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Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence-and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity. [ABSTRACT FROM AUTHOR]

Acid‐β‐glucosidase (GCase, EC3.2.1.45), the lysosomal enzyme which hydrolyzes the simple glycosphingolipid, glucosylceramide (GlcCer), is encoded by the GBA1 gene. Biallelic mutations in GBA1 cause the human inherited metabolic disorder, Gaucher disease (GD), in which GlcCer accumulates, while heterozygous GBA1 mutations are the highest genetic risk factor for Parkinson's disease (PD). Recombinant GCase (e.g., Cerezyme®) is produced for use in enzyme replacement therapy for GD and is largely successful in relieving disease symptoms, except for the neurological symptoms observed in a subset of patients. As a first step toward developing an alternative to the recombinant human enzymes used to treat GD, we applied the PROSS stability‐design algorithm to generate GCase variants with enhanced stability. One of the designs, containing 55 mutations compared to wild‐type human GCase, exhibits improved secretion and thermal stability. Furthermore, the design has higher enzymatic activity than the clinically used human enzyme when incorporated into an AAV vector, resulting in a larger decrease in the accumulation of lipid substrates in cultured cells. Based on stability‐design calculations, we also developed a machine learning‐based approach to distinguish benign from deleterious (i.e., disease‐causing) GBA1 mutations. This approach gave remarkably accurate predictions of the enzymatic activity of single‐nucleotide polymorphisms in the GBA1 gene that are not currently associated with GD or PD. This latter approach could be applied to other diseases to determine risk factors in patients carrying rare mutations. [ABSTRACT FROM AUTHOR]

Controlled degradation of proteins is necessary for ensuring their abundance and sustaining a healthy and accurately functioning proteome. One of the degradation routes involves the uncapped 20S proteasome, which cleaves proteins with a partially unfolded region, including those that are damaged or contain intrinsically disordered regions. This degradation route is tightly controlled by a recently discovered family of proteins named Catalytic Core Regulators (CCRs). Here, we show that CCRs function through an allosteric mechanism, coupling the physical binding of the PSMB4 β-subunit with attenuation of the complex's three proteolytic activities. In addition, by dissecting the structural properties that are required for CCR-like function, we could recapitulate this activity using a designed protein that is half the size of natural CCRs. These data uncover an allosteric path that does not involve the proteasome's enzymatic subunits but rather propagates through the non-catalytic subunit PSMB4. This way of 20S proteasome-specific attenuation opens avenues for decoupling the 20S and 26S proteasome degradation pathways as well as for developing selective 20S proteasome inhibitors. A family of regulators named Catalytic Core Regulators (CCRs) oversees the function of the 20S proteasome. Here, the authors show that CCRs function through an allosteric mechanism, coupling the physical binding of the PSMB4 β-subunit with attenuation of the proteasome three proteolytic activities. [ABSTRACT FROM AUTHOR]

Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins. Mutations in a protein active site can alter function in useful ways, but the active site is sensitive to changes. Here the authors present a general strategy to design combinatorial mutation libraries. Applied to GFP, the authors isolate thousands of fluorescent designs that exhibit large and useful changes in spectral properties. [ABSTRACT FROM AUTHOR]

Until now, membrane-protein stabilization has relied on iterations of mutations and screening. We now validate a one-step algorithm, mPROSS, for stabilizing membrane proteins directly from an AlphaFold2 model structure. Applied to the lipid-generating enzyme, ceramide synthase, 37 designed mutations lead to a more stable form of human CerS2. Together with molecular dynamics simulations, we propose a pathway by which substrates might be delivered to the ceramide synthases. Membrane proteins are involved in many critical cellular pathways. Here, authors use a combination of structural predictions, an algorithm for stabilizing membrane proteins, and molecular dynamics to reveal a putative mechanism for the action of ceramide synthases. [ABSTRACT FROM AUTHOR]

Several methods have been developed to explore interactions among water-soluble proteins or regions of proteins. However, techniques to target transmembrane domains (TMDs) have not been examined thoroughly despite their importance. Here, we developed a computational approach to design sequences that specifically modulate protein-protein interactions in the membrane. To illustrate this method, we demonstrated that BclxL can interact with other members of the B cell lymphoma 2 (Bcl2) family through the TMD and that these interactions are required for BclxL control of cell death. Next, we designed sequences that specifically recognize and sequester the TMD of BclxL. Hence, we were able to prevent BclxL intramembrane interactions and cancel its antiapoptotic effect. These results advance our understanding of protein-protein interactions in membranes and provide a means to modulate them. Moreover, the success of our approach may trigger the development of a generation of inhibitors targeting interactions between TMDs. [ABSTRACT FROM AUTHOR]

The branched-chain amino acids (BCAAs) leucine, isoleucine and valine are synthesized via a common biosynthetic pathway. Ketol-acid reductoisomerase (KARI) is the second enzyme in this pathway. In addition to its role in BCAA biosynthesis, KARI catalyzes two rate-limiting steps that are key components of a cell-free biofuel biosynthesis route. For industrial applications, reaction temperature and enzyme stability are key factors that affect process robustness and product yield. Here, we have solved the cryo-EM structure (2.94 Å resolution) of a homododecameric Class I KARI (from Campylobacter jejuni) and demonstrated how a triad of amino acid side chains plays a crucial role in promoting the oligomerization of this enzyme. Importantly, both its thermal and solvent stability are greatly enhanced in the dodecameric state when compared to its dimeric counterpart (apparent melting temperatures (Tm) of 83.1 °C and 51.5 °C, respectively). We also employed protein design (PROSS) for a tetrameric Class II KARI (from Escherichia coli) to generate a variant with improved thermal and solvent stabilities. In total, 34 mutations were introduced, which did not affect the oligomeric state of this enzyme but resulted in a fully functional catalyst with a significantly elevated Tm (58.5 °C vs. 47.9 °C for the native version). [ABSTRACT FROM AUTHOR]

Imaging of gene-expression patterns in live animals is difficult to achieve with fluorescent proteins because tissues are opaque to visible light. Imaging of transgene expression with magnetic resonance imaging (MRI), which penetrates to deep tissues, has been limited by single reporter visualization capabilities. Moreover, the low-throughput capacity of MRI limits large-scale mutagenesis strategies to improve existing reporters. Here we develop an MRI system, called GeneREFORM, comprising orthogonal reporters for two-color imaging of transgene expression in deep tissues. Starting from two promiscuous deoxyribonucleoside kinases, we computationally designed highly active, orthogonal enzymes ('reporter genes') that specifically phosphorylate two MRI-detectable synthetic deoxyribonucleosides ('reporter probes'). Systemically administered reporter probes exclusively accumulate in cells expressing the designed reporter genes, and their distribution is displayed as pseudo-colored MRI maps based on dynamic proton exchange for noninvasive visualization of transgene expression. We envision that future extensions of GeneREFORM will pave the way to multiplexed deep-tissue mapping of gene expression in live animals. MRI detection of transgene expression in animals is expanded to two colors. [ABSTRACT FROM AUTHOR]

The ability to alter the genomes of living cells is key to understanding how genes influence the functions of organisms and will be critical to modify living systems for useful purposes. However, this promise has long been limited by the technical challenges involved in genetic engineering. Recent advances in gene editing have bypassed some of these challenges but they are still far from ideal. Here we use FuncLib to computationally design Cas9 enzymes with substantially higher donor-independent editing activities. We use genetic circuits linked to cell survival in yeast to quantify Cas9 activity and discover synergistic interactions between engineered regions. These hyperactive Cas9 variants function efficiently in mammalian cells and introduce larger and more diverse pools of insertions and deletions into targeted genomic regions, providing tools to enhance and expand the possible applications of CRISPR-based gene editing. The ability to alter the genomes of living cells is key to understanding how genes influence the functions of organisms and will be critical to modify living systems for useful purposes. Here, the authors use computational design to discover Cas9 enzymes with increased activity. [ABSTRACT FROM AUTHOR]

Lassa virus (LASV) is a human pathogen, causing substantial morbidity and mortality1,2. Similar to other Arenaviridae, it presents a class-I spike complex on its surface that facilitates cell entry. The virus’s cellular receptor is matriglycan, a linear carbohydrate that is present on α-dystroglycan3,4, but the molecular mechanism that LASV uses to recognize this glycan is unknown. In addition, LASV and other arenaviruses have a unique signal peptide that forms an integral and functionally important part of the mature spike5–8; yet the structure, function and topology of the signal peptide in the membrane remain uncertain9–11. Here we solve the structure of a complete native LASV spike complex, finding that the signal peptide crosses the membrane once and that its amino terminus is located in the extracellular region. Together with a double-sided domain-switching mechanism, the signal peptide helps to stabilize the spike complex in its native conformation. This structure reveals that the LASV spike complex is preloaded with matriglycan, suggesting the mechanism of binding and rationalizing receptor recognition by α-dystroglycan-tropic arenaviruses. This discovery further informs us about the mechanism of viral egress and may facilitate the rational design of novel therapeutics that exploit this binding site.The structure of the complete native spike complex of Lassa virus reveals its membrane topology and the matriglycan-depended recognition of its α-dystroglycan cellular receptor. [ABSTRACT FROM AUTHOR]

Stabilizing antigenic proteins as vaccine immunogens or diagnostic reagents is a stringent case of protein engineering and design as the exterior surface must maintain recognition by receptor(s) and antigen—specific antibodies at multiple distinct epitopes. This is a challenge, as stability enhancing mutations must be focused on the protein core, whereas successful computational stabilization algorithms typically select mutations at solvent-facing positions. In this study, we report the stabilization of SARS-CoV-2 Wuhan Hu-1 Spike receptor binding domain using a combination of deep mutational scanning and computational design, including the FuncLib algorithm. Our most successful design encodes I358F, Y365W, T430I, and I513L receptor binding domain mutations, maintains recognition by the receptor ACE2 and a panel of different anti-receptor binding domain monoclonal antibodies, is between 1 and 2°C more thermally stable than the original receptor binding domain using a thermal shift assay, and is less proteolytically sensitive to chymotrypsin and thermolysin than the original receptor binding domain. Our approach could be applied to the computational stabilization of a wide range of proteins without requiring detailed knowledge of active sites or binding epitopes. We envision that this strategy may be particularly powerful for cases when there are multiple or unknown binding sites. [ABSTRACT FROM AUTHOR]

Dan (Danny) Tawfik, a leader in biochemistry and protein evolution, died due to a fatal climbing accident on May 4th, 2021. Later, in collaboration with both of us, we used atomistic protein-design calculations to dramatically stabilize proteins [17], enabling the laboratory evolution of even more efficient enzymes for breaking down nerve agents [9]. While the above highlights Danny's unique scientific contribution, to us and an entire generation of young protein scientists, Danny's mentorship, boldness in pursuing new ideas, and his boundless generosity in sharing his insights and time have been no less inspiring. Over the past decade, Danny's focus shifted from studying the small but powerful steps by which evolution modulates enzyme activity to investigate the emergence of proteins at the dawn of protein evolution. [Extracted from the article]

Pseudomonas aeruginosa is an opportunistic human pathogen that develops difficult-to-treat biofilms in immunocompromised individuals, cystic fibrosis patients, and in chronic wounds. P. aeruginosa has an arsenal of physiological attributes that enable it to evade standard antibiotic treatments, particularly in the context of biofilms where it grows slowly and becomes tolerant to many drugs. One of its survival strategies involves the production of the redoxactive phenazine, pyocyanin, which promotes biofilm development. We previously identified an enzyme, PodA, that demethylated pyocyanin and disrupted P. aeruginosa biofilm development in vitro. Here, we asked if this protein could be used as a potential therapeutic for P. aeruginosa infections together with tobramycin, an antibiotic typically used in the clinic. A major roadblock to answering this question was the poor yield and stability of wild-type PodA purified from standard Escherichia coli overexpression systems. We hypothesized that the insufficient yields were due to poor packing within PodA's obligatory homotrimeric interfaces. We therefore applied the protein design algorithm, AffiLib, to optimize the symmetric core of this interface, resulting in a design that incorporated five mutations leading to a 20-fold increase in protein yield from heterologous expression and purification and a substantial increase in stability to environmental conditions. The addition of the designed PodA with tobramycin led to increased killing of P. aeruginosa cultures under oxic and hypoxic conditions in both the planktonic and biofilm states. This study highlights the potential for targeting extracellular metabolites to assist the control of P. aeruginosa biofilms that tolerate conventional antibiotic treatment. [ABSTRACT FROM AUTHOR]

The functional sites of many protein families are dominated by diverse backbone regions that lack secondary structure (loops) but fold stably into their functionally competent state. Nevertheless, the design of structured loop regions from scratch, especially in functional sites, has met with great difficulty. We therefore developed an approach, called AbDesign, to exploit the natural modularity of many protein families and computationally assemble a large number of new backbones by combining naturally occurring modular fragments. This strategy yielded large, atomically accurate, and highly efficient proteins, including antibodies and enzymes exhibiting dozens of mutations from any natural protein. The combinatorial backbone‐conformation space that can be accessed by AbDesign even for a modestly sized family of homologs may exceed the diversity in the entire PDB, providing the sub‐Ångstrom level of control over the positioning of active‐site groups that is necessary for obtaining highly active proteins. This manuscript describes how to implement the pipeline using code that is freely available at https://github.com/Fleishman‐Lab/AbDesign_for_enzymes. [ABSTRACT FROM AUTHOR]

Many human pathogens use host cell‐surface receptors to attach and invade cells. Often, the host‐pathogen interaction affinity is low, presenting opportunities to block invasion using a soluble, high‐affinity mimic of the host protein. The Plasmodium falciparum reticulocyte‐binding protein homolog 5 (RH5) provides an exciting candidate for mimicry: it is highly conserved and its moderate affinity binding to the human receptor basigin (KD ≥1 μM) is an essential step in erythrocyte invasion by this malaria parasite. We used deep mutational scanning of a soluble fragment of human basigin to systematically characterize point mutations that enhance basigin affinity for RH5 and then used Rosetta to design a variant within the sequence space of affinity‐enhancing mutations. The resulting seven‐mutation design exhibited 1900‐fold higher affinity (KD approximately 1 nM) for RH5 with a very slow binding off rate (0.23 h−1) and reduced the effective Plasmodium growth‐inhibitory concentration by at least 10‐fold compared to human basigin. The design provides a favorable starting point for engineering on‐rate improvements that are likely to be essential to reach therapeutically effective growth inhibition. [ABSTRACT FROM AUTHOR]

Antibodies developed for research and clinical applications may exhibit suboptimal stability, expressibility, or affinity. Existing optimization strategies focus on surface mutations, whereas natural affinity maturation also introduces mutations in the antibody core, simultaneously improving stability and affinity. To systematically map the mutational tolerance of an antibody variable fragment (Fv), we performed yeast display and applied deep mutational scanning to an anti-lysozyme antibody and found that many of the affinity-enhancing mutations clustered at the variable light-heavy chain interface, within the antibody core. Rosetta design combined enhancing mutations, yielding a variant with tenfold higher affinity and substantially improved stability. To make this approach broadly accessible, we developed AbLIFT, an automated web server that designs multipoint core mutations to improve contacts between specific Fv light and heavy chains (). We applied AbLIFT to two unrelated antibodies targeting the human antigens VEGF and QSOX1. Strikingly, the designs improved stability, affinity, and expression yields. The results provide proof-of-principle for bypassing laborious cycles of antibody engineering through automated computational affinity and stability design. [ABSTRACT FROM AUTHOR]

Protein networks in all organisms comprise homologous interacting pairs. In these networks, some proteins are specific, interacting with one or a few binding partners, whereas others are multispecific and bind a range of targets. We describe an algorithm that starts from an interacting pair and designs dozens of new pairs with diverse backbone conformations at the binding site as well as new binding orientations and sequences. Applied to a high-affinity bacterial pair, the algorithm results in 18 new ones, with cognate affinities from pico- to micromolar. Three pairs exhibit 3-5 orders of magnitude switch in specificity relative to the wild type, whereas others are multispecific, collectively forming a protein-interaction network. Crystallographic analysis confirms design accuracy, including in new backbones and polar interactions. Preorganized polar interaction networks are responsible for high specificity, thus defining design principles that can be applied to program synthetic cellular interaction networks of desired affinity and specificity. [ABSTRACT FROM AUTHOR]

Photorespiration recycles ribulose-1,5-bisphosphate carboxylase/ oxygenase (Rubisco) oxygenation product, 2-phosphoglycolate, back into the Calvin Cycle. Natural photorespiration, however, limits agricultural productivity by dissipating energy and releasing CO2. Several photorespiration bypasses have been previously suggested but were limited to existing enzymes and pathways that release CO2. Here, we harness the power of enzyme and metabolic engineering to establish synthetic routes that bypass photorespiration without CO2 release. By defining specific reaction rules, we systematically identified promising routes that assimilate 2-phosphoglycolate into the Calvin Cycle without carbon loss. We further developed a kinetic--stoichiometric model that indicates that the identified synthetic shunts could potentially enhance carbon fixation rate across the physiological range of irradiation and CO2, even if most of their enzymes operate at a tenth of Rubisco's maximal carboxylation activity. Glycolate reduction to glycolaldehyde is essential for several of the synthetic shunts but is not known to occur naturally. We, therefore, used computational design and directed evolution to establish this activity in two sequential reactions. An acetyl-CoA synthetase was engineered for higher stability and glycolyl-CoA synthesis. A propionyl-CoA reductase was engineered for higher selectivity for glycolyl-CoA and for use of NADPH over NAD+, thereby favoring reduction over oxidation. The engineered glycolate reduction module was then combined with down-stream condensation and assimilation of glycolaldehyde to ribulose 1,5-bisphosphate, thus providing proof of principle for a carbon-conserving photorespiration pathway. [ABSTRACT FROM AUTHOR]

Automated design of enzymes with wild-type-like catalytic properties has been a long- standing but elusive goal. Here, we present a general, automated method for enzyme design through combinatorial backbone assembly. Starting from a set of homologous yet structurally diverse enzyme structures, the method assembles new backbone combinations and uses Rosetta to optimize the amino acid sequence, while conserving key catalytic residues. We apply this method to two unrelated enzyme families with TIM-barrel folds, glycoside hydrolase 10 (GH10) xylanases and phosphotriesterase-like lactonases (PLLs), designing 43 and 34 proteins, respectively. Twenty-one GH10 and seven PLL designs are active, including designs derived from templates with <25% sequence identity. Moreover, four designs are as active as natural enzymes in these families. Atomic accuracy in a high-activity GH10 design is further confirmed by crystallographic analysis. Thus, combinatorial-backbone assembly and design may be used to generate stable, active, and structurally diverse enzymes with altered selectivity or activity. [ABSTRACT FROM AUTHOR]

Proteins are increasingly used in basic and applied biomedical research. Many proteins, however, are only marginally stable and can be expressed in limited amounts, thus hampering research and applications. Research has revealed the thermodynamic, cellular, and evolutionary principles and mechanisms that underlie marginal stability. With this growing understanding, computational stability design methods have advanced over the past two decades starting from methods that selectively addressed only some aspects of marginal stability. Current methods are more general and, by combining phylogenetic analysis with atomistic design, have shown drastic improvements in solubility, thermal stability, and aggregation resistance while maintaining the protein's primary molecular activity. Stability design is opening the way to rational engineering of improved enzymes, therapeutics, and vaccines and to the application of protein design methodology to large proteins and molecular activities that have proven challenging in the past. [ABSTRACT FROM AUTHOR]

Natural proteins must both fold into a stable conformation and exert their molecular function. To date, computational design has successfully produced stable and atomically accurate proteins by using so-called "ideal" folds rich in regular secondary structures and almost devoid of loops and destabilizing elements, such as cavities. Molecular function, such as binding and catalysis, however, often demands nonideal features, including large and irregular loops and buried polar interaction networks, which have remained challenging for fold design. Through five design/experiment cycles, we learned principles for designing stable and functional antibody variable fragments (Fvs). Specifically, we (i) used sequence-design constraints derived from antibody multiple-sequence alignments, and (ii) during backbone design, maintained stabilizing interactions observed in natural antibodies between the framework and loops of complementarity-determining regions (CDRs) 1 and 2. Designed Fvs bound their ligands with midnanomolar affinities and were as stable as natural antibodies, despite having >30 mutations from mammalian antibody germlines. Furthermore, crystallographic analysis demonstrated atomic accuracy throughout the framework and in four of six CDRs in one design and atomic accuracy in the entire Fv in another. The principles we learned are general, and can be implemented to design other nonideal folds, generating stable, specific, and precise antibodies and enzymes. [ABSTRACT FROM AUTHOR]

While potent monoclonal antibodies against ricin were introduced over the years, the question whether increasing antibody affinity enables better toxin neutralization was not fully addressed yet. The aim of this study was to characterize the contribution of antibody affinity to the ricin neutralization potential of the antibody. cHD23 monoclonal antibody that targets the toxin B-subunit and interferes with its binding to membranal receptors, was isolated. In order to create antibody clones with improved affinity toward ricin, a scFv-phage display library containing mutated versions of the variable regions of cHD23 was constructed and clones with improved binding of ricin were isolated. Structural modeling of these mutants suggests that the inserted mutations may increase the antibody conformational flexibility thus improving its ability to bind ricin. While it was found that the selected clones exhibited improved neutralization of ricin, the correlation between the KD values and potency was only minor (r = 0.55). However, a positive correlation (r = 0.84) exist between the off-rate values (koff) of the affinity matured clones and their ability to neutralize ricin. As cell membranes display inordinately large amounts of potential surface binding sites for ricin, it is suggested that antibodies with improved off-rate values block the ability of the toxin to bind to target receptors, in a highly efficient manner. Currently, antibody-based therapy is the most effective treatment for ricin intoxication and it is anticipated that the findings of this study will provide useful information and a possible strategy to design an improved antibody-based therapy for the toxin. [ABSTRACT FROM AUTHOR]

Improving an enzyme's initially low catalytic efficiency with a new target substrate by an order of magnitude or two may require only a few rounds of mutagenesis and screening or selection. However, subsequent rounds of optimization tend to yield decreasing degrees of improvement (diminishing returns) eventually leading to an optimization plateau. We aimed to optimize the catalytic efficiency of bacterial phosphotriesterase (PTE) toward V-type nerve agents. Previously, we improved the catalytic efficiency of wild-type PTE toward the nerve agent VX by 500-fold, to a catalytic efficiency (kcat/KM) of 5 × 106M-1 min-1. However, effective in vivo detoxification demands an enzyme with a catalytic efficiency of >107M-1 min-1. Here, following eight additional rounds of directed evolution and the computational design of a stabilized variant, we evolved PTE variants that detoxify VX with a kcat/KM ≥ 5 × 107M-1 min-1 and Russian VX (RVX) with a kcat/KM ≥ 107M-1 min-1. These final 10-fold improvements were the most time consuming and laborious, as most libraries yielded either minor or no improvements. Stabilizing the evolving enzyme, and avoiding tradeoffs in activity with different substrates, enabled us to obtain further improvements beyond the optimization plateau and evolve PTE variants that were overall improved by >5000-fold with VX and by >17 000-fold with RVX. The resulting variants also hydrolyze G-type nerve agents with high efficiency (GA, GB at kcat/KM > 5 × 107M-1 min-1) and can thus serve as candidates for broadspectrum nerve-agent prophylaxis and post-exposure therapy using low enzyme doses. [ABSTRACT FROM AUTHOR]

Allosteric regulation underlies living cells' ability to sense changes in nutrient and signaling-molecule concentrations, but the ability to computationally design allosteric regulation into non-allosteric proteins has been elusive. Allosteric-site design is complicated by the requirement to encode the relative stabilities of active and inactive conformations of the same protein in the presence and absence of both ligand and effector. To address this challenge, we used Rosetta to design the backbone of the flexible heavy-chain complementarity-determining region 3 (HCDR3), and used geometric matching and sequence optimization to place a Zn2+-coordination site in a fluorescein-binding antibody. We predicted that due to HCDR3's flexibility, the fluorescein-binding pocket would configure properly only upon Zn2+ application. We found that regulation by Zn2+ was reversible and sensitive to the divalent ion's identity, and came at the cost of reduced antibody stability and fluorescein-binding affinity. Fluorescein bound at an order of magnitude higher affinity in the presence of Zn2+ than in its absence, and the increase in fluorescein affinity was due almost entirely to faster fluorescein on-rate, suggesting that Zn2+ preorganized the antibody for fluorescein binding. Mutation analysis demonstrated the extreme sensitivity of Zn2+ regulation on the atomic details in and around the metal-coordination site. The designed antibody could serve to study how allosteric regulation evolved from non-allosteric binding proteins, and suggests a way to designing molecular sensors for environmental and biomedical targets. [ABSTRACT FROM AUTHOR]

Many promising vaccine candidates from pathogenic viruses, bacteria, and parasites are unstable and cannot be produced cheaply for clinical use. For instance, Plasmodium falciparum reticulocyte-binding protein homolog 5 (PfRH5) is essential for erythrocyte invasion, is highly conserved among field isolates, and elicits antibodies that neutralize in vitro and protect in an animal model, making it a leading malaria vaccine candidate. However, functional RH5 is only expressible in eukaryotic systems and exhibits moderate temperature tolerance, limiting its usefulness in hot and low-income countries where malaria prevails. Current approaches to immunogen stabilization involve iterative application of rational or semirational design, random mutagenesis, and biochemical characterization. Typically, each round of optimization yields minor improvement in stability, and multiple rounds are required. In contrast, we developed a one-step design strategy using phylogenetic analysis and Rosetta atomistic calculations to design PfRH5 variantswith improved packing and surface polarity. To demonstrate the robustness of this approach, we tested three PfRH5 designs, all of which showed improved stability relative to wild type. The best, bearing 18 mutations relative to PfRH5, expressed in a folded form in bacteria at >1 mg of protein per L of culture, and had 10-15 °C higher thermal tolerance than wild type, while also retaining ligand binding and immunogenic properties indistinguishable from wild type, proving its value as an immunogen for a future generation of vaccines against the malaria blood stage. We envision that this efficient computational stability design methodology will also be used to enhance the biophysical properties of other recalcitrant vaccine candidates from emerging pathogens. [ABSTRACT FROM AUTHOR]

ABSTRACT Current methods for antibody structure prediction rely on sequence homology to known structures. Although this strategy often yields accurate predictions, models can be stereo-chemically strained. Here, we present a fully automated algorithm, called AbPredict, that disregards sequence homology, and instead uses a Monte Carlo search for low-energy conformations built from backbone segments and rigid-body orientations that appear in antibody molecular structures. We find cases where AbPredict selects accurate loop templates with sequence identity as low as 10%, whereas the template of highest sequence identity diverges substantially from the query's conformation. Accordingly, in several cases reported in the recent Antibody Modeling Assessment benchmark, AbPredict models were more accurate than those from any participant, and the models' stereo-chemical quality was consistently high. Furthermore, in two blind cases provided to us by crystallographers prior to structure determination, the method achieved <1.5 Ångstrom overall backbone accuracy. Accurate modeling of unstrained antibody structures will enable design and engineering of improved binders for biomedical research directly from sequence. Proteins 2016; 85:30-38. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Summary Methods for antibody structure prediction rely on sequence homology to experimentally determined structures. Resulting models may be accurate but are often stereochemically strained, limiting their usefulness in modeling and design workflows. We present the AbPredict 2 web-server, which instead of using sequence homology, conducts a Monte Carlo-based search for low-energy combinations of backbone conformations to yield accurate and unstrained antibody structures. Availability and implementation We introduce several important improvements over the previous AbPredict implementation: (i) backbones and sidechains are now modeled using ideal bond lengths and angles, substantially reducing stereochemical strain, (ii) sampling of the rigid-body orientation at the light-heavy chain interface is improved, increasing model accuracy and (iii) runtime is reduced 20-fold without compromising accuracy, enabling the implementation of AbPredict 2 as a fully automated web-server (http://abpredict.weizmann.ac.il). Accurate and unstrained antibody model structures may in some cases obviate the need for experimental structures in antibody optimization workflows. [ABSTRACT FROM AUTHOR]

We protein engineers are ambivalent about evolution: on the one hand, evolution inspires us with myriad examples of biomolecular binders, sensors, and catalysts; on the other hand, these examples are seldom well-adapted to the engineering tasks we have in mind. Protein engineers have therefore modified natural proteins by point substitutions and fragment exchanges in an effort to generate new functions. A counterpoint to such design efforts, which is being pursued now with greater success, is to completely eschew the starting materials provided by nature and to design new protein functions from scratch by using de novo molecular modeling and design. While important progress has been made in both directions, some areas of protein design are still beyond reach. To this end, we advocate a synthesis of these two strategies: by using design calculations to both recombine and optimize fragments from natural proteins, we can build stable and as of yet un-sampled structures, thereby granting access to an expanded repertoire of conformations and desired functions. We propose that future methods that combine phylogenetic analysis, structure and sequence bioinformatics, and atomistic modeling may well succeed where any one of these approaches has failed on its own. [ABSTRACT FROM AUTHOR]

ABSTRACT Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function-essential to exert control over all polypeptide degrees of freedom-remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high-affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 Å root-mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti-lysozyme antibodies, complementarity-determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity. Proteins 2015; 83:1385-1406. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Computational design provides the opportunity to program protein–protein interactions for desired applications. We used de novo protein interface design to generate a pH-dependent Fc domain binding protein that buries immunoglobulin G (lgG) His-433. Using next-generation sequencing of naïve and selected pools of a library of design variants, we generated a molecular footprint of the designed binding surface, confirming the binding mode and guiding further optimization of the balance between affinity and pH sensitivity. In biolayer interferometry experiments, the optimized design binds lgG with a Kd of ∼4 nM at pH 8.2, and approximately 500-fold more weakly at pH 5.5. The protein is extremely stable, heat-resistant and highly expressed in bacteria, and allows pH-based control of binding for lgG affinity purification and diagnostic devices. [ABSTRACT FROM AUTHOR]

Botulinum neurotoxin type E (BoNT/E), the fastest acting toxin of all BoNTs, cleaves the 25 kDa synaptosomal-associated protein (SNAP-25) in motor neurons, leading to flaccid paralysis. The specific detection and quantification of the BoNT/E-cleaved SNAP-25 neoepitope can facilitate the development of cell-based assays for the characterization of anti-BoNT/E antibody preparations. In order to isolate highly specific monoclonal antibodies suitable for the in vitro immuno-detection of the exposed neoepitope, mice and rabbits were immunized with an eight amino acid peptide composed of the C-terminus of the cleaved SNAP-25. The immunized rabbits developed a specific and robust polyclonal antibody response, whereas the immunized mice mostly demonstrated a weak antibody response that could not discriminate between the two forms of SNAP-25. An immune scFv phage-display library was constructed from the immunized rabbits and a panel of antibodies was isolated. The sequence alignment of the isolated clones revealed high similarity between both heavy and light chains with exceptionally short HCDR3 sequences. A chimeric scFv-Fc antibody was further expressed and characterized, exhibiting a selective, ultra-high affinity (pM) towards the SNAP-25 neoepitope. Moreover, this antibody enabled the sensitive detection of cleaved SNAP-25 in BoNT/E treated SiMa cells with no cross reactivity with the intact SNAP-25. Thus, by applying an immunization and selection procedure, we have isolated a novel, specific and high-affinity antibody against the BoNT/E-derived SNAP-25 neoepitope. This novel antibody can be applied in in vitro assays that determine the potency of antitoxin preparations and reduce the use of laboratory animals for these purposes. [ABSTRACT FROM AUTHOR]

ABSTRACT Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies. Proteins 2013; 81:1980-1987. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Recent years have seen the first applications of computational protein design to generate novel catalysts, binding pairs of proteins, protein inhibitors, and large oligomeric assemblies. At their core these methods rely on a similar hybrid energy function, composed of physics-based and database-derived terms, while different sequence and conformational sampling approaches are used for each design category. Although these are first steps for the computational design of novel function, crystal structures and biochemical characterization already point out where success and failure are likely in the application of protein design. Contrasting failed and successful design attempts has been used to diagnose deficiencies in the approaches and in the underlying hybrid energy function. In this manner, design provides an inherent mechanism by which crucial information is obtained on pressing areas where focused efforts to improve methods are needed. Of the successful designs, many feature pre-organized sites that are poised to perform their intended function, and improvements often result from disfavoring alternative functionally suboptimal states. These rapid developments and fundamental insights obtained thus far promise to make computational design of novel molecular function general, robust, and routine. [Copyright &y& Elsevier]

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. Proteins 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

We show that comprehensive sequence-function maps obtained by deep sequencing can be used to reprogram interaction specificity and to leapfrog over bottlenecks in affinity maturation by combining many individually small contributions not detectable in conventional approaches. We use this approach to optimize two computationally designed inhibitors against H1N1 influenza hemagglutinin and, in both cases, obtain variants with subnanomolar binding affinity. The most potent of these, a 51-residue protein, is broadly cross-reactive against all influenza group 1 hemagglutinins, including human H2, and neutralizes H1N1 viruses with a potency that rivals that of several human monoclonal antibodies, demonstrating that computational design followed by comprehensive energy landscape mapping can generate proteins with potential therapeutic utility. [ABSTRACT FROM AUTHOR]

Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins. [ABSTRACT FROM AUTHOR]

Protein-design methodology can now generate models of protein structures and interfaces with computed energies in the range of those of naturally occurring structures. Comparison of the properties of native structures and complexes to isoenergetic design models can provide insight into the properties of the former that reflect selection pressure for factors beyond the energy of the native state. We report here that sidechains in native structures and interfaces are significantly more constrained than designed interfaces and structures with equal computed binding energy or stability, which may reflect selection against potentially deleterious non-native interactions. [ABSTRACT FROM AUTHOR]

Modeling the conformational changes that occur on binding of macromolecules is an unsolved challenge. In previous rounds of the Critical Assessment of PRediction of Interactions (CAPRI), it was demonstrated that the Rosetta approach to macromolecular modeling could capture side chain conformational changes on binding with high accuracy. In rounds 13-19 we tested the ability of various backbone remodeling strategies to capture the main-chain conformational changes observed during binding events. These approaches span a wide range of backbone motions, from limited refinement of loops to relieve clashes in homologous docking, through extensive remodeling of loop segments, to large-scale remodeling of RNA. Although the results are encouraging, major improvements in sampling and energy evaluation are clearly required for consistent high accuracy modeling. Analysis of our failures in the CAPRI challenges suggest that conformational sampling at the termini of exposed beta strands is a particularly pressing area for improvement. Proteins 2010. © Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

We present a large-scale approach to investigate the functional consequences of sequence variation in a protein. The approach entails the display of hundreds of thousands of protein variants, moderate selection for activity and high-throughput DNA sequencing to quantify the performance of each variant. Using this strategy, we tracked the performance of >600,000 variants of a human WW domain after three and six rounds of selection by phage display for binding to its peptide ligand. Binding properties of these variants defined a high-resolution map of mutational preference across the WW domain; each position had unique features that could not be captured by a few representative mutations. Our approach could be applied to many in vitro or in vivo protein assays, providing a general means for understanding how protein function relates to sequence. [ABSTRACT FROM AUTHOR]

High-affinity, high-selectivity protein-protein interactions that are critical for cell survival present an evolutionary paradox: How does selectivity evolve when acquired mutations risk a lethal loss of high-affinity binding? A detailed understanding of selectivity in such complexes requires structural information on weak, noncognate complexes which can be difficult to obtain due to their transient and dynamic nature. Using NMR-based docking as a guide, we deployed a disulfide-trapping strategy on a noncognate complex between the colicin E9 endonuclease (E9 DNase) and immunity protein 2 (Im2), which is seven orders of magnitude weaker binding than the cognate femtomolar E9 DNase-lm9 interaction. The 1.77 Å crystal structure of the E9 DNase-1m2 complex reveals an entirely noncovalent interface where the intersubunit disulfide merely supports the crystal lattice. In combination with computational alanine scanning of interfacial residues, the structure reveals that the driving force for binding is so strong that a severely unfavorable specificity contact is tolerated at the interface and as a result the complex becomes weakened through "frustration." As well as rationalizing past mutational and thermodynamic data, comparing our noncognate structure with previous cognate complexes highlights the importance of loop regions in developing selectivity and accentuates the multiple roles of buried water molecules that stabilize, ameliorate, or aggravate interfacial contacts. The study provides direct support for dual-recognition in colicin DNase-lm protein complexes and shows that weakened noncognate complexes are primed for high-affinity binding, which can be achieved by economical mutation of a limited number of residues at the interface. [ABSTRACT FROM AUTHOR]

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

Overexpression of the receptor tyrosine kinase (RTK) erbB2 (also designated neu or HER2) was implicated in causing a variety of human cancers, including mammary and ovarian carcinomas. Ligand-induced receptor dimerization is critical for stimulation of the intrinsic protein tyrosine kinase (PTK) of RTKs. it was therefore proposed that PTK activity is stimulated as a result of the reorientation of the cytoplasmic domains within receptor dimers, leading to transautophosphorylation and stimulation of enzymatic activity. Here, we propose a molecular mechanism for rotation-coupled activation of the erbB2 receptor. Using a computational exploration of conformation space of the transmembrane (TM) segments of an erbB2 homodimer, we found two stable conformations of the TM domain. We suggest that these conformations correspond to the active and inactive states of erbB2, and that the receptor molecules may switch from one conformation to the other without crossing exceedingly unfavorable states. This model provides an explanation for the biochemical and oncogenic properties of erbB2, such as the effects of erbB2 overexpression on kinase activity and cell transformation. Furthermore, the opposing effects of the neu[sup *] activating oncogenic point mutation and the Val-655→Ile singlenucleotide polymorphism shown to be linked to reduced risk of breast cancer are explained in terms of shifts in the equilibrium between the active and inactive states of erbB2 in vivo. [ABSTRACT FROM AUTHOR]

Characterization of overexpressed proteins is essential for assessing their quality, and providing input for iterative redesign and optimization. This process is typically carried out following purification procedures that require pronounced cost of time and labor. Therefore, quality assessment of recombinant proteins with no prior purification offers a major advantage. Here, we report a native mass spectrometry method that enables characterization of overproduced proteins directly from culture media. Properties such as solubility, molecular weight, folding, assembly state, overall structure, post-translational modifications and binding to relevant biomolecules are immediately revealed. We show the applicability of the method for in-depth characterization of secreted recombinant proteins from eukaryotic systems such as yeast, insect, and human cells. This method, which can be readily extended to high-throughput analysis, considerably shortens the time gap between protein production and characterization, and is particularly suitable for characterizing engineered and mutated proteins, and optimizing yield and quality of overexpressed proteins. Ben-Nissan et al. present a rapid mass-spectrometry method for characterizing recombinant proteins directly from culture media. They test their method on secreted recombinant proteins from yeast, insect, and human cells, revealing solubility, molecular weight, structure, ligand binding and post-translational modifications. [ABSTRACT FROM AUTHOR]