pM) and the later addition of MBC (up to 16 μM) did not affect the fluorescent signal. Instead, the additions of αII-spec to a preformed Ca(2+) -hCaM M124C-mBBr-MBC complex modified the fluorescent signal. However, MBC was able to displace the PDE1A and MLCK from its complex with Ca(2+) -CaM. In addition, docking studies were performed for all complexes with both ligands showing an excellent correlation with experimental data. These experiments may help to explain why in vivo many CaM drugs target prefer only a subset of the Ca(2+) -CaM regulated proteins and adds to the understanding of molecular interactions between protein complexes and small ligands.

Energy functions in de novo protein design: current challenges and future prospects

In the past decade, a concerted effort to successfully capture specific tertiary packing interactions produced specific three-dimensional structures for many de novo designed proteins that are validated by nuclear magnetic resonance and/or X-ray crystallographic techniques. However, the success rate of computational design remains low. In this review, we provide an overview of experimentally validated, de novo designed proteins and compare four available programs, RosettaDesign, EGAD, Liang-Grishin, and RosettaDesign-SR, by assessing designed sequences computationally. Computational assessment includes the recovery of native sequences, the calculation of sizes of hydrophobic patches and total solvent-accessible surface area, and the prediction of structural properties such as intrinsic disorder, secondary structures, and three-dimensional structures. This computational assessment, together with a recent community-wide experiment in assessing scoring functions for interface design, suggests that the next-generation protein-design scoring function will come from the right balance of complementary interaction terms. Such balance may be found when more negative experimental data become available as part of a training set.

A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds

The accurate design of new protein-protein interactions is a longstanding goal of computational protein design. However, most computationally designed interfaces fail to form experimentally. This investigation compares five previously described successful de novo interface designs with 158 failures. Both sets of proteins were designed with the molecular modeling program Rosetta. Designs were considered a success if a high-resolution crystal structure of the complex closely matched the design model and the equilibrium dissociation constant for binding was less than 10 μM. The successes and failures represent a wide variety of interface types and design goals including heterodimers, homodimers, peptide-protein interactions, one-sided designs (i.e., where only one of the proteins was mutated) and two-sided designs. The most striking feature of the successful designs is that they have fewer polar atoms at their interfaces than many of the failed designs. Designs that attempted to create extensive sets of interface-spanning hydrogen bonds resulted in no detectable binding. In contrast, polar atoms make up more than 40% of the interface area of many natural dimers, and native interfaces often contain extensive hydrogen bonding networks. These results suggest that Rosetta may not be accurately balancing hydrogen bonding and electrostatic energies against desolvation penalties and that design processes may not include sufficient sampling to identify side chains in preordered conformations that can fully satisfy the hydrogen bonding potential of the interface.

Computational protein design suggests that human PCNA-partner interactions are not optimized for affinity

Increasing the affinity of binding proteins is invaluable for basic and applied biological research. Currently, directed protein evolution experiments are the main approach for generating such proteins through the construction and screening of large mutant libraries. Proliferating cell nuclear antigen (PCNA) is an essential hub protein that interacts with many different partners to tightly regulate DNA replication and repair in all eukaryotes. Here, we used computational design to generate human PCNA mutants with enhanced affinity for several different partners. We identified double mutations in PCNA, outside the main partner binding site, that were predicted to increase PCNA-partner binding affinities compared to the wild-type protein by forming additional hydrophobic interactions with conserved residues in the PCNA partners. Affinity increases were experimentally validated with four different PCNA partners, demonstrating that computational design can reveal unexpected regions where affinity enhancements in natural systems are possible. The designed PCNA mutants can be used as a valuable tool for further examination of the regulation of PCNA-partner interactions during DNA replication and repair both in vitro and in vivo. More broadly, the ability to engineer affinity increases toward several PCNA partners suggests that interaction affinity is not an evolutionarily optimized trait of this system.

