Designed Endocytosis-Triggering Proteins mediate Targeted Degradation.

Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by interaction with endogenous ligands. Therapeutic approaches such as LYTAC1,2 and KineTAC3, have taken advantage of this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. While powerful, these approaches can be limited by possible competition with the endogenous ligand(s), the requirement in some cases for chemical modification that limits genetic encodability and can complicate manufacturing, and more generally, there may not be natural ligands which stimulate endocytosis through a given receptor. Here we describe general protein design approaches for designing endocytosis triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for the IGF-2R, ASGPR, Sortillin, and Transferrin receptors, and show that fusing these tags to proteins which bind to soluble or transmembrane protein leads to lysosomal trafficking and target degradation; as these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. The modularity and genetic encodability of EndoTags enables AND gate control for higher specificity targeted degradation, and the localized secretion of degraders from engineered cells. The tunability and modularity of our genetically encodable EndoTags should contribute to deciphering the relationship between receptor engagement and cellular trafficking, and they have considerable therapeutic potential as targeted degradation inducers, signaling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody drug and RNA conjugates.

Computational design of N-linked glycans for high throughput epitope profiling.

Efficient identification of epitopes is crucial for drug discovery and design as it enables the selection of optimal epitopes, expansion of lead antibody diversity, and verification of binding interface. Although high-resolution low throughput methods like x-ray crystallography can determine epitopes or protein-protein interactions accurately, they are time-consuming and can only be applied to a limited number of complexes. To overcome these limitations, we have developed a rapid computational method that incorporates N-linked glycans to mask epitopes or protein interaction surfaces, thereby providing a mapping of these regions. Using human coagulation factor IXa (fIXa) as a model system, we computationally screened 158 positions and expressed 98 variants to test experimentally for epitope mapping. We were able to delineate epitopes rapidly and reliably through the insertion of N-linked glycans that efficiently disrupted binding in a site-selective manner. To validate the efficacy of our method, we conducted ELISA experiments and high-throughput yeast surface display assays. Furthermore, x-ray crystallography was employed to verify the results, thereby recapitulating through the method of N-linked glycans a coarse-grained mapping of the epitope.

A systematic approach for evaluating the role of surface-exposed loops in trypsin-like serine proteases applied to the 170 loop in coagulation factor VIIa.

Proteases play a major role in many vital physiological processes. Trypsin-like serine proteases (TLPs), in particular, are paramount in proteolytic cascade systems such as blood coagulation and complement activation. The structural topology of TLPs is highly conserved, with the trypsin fold comprising two ß-barrels connected by a number of variable surface-exposed loops that provide a surprising capacity for functional diversity and substrate specificity. To expand our understanding of the roles these loops play in substrate and co-factor interactions, we employ a systematic methodology akin to the natural truncations and insertions observed through evolution of TLPs. The approach explores a larger deletion space than classical random or directed mutagenesis. Using FVIIa as a model system, deletions of 1-7 amino acids through the surface exposed 170 loop, a vital allosteric regulator, was introduced. All variants were extensively evaluated by established functional assays and computational loop modelling with Rosetta. The approach revealed detailed structural and functional insights recapitulation and expanding on the main findings in relation to 170 loop functions elucidated over several decades using more cumbersome crystallization and single deletion/mutation methodologies. The larger deletion space was key in capturing the most active variant, which unexpectedly had a six-amino acid truncation. This variant would have remained undiscovered if only 2-3 deletions were considered, supporting the usefulness of the methodology in general protease engineering approaches. Our findings shed further light on the complex role that surface-exposed loops play in TLP function and supports the important role of loop length in the regulation and fine-tunning of enzymatic function throughout evolution.

Removal of lycopene substrate inhibition enables high carotenoid productivity in Yarrowia lipolytica.