Retention of conformational entropy upon calmodulin binding to target peptides is driven by transient salt bridges

Calmodulin (CaM) is a highly flexible calcium-binding protein that mediates signal transduction through an ability to differentially bind to highly variable binding sequences in target proteins. To identify how binding affects CaM motions, and its relationship to conformational entropy and target peptide sequence, we have employed fully atomistic, explicit solvent molecular dynamics simulations of unbound CaM and CaM bound to five different target peptides. The calculated CaM conformational binding entropies correlate with experimentally derived conformational entropies with a correlation coefficient R(2) of 0.95. Selected side-chain interactions with target peptides restrain interhelical loop motions, acting to tune the conformational entropy of the bound complex via widely distributed CaM motions. In the complex with the most conformational entropy retention (CaM in complex with the neuronal nitric oxide synthase binding sequence), Lys-148 at the C-terminus of CaM forms transient salt bridges alternating between Glu side chains in the N-domain, the central linker, and the binding target. Additional analyses of CaM structures, fluctuations, and CaM-target interactions illuminate the interplay between electrostatic, side chain, and backbone properties in the ability of CaM to recognize and discriminate against targets by tuning its conformational entropy, and suggest a need to consider conformational dynamics in optimizing binding affinities.

Structural, kinetic, and thermodynamic studies of specificity designed HIV-1 protease

HIV-1 protease recognizes and cleaves more than 12 different substrates leading to viral maturation. While these substrates share no conserved motif, they are specifically selected for and cleaved by protease during viral life cycle. Drug resistant mutations evolve within the protease that compromise inhibitor binding but allow the continued recognition of all these substrates. While the substrate envelope defines a general shape for substrate recognition, successfully predicting the determinants of substrate binding specificity would provide additional insights into the mechanism of altered molecular recognition in resistant proteases. We designed a variant of HIV protease with altered specificity using positive computational design methods and validated the design using X-ray crystallography and enzyme biochemistry. The engineered variant, Pr3 (A28S/D30F/G48R), was designed to preferentially bind to one out of three of HIV protease's natural substrates; RT-RH over p2-NC and CA-p2. In kinetic assays, RT-RH binding specificity for Pr3 increased threefold compared to the wild-type (WT), which was further confirmed by isothermal titration calorimetry. Crystal structures of WT protease and the designed variant in complex with RT-RH, CA-p2, and p2-NC were determined. Structural analysis of the designed complexes revealed that one of the engineered substitutions (G48R) potentially stabilized heterogeneous flap conformations, thereby facilitating alternate modes of substrate binding. Our results demonstrate that while substrate specificity could be engineered in HIV protease, the structural pliability of protease restricted the propagation of interactions as predicted. These results offer new insights into the plasticity and structural determinants of substrate binding specificity of the HIV-1 protease.

Metal-mediated affinity and orientation specificity in a computationally designed protein homodimer

Computationally designing protein-protein interactions with high affinity and desired orientation is a challenging task. Incorporating metal-binding sites at the target interface may be one approach for increasing affinity and specifying the binding mode, thereby improving robustness of designed interactions for use as tools in basic research as well as in applications from biotechnology to medicine. Here we describe a Rosetta-based approach for the rational design of a protein monomer to form a zinc-mediated, symmetric homodimer. Our metal interface design, named MID1 (NESG target ID OR37), forms a tight dimer in the presence of zinc (MID1-zinc) with a dissociation constant <30 nM. Without zinc the dissociation constant is 4 μM. The crystal structure of MID1-zinc shows good overall agreement with the computational model, but only three out of four designed histidines coordinate zinc. However, a histidine-to-glutamate point mutation resulted in four-coordination of zinc, and the resulting metal binding site and dimer orientation closely matches the computational model (Cα rmsd = 1.4 Å).

Integration of cell-free protein coexpression with an enzyme-linked immunosorbent assay enables rapid analysis of protein-protein interactions directly from DNA

Assays that integrate detection of binding with cell-free protein expression directly from DNA can dramatically increase the pace at which protein-protein interactions (PPIs) can be analyzed by mutagenesis. In this study, we present a method that combines in vitro protein production with an enzyme-linked immunosorbent assay (ELISA) to measure PPIs. This method uses readily available commodity instrumentation and generic antibody-affinity tag interactions. It is straightforward and rapid to execute, enabling many interactions to be assessed in parallel. In traditional ELISAs, reporter complexes are assembled stepwise with one layer at a time. In the method presented here, all the members of the reporter complex are present and assembled together. The signal strength is dependent on all the intercomponent interaction affinities and concentrations. Although this assay is straightforward to execute, establishing proper conditions and analysis of the results require a thorough understanding of the processes that determine the signal strength. The formation of the fully assembled reporter sandwich can be modeled as a competition between Langmuir adsorption isotherms for the immobilized components and binding equilibria of the solution components. We have shown that modeling this process provides semiquantitative understanding of the effects of affinity and concentration and can guide strategies for the development of experimental protocols. We tested the method experimentally using the interaction between a synthetic ankyrin repeat protein (Off7) and maltose-binding protein. Measurements obtained for a collection of alanine mutations in the interface between these two proteins demonstrate that a range of affinities can be analyzed.

Anchored design of protein-protein interfaces

Background

Few existing protein-protein interface design methods allow for extensive backbone rearrangements during the design process. There is also a dichotomy between redesign methods, which take advantage of the native interface, and de novo methods, which produce novel binders.

Methodology

Here, we propose a new method for designing novel protein reagents that combines advantages of redesign and de novo methods and allows for extensive backbone motion. This method requires a bound structure of a target and one of its natural binding partners. A key interaction in this interface, the anchor, is computationally grafted out of the partner and into a surface loop on the design scaffold. The design scaffold's surface is then redesigned with backbone flexibility to create a new binding partner for the target. Careful choice of a scaffold will bring experimentally desirable characteristics into the new complex. The use of an anchor both expedites the design process and ensures that binding proceeds against a known location on the target. The use of surface loops on the scaffold allows for flexible-backbone redesign to properly search conformational space.

Conclusions and Significance

This protocol was implemented within the Rosetta3 software suite. To demonstrate and evaluate this protocol, we have developed a benchmarking set of structures from the PDB with loop-mediated interfaces. This protocol can recover the correct loop-mediated interface in 15 out of 16 tested structures, using only a single residue as an anchor.

A de novo protein binding pair by computational design and directed evolution

The de novo design of protein-protein interfaces is a stringent test of our understanding of the principles underlying protein-protein interactions and would enable unique approaches to biological and medical challenges. Here we describe a motif-based method to computationally design protein-protein complexes with native-like interface composition and interaction density. Using this method we designed a pair of proteins, Prb and Pdar, that heterodimerize with a Kd of 130 nM, 1000-fold tighter than any previously designed de novo protein-protein complex. Directed evolution identified two point mutations that improve affinity to 180 pM. Crystal structures of an affinity-matured complex reveal binding is entirely through the designed interface residues. Surprisingly, in the in vitro evolved complex one of the partners is rotated 180° relative to the original design model, yet still maintains the central computationally designed hotspot interaction and preserves the character of many peripheral interactions. This work demonstrates that high-affinity protein interfaces can be created by designing complementary interaction surfaces on two noninteracting partners and underscores remaining challenges.

Optimizing energy functions for protein-protein interface design

Protein design methods have been originally developed for the design of monomeric proteins. When applied to the more challenging task of protein–protein complex design, these methods yield suboptimal results. In particular, they often fail to recapitulate favorable hydrogen bonds and electrostatic interactions across the interface. In this work, we aim to improve the energy function of the protein design program ORBIT to better account for binding interactions between proteins. By using the advanced machine learning framework of conditional random fields, we optimize the relative importance of all the terms in the energy function, attempting to reproduce the native side-chain conformations in protein–protein interfaces. We evaluate the performance of several optimized energy functions, each describes the van der Waals interactions using a different potential. In comparison with the original energy function, our best energy function (a) incorporates a much “softer” repulsive van der Waals potential, suitable for the discrete rotameric representation of amino acid side chains; (b) does not penalize burial of polar atoms, reflecting the frequent occurrence of polar buried residues in protein–protein interfaces; and (c) significantly up-weights the electrostatic term, attesting to the high importance of these interactions for protein–protein complex formation. Using this energy function considerably improves side chain placement accuracy for interface residues in a large test set of protein–protein complexes. Moreover, the optimized energy function recovers the native sequences of protein–protein interface at a higher rate than the default function and performs substantially better in predicting changes in free energy of binding due to mutations.

Computational protein design using flexible backbone remodeling and resurfacing: case studies in structure-based antigen design

Computational protein design has promise for vaccine design and other applications. We previously transplanted the HIV 4E10 epitope onto non-HIV protein scaffolds for structural stabilization and immune presentation. Here, we developed two methods to optimize the structure of an antigen, flexible backbone remodeling and resurfacing, and we applied these methods to a 4E10 scaffold. In flexible-backbone remodeling, an existing backbone segment is replaced by a de novo designed segment of prespecified length and secondary structure. With remodeling, we replaced a potentially immunodominant domain on the scaffold with a helix-loop segment that made intimate contact to the protein core. All three domain trim designs tested experimentally had improved thermal stability and similar binding affinity for the 4E10 antibody compared to the parent scaffold. A crystal structure of one design had a 0.8 Å backbone RMSD to the computational model in the rebuilt region. Comparison of parent and trimmed scaffold reactivity to anti-parent sera confirmed the deletion of an immunodominant domain. In resurfacing, the surface of an antigen outside a target epitope is redesigned to obtain variants that maintain only the target epitope. Resurfaced variants of two scaffolds were designed in which 50 positions amounting to 40% of the protein sequences were mutated. Surface-patch analyses indicated that most potential antibody footprints outside the 4E10 epitope were altered. The resurfaced variants maintained thermal stability and binding affinity. These results indicate that flexible-backbone remodeling and resurfacing are useful tools for antigen optimization and protein engineering generally.

Improving computational protein design by using structure-derived sequence profile

Designing a protein sequence that will fold into a predefined structure is of both practical and fundamental interest. Many successful, computational designs in the last decade resulted from improved understanding of hydrophobic and polar interactions between side chains of amino acid residues in stabilizing protein tertiary structures. However, the coupling between main-chain backbone structure and local sequence has yet to be fully addressed. Here, we attempt to account for such coupling by using a sequence profile derived from the sequences of five residue fragments in a fragment library that are structurally matched to the five-residue segments contained in a target structure. We further introduced a term to reduce low complexity regions of designed sequences. These two terms together with optimized reference states for amino-acid residues were implemented in the RosettaDesign program. The new method, called RosettaDesign-SR, makes a 12% increase (from 34 to 46%) in fraction of proteins whose designed sequences are more than 35% identical to wild-type sequences. Meanwhile, it reduces 8% (from 22% to 14%) to the number of designed sequences that are not homologous to any known protein sequences according to psi-blast. More importantly, the sequences designed by RosettaDesign-SR have 2-3% more polar residues at the surface and core regions of proteins and these surface and core polar residues have about 4% higher sequence identity to wild-type sequences than by RosettaDesign. Thus, the proteins designed by RosettaDesign-SR should be less likely to aggregate and more likely to have unique structures due to more specific polar interactions.

Specificity in computational protein design

A long-standing goal of computational protein design is to create proteins similar to those found in Nature. One motivation is to harness the exquisite functional capabilities of proteins for our own purposes. The extent of similarity between designed and natural proteins also reports on how faithfully our models represent the selective pressures that determine protein sequences. As the field of protein design shifts emphasis from reproducing native-like protein structure to function, it has become important that these models treat the notion of specificity in molecular interactions. Although specificity may, in some cases, be achieved by optimization of a desired protein in isolation, methods have been developed to address directly the desire for proteins that exhibit specific functions and interactions.

An efficient algorithm for multistate protein design based on FASTER

Most of the methods that have been developed for computational protein design involve the selection of side-chain conformations in the context of a single, fixed main-chain structure. In contrast, multistate design (MSD) methods allow sequence selection to be driven by the energetic contributions of multiple structural or chemical states simultaneously. This methodology is expected to be useful when the design target is an ensemble of related states rather than a single structure, or when a protein sequence must assume several distinct conformations to function. MSD can also be used with explicit negative design to suggest sequences with altered structural, binding, or catalytic specificity. We report implementation details of an efficient multistate design optimization algorithm based on FASTER (MSD-FASTER). We subjected the algorithm to a battery of computational tests and found it to be generally applicable to various multistate design problems; designs with a large number of states and many designed positions are completely feasible. A direct comparison of MSD-FASTER and multistate design Monte Carlo indicated that MSD-FASTER discovers low-energy sequences much more consistently. MSD-FASTER likely performs better because amino acid substitutions are chosen on an energetic basis rather than randomly, and because multiple substitutions are applied together. Through its greater efficiency, MSD-FASTER should allow protein designers to test experimentally better-scoring sequences, and thus accelerate progress in the development of improved scoring functions and models for computational protein design.

What makes Ras an efficient molecular switch: a computational, biophysical, and structural study of Ras-GDP interactions with mutants of Raf

Ras is a small GTP-binding protein that is an essential molecular switch for a wide variety of signaling pathways including the control of cell proliferation, cell cycle progression and apoptosis. In the GTP-bound state, Ras can interact with its effectors, triggering various signaling cascades in the cell. In the GDP-bound state, Ras looses its ability to bind to known effectors. The interaction of the GTP-bound Ras (Ras(GTP)) with its effectors has been studied intensively. However, very little is known about the much weaker interaction between the GDP-bound Ras (Ras(GDP)) and Ras effectors. We investigated the factors underlying the nucleotide-dependent differences in Ras interactions with one of its effectors, Raf kinase. Using computational protein design, we generated mutants of the Ras-binding domain of Raf kinase (Raf) that stabilize the complex with Ras(GDP). Most of our designed mutations narrow the gap between the affinity of Raf for Ras(GTP) and Ras(GDP), producing the desired shift in binding specificity towards Ras(GDP). A combination of our best designed mutation, N71R, with another mutation, A85K, yielded a Raf mutant with a 100-fold improvement in affinity towards Ras(GDP). The Raf A85K and Raf N71R/A85K mutants were used to obtain the first high-resolution structures of Ras(GDP) bound to its effector. Surprisingly, these structures reveal that the loop on Ras previously termed the switch I region in the Ras(GDP).Raf mutant complex is found in a conformation similar to that of Ras(GTP) and not Ras(GDP). Moreover, the structures indicate an increased mobility of the switch I region. This greater flexibility compared to the same loop in Ras(GTP) is likely to explain the natural low affinity of Raf and other Ras effectors to Ras(GDP). Our findings demonstrate that an accurate balance between a rigid, high-affinity conformation and conformational flexibility is required to create an efficient and stringent molecular switch.

Computer-aided design of functional protein interactions

Predictive methods for the computational design of proteins search for amino acid sequences adopting desired structures that perform specific functions. Typically, design of 'function' is formulated as engineering new and altered binding activities into proteins. Progress in the design of functional protein-protein interactions is directed toward engineering proteins to precisely control biological processes by specifically recognizing desired interaction partners while avoiding competitors. The field is aiming for strategies to harness recent advances in high-resolution computational modeling-particularly those exploiting protein conformational variability-to engineer new functions and incorporate many functional requirements simultaneously.

Accurate prediction for atomic-level protein design and its application in diversifying the near-optimal sequence space

The task of engineering a protein to assume a target three-dimensional structure is known as protein design. Computational search algorithms are devised to predict a minimal energy amino acid sequence for a particular structure. In practice, however, an ensemble of low-energy sequences is often sought. Primarily, this is performed because an individual predicted low-energy sequence may not necessarily fold to the target structure because of both inaccuracies in modeling protein energetics and the nonoptimal nature of search algorithms employed. Additionally, some low-energy sequences may be overly stable and thus lack the dynamic flexibility required for biological functionality. Furthermore, the investigation of low-energy sequence ensembles will provide crucial insights into the pseudo-physical energy force fields that have been derived to describe structural energetics for protein design. Significantly, numerous studies have predicted low-energy sequences, which were subsequently synthesized and demonstrated to fold to desired structures. However, the characterization of the sequence space defined by such energy functions as compatible with a target structure has not been performed in full detail. This issue is critical for protein design scientists to successfully continue using these force fields at an ever-increasing pace and scale. In this paper, we present a conceptually novel algorithm that rapidly predicts the set of lowest energy sequences for a given structure. Based on the theory of probabilistic graphical models, it performs efficient inspection and partitioning of the near-optimal sequence space, without making any assumptions of positional independence. We benchmark its performance on a diverse set of relevant protein design examples and show that it consistently yields sequences of lower energy than those derived from state-of-the-art techniques. Thus, we find that previously presented search techniques do not fully depict the low-energy space as precisely. Examination of the predicted ensembles indicates that, for each structure, the amino acid identity at a majority of positions must be chosen extremely selectively so as to not incur significant energetic penalties. We investigate this high degree of similarity and demonstrate how more diverse near-optimal sequences can be predicted in order to systematically overcome this bottleneck for computational design. Finally, we exploit this in-depth analysis of a collection of the lowest energy sequences to suggest an explanation for previously observed experimental design results. The novel methodologies introduced here accurately portray the sequence space compatible with a protein structure and further supply a scheme to yield heterogeneous low-energy sequences, thus providing a powerful instrument for future work on protein design.

Exploring functional roles of multibinding protein interfaces

Cellular processes are highly interconnected and many proteins are shared in different pathways. Some of these shared proteins or protein families may interact with diverse partners using the same interface regions; such multibinding proteins are the subject of our study. The main goal of our study is to attempt to decipher the mechanisms of specific molecular recognition of multiple diverse partners by promiscuous protein regions. To address this, we attempt to analyze the physicochemical properties of multibinding interfaces and highlight the major mechanisms of functional switches realized through multibinding. We find that only 5% of protein families in the structure database have multibinding interfaces, and multibinding interfaces do not show any higher sequence conservation compared with the background interface sites. We highlight several important functional mechanisms utilized by multibinding families. (a) Overlap between different functional pathways can be prevented by the switches involving nearby residues of the same interfacial region. (b) Interfaces can be reused in pathways where the substrate should be passed from one protein to another sequentially. (c) The same protein family can develop different specificities toward different binding partners reusing the same interface; and finally, (d) inhibitors can attach to substrate binding sites as substrate mimicry and thereby prevent substrate binding.

Design, expression and characterization of mutants of fasciculin optimized for interaction with its target, acetylcholinesterase

Predicting mutations that enhance protein–protein affinity remains a challenging task, especially for high-affinity complexes. To test our capability to improve the affinity of such complexes, we studied interaction of acetylcholinesterase with the snake toxin, fasciculin. Using the program ORBIT, we redesigned fasciculin's sequence to enhance its interactions with Torpedo californica acetylcholinesterase. Mutations were predicted in 5 out of 13 interfacial residues on fasciculin, preserving most of the polar inter-molecular contacts seen in the wild-type toxin/enzyme complex. To experimentally characterize fasciculin mutants, we developed an efficient strategy to over-express the toxin in Escherichia coli, followed by refolding to the native conformation. Despite our predictions, a designed quintuple fasciculin mutant displayed reduced affinity for the enzyme. However, removal of a single mutation in the designed sequence produced a quadruple mutant with improved affinity. Moreover, one designed mutation produced 7-fold enhancement in affinity for acetylcholinesterase. This led us to reassess our criteria for enhancing affinity of the toxin for the enzyme. We observed that the change in the predicted inter-molecular energy, rather than in the total energy, correlates well with the change in the experimental free energy of binding, and hence may serve as a criterion for enhancement of affinity in protein–protein complexes.

Thermostable variants of cocaine esterase for long-time protection against cocaine toxicity

Enhancing cocaine metabolism by administration of cocaine esterase (CocE) has been recognized as a promising treatment strategy for cocaine overdose and addiction, because CocE is the most efficient native enzyme for metabolizing the naturally occurring cocaine yet identified. A major obstacle to the clinical application of CocE is the thermoinstability of native CocE with a half-life of only a few minutes at physiological temperature (37 degrees C). Here we report thermostable variants of CocE developed through rational design using a novel computational approach followed by in vitro and in vivo studies. This integrated computational-experimental effort has yielded a CocE variant with a approximately 30-fold increase in plasma half-life both in vitro and in vivo. The novel design strategy can be used to develop thermostable mutants of any protein.

The better tag remains unseen

One of the most crucial steps in protein structure determination by nuclear magnetic resonance (NMR) spectroscopy is the preparation of highly concentrated and well behaving protein samples. Here we present a system of modular tags which allows for high level expression, sophisticated purification of full-length protein, and solubility enhancement while keeping the amount of additional resonances low. This system consists of two different expression constructs and utilizes the tight binding of human calmodulin (hCaM) to the calmodulin binding peptide (CBP), which has already been used as a purification tag.