Substrate inhibition of enzymes can be a major obstacle to the production of valuable chemicals in engineered microorganisms. Here, we show substrate inhibition of lycopene cyclase as the main limitation in carotenoid biosynthesis in Yarrowia lipolytica. To overcome this bottleneck, we exploit two independent approaches. Structure-guided protein engineering yields a variant, Y27R, characterized by complete loss of substrate inhibition without reduction of enzymatic activity. Alternatively, establishing a geranylgeranyl pyrophosphate synthase-mediated flux flow restrictor also prevents the onset of substrate inhibition by diverting metabolic flux away from the inhibitory metabolite while maintaining sufficient flux towards product formation. Both approaches result in high levels of near-exclusive ß-carotene production. Ultimately, we construct strains capable of producing 39.5 g/L ß-carotene at a productivity of 0.165 g/L/h in bioreactor fermentations (a 1441-fold improvement over the initial strain). Our findings provide effective approaches for removing substrate inhibition in engineering pathways for efficient synthesis of natural products.

Improvement of a synthetic live bacterial therapeutic for phenylketonuria with biosensor-enabled enzyme engineering.

In phenylketonuria (PKU) patients, a genetic defect in the enzyme phenylalanine hydroxylase (PAH) leads to elevated systemic phenylalanine (Phe), which can result in severe neurological impairment. As a treatment for PKU, Escherichia coli Nissle (EcN) strain SYNB1618 was developed under Synlogic's Synthetic Biotic™ platform to degrade Phe from within the gastrointestinal (GI) tract. This clinical-stage engineered strain expresses the Phe-metabolizing enzyme phenylalanine ammonia lyase (PAL), catalyzing the deamination of Phe to the non-toxic product trans-cinnamate (TCA). In the present work, we generate a more potent EcN-based PKU strain through optimization of whole cell PAL activity, using biosensor-based high-throughput screening of mutant PAL libraries. A lead enzyme candidate from this screen is used in the construction of SYNB1934, a chromosomally integrated strain containing the additional Phe-metabolizing and biosafety features found in SYNB1618. Head-to-head, SYNB1934 demonstrates an approximate two-fold increase in in vivo PAL activity compared to SYNB1618.

Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites.

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational "anchor extension" methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC50 values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.

A factor VIIIa-mimetic bispecific antibody, Mim8, ameliorates bleeding upon severe vascular challenge in hemophilia A mice.

Hemophilia A is a bleeding disorder resulting from deficient factor VIII (FVIII), which normally functions as a cofactor to activated factor IX (FIXa) that facilitates activation of factor X (FX). To mimic this property in a bispecific antibody format, a screening was conducted to identify functional pairs of anti-FIXa and anti-FX antibodies, followed by optimization of functional and biophysical properties. The resulting bispecific antibody (Mim8) assembled efficiently with FIXa and FX on membranes, and supported activation with an apparent equilibrium dissociation constant of 16 nM. Binding affinity with FIXa and FX in solution was much lower, with equilibrium dissociation constant values for FIXa and FX of 2.3 and 1.5 µM, respectively. In addition, the activity of Mim8 was dependent on stimulatory activity contributed by the anti-FIXa arm, which enhanced the proteolytic activity of FIXa by 4 orders of magnitude. In hemophilia A plasma and whole blood, Mim8 normalized thrombin generation and clot formation, with potencies 13 and 18 times higher than a sequence-identical analogue of emicizumab. A similar potency difference was observed in a tail vein transection model in hemophilia A mice, whereas reduction of bleeding in a severe tail-clip model was observed only for Mim8. Furthermore, the pharmacokinetic parameters of Mim8 were investigated and a half-life of 14 days shown in cynomolgus monkeys. In conclusion, Mim8 is an activated FVIII mimetic with a potent and efficacious hemostatic effect based on preclinical data.

Enhanced chemoselectivity of a plant cytochrome P450 through protein engineering of surface and catalytic residues.

Cytochrome P450s (P450s) are the most versatile catalysts utilized by plants to produce structurally and functionally diverse metabolites. Given the high degree of gene redundancy and challenge to functionally characterize plant P450s, protein engineering is used as a complementary strategy to study the mechanisms of P450-mediated reactions, or to alter their functions. We previously proposed an approach of engineering plant P450s based on combining high-accuracy homology models generated by Rosetta combined with data-driven design using evolutionary information of these enzymes. With this strategy, we repurposed a multi-functional P450 (CYP87D20) into a monooxygenase after redesigning its active site. Since most plant P450s are membrane-anchored proteins that are adapted to the micro-environments of plant cells, expressing them in heterologous hosts usually results in problems of expression or activity. Here, we applied computational design to tackle these issues by simultaneous optimization of the protein surface and active site. After screening 17 variants, effective substitutions of surface residues were observed to improve both expression and activity of CYP87D20. In addition, the identified substitutions were additive and by combining them a highly efficient C11 hydroxylase of cucurbitadienol was created to participate in the mogrol biosynthesis. This study shows the importance of considering the interplay between surface and active site residues for P450 engineering. Our integrated strategy also opens an avenue to create more tailoring enzymes with desired functions for the metabolic engineering of high-valued compounds like mogrol, the precursor of natural sweetener mogrosides. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-021-00056-z.

Multi-input chemical control of protein dimerization for programming graded cellular responses.

Chemical and optogenetic methods for post-translationally controlling protein function have enabled modulation and engineering of cellular functions. However, most of these methods only confer single-input, single-output control. To increase the diversity of post-translational behaviors that can be programmed, we built a system based on a single protein receiver that can integrate multiple drug inputs, including approved therapeutics. Our system translates drug inputs into diverse outputs using a suite of engineered reader proteins to provide variable dimerization states of the receiver protein. We show that our single receiver protein architecture can be used to program a variety of cellular responses, including graded and proportional dual-output control of transcription and mammalian cell signaling. We apply our tools to titrate the competing activities of the Rac and Rho GTPases to control cell morphology. Our versatile tool set will enable researchers to post-translationally program mammalian cellular processes and to engineer cell therapies.

Improving the Developability of an Antigen Binding Fragment by Aspartate Substitutions.

Aggregation can be a major challenge in the development of antibody-based pharmaceuticals as it can compromise the quality of the product during bioprocessing, formulation, and drug administration. To avoid aggregation, developability assessment is often run in parallel with functional optimization in the early screening phases to flag and deselect problematic molecules. As developability assessment can be demanding with regard to time and resources, there is a high focus on the development of molecule design strategies for engineering molecules with a high developability potential. Previously, Dudgeon et al. [(2012) Proc. Natl. Acad. Sci. U. S. A. 109, 10879-10884] demonstrated how Asp substitutions at specific positions in human variable domains and single-chain variable fragments could decrease the aggregation propensity. Here, we have investigated whether these Asp substitutions would improve the developability potential of a murine antigen binding fragment (Fab). A full combinatorial library consisting of 393 Fab variants with single, double, and triple Asp substitutions was first screened in silico with Rosetta; thereafter, 26 variants with the highest predicted thermodynamic stability were selected for production. All variants were subjected to a set of developability studies. Interestingly, most variants had thermodynamic stability on par with or improved relative to that of the wild type. Twenty-five of the variants exhibited improved nonspecificity. Half of the variants exhibited improved aggregation resistance. Strikingly, while we observed remarkable improvement in the developability potential, the Asp substitutions had no substantial effect on the antigenic binding affinity. Altogether, by combining the insertion of negative charges and the in silico screen based on computational models, we were able to improve the developability of the Fab rapidly.

A structural and data-driven approach to engineering a plant cytochrome P450 enzyme.

Functional manipulation of biosynthetic enzymes such as cytochrome P450s (or P450s) has attracted great interest in metabolic engineering of plant natural products. Cucurbitacins and mogrosides are plant triterpenoids that share the same backbone but display contrasting bioactivities. This structural and functional diversity of the two metabolites can be manipulated by engineering P450s. However, the functional redesign of P450s through directed evolution (DE) or structure-guided protein engineering is time consuming and challenging, often because of a lack of high-throughput screening methods and crystal structures of P450s. In this study, we used an integrated approach combining computational protein design, evolutionary information, and experimental data-driven optimization to alter the substrate specificity of a multifunctional P450 (CYP87D20) from cucumber. After three rounds of iterative design and evaluation of 96 protein variants, CYP87D20, which is involved in the cucurbitacin C biosynthetic pathway, was successfully transformed into a P450 mono-oxygenase that performs a single specific hydroxylation at C11 of cucurbitadienol. This integrated P450-engineering approach can be further applied to create a de novo pathway to produce mogrol, the precursor of the natural sweetener mogroside, or to alter the structural diversity of plant triterpenoids by functionally manipulating other P450s.

Optimizing selectivity of anion hydrophobic multimodal chromatography for purification of a single-chain variable fragment.

Single-chain variable fragments (scFv) are widely used in several fields. However, they can be challenging to purify unless using expensive Protein L-based affinity adsorbents or affinity tags. In this work, a purification process for a scFv using mixed-mode (MM) chromatography was developed by design of experiments (DoE) and proteomics for host cell protein (HCP) quantification. Capture of scFv from human embryonic kidney 293 (HEK293) cell feedstocks was performed by hydrophobic charge induction chromatography (MEP HyperCel™), whereafter polishing was performed by anion hydrophobic MM chromatography (Capto Adhere™). The DoE designs of the polishing step included both binding and flow-through modes, the latter being the standard mode for HCP removal. Chromatography with Capto Adhere™ in binding-mode with elution by linear salt gradient at pH 7.5 resulted in optimal yield, purity and HCP reduction factor of 98.9 > 98.5%, and 14, respectively. Totally, 258 different HCPs were removed, corresponding to 84% of identified HCPs. The optimized conditions enabled binding of the scFv to Capto Adhere™ below its theoretical pI, while the majority of HCPs were in the flow-through. Surface property maps indicated the presence of hydrophobic patches in close proximity to negatively charged patches that could potentially play a role in this unique selectivity.

Unintended specificity of an engineered ligand-binding protein facilitated by unpredicted plasticity of the protein fold.

Attempts to create novel ligand-binding proteins often focus on formation of a binding pocket with shape complementarity against the desired ligand (particularly for compounds that lack distinct polar moieties). Although designed proteins often exhibit binding of the desired ligand, in some cases they display unintended recognition behavior. One such designed protein, that was originally intended to bind tetrahydrocannabinol (THC), was found instead to display binding of 25-hydroxy-cholecalciferol (25-D3) and was subjected to biochemical characterization, further selections for enhanced 25-D3 binding affinity and crystallographic analyses. The deviation in specificity is due in part to unexpected altertion of its conformation, corresponding to a significant change of the orientation of an α-helix and an equally large movement of a loop, both of which flank the designed ligand-binding pocket. Those changes led to engineered protein constructs that exhibit significantly more contacts and complementarity towards the 25-D3 ligand than the initial designed protein had been predicted to form towards its intended THC ligand. Molecular dynamics simulations imply that the initial computationally designed mutations may contribute to the movement of the helix. These analyses collectively indicate that accurate prediction and control of backbone dynamics conformation, through a combination of improved conformational sampling and/or de novo structure design, represents a key area of further development for the design and optimization of engineered ligand-binding proteins.

Sampling and energy evaluation challenges in ligand binding protein design.

The steroid hormone 17α-hydroxylprogesterone (17-OHP) is a biomarker for congenital adrenal hyperplasia and hence there is considerable interest in development of sensors for this compound. We used computational protein design to generate protein models with binding sites for 17-OHP containing an extended, nonpolar, shape-complementary binding pocket for the four-ring core of the compound, and hydrogen bonding residues at the base of the pocket to interact with carbonyl and hydroxyl groups at the more polar end of the ligand. Eight of 16 designed proteins experimentally tested bind 17-OHP with micromolar affinity. A co-crystal structure of one of the designs revealed that 17-OHP is rotated 180° around a pseudo-two-fold axis in the compound and displays multiple binding modes within the pocket, while still interacting with all of the designed residues in the engineered site. Subsequent rounds of mutagenesis and binding selection improved the ligand affinity to nanomolar range, while appearing to constrain the ligand to a single bound conformation that maintains the same "flipped" orientation relative to the original design. We trace the discrepancy in the design calculations to two sources: first, a failure to model subtle backbone changes which alter the distribution of sidechain rotameric states and second, an underestimation of the energetic cost of desolvating the carbonyl and hydroxyl groups of the ligand. The difference between design model and crystal structure thus arises from both sampling limitations and energy function inaccuracies that are exacerbated by the near two-fold symmetry of the molecule.

Computational design of environmental sensors for the potent opioid fentanyl.

We describe the computational design of proteins that bind the potent analgesic fentanyl. Our approach employs a fast docking algorithm to find shape complementary ligand placement in protein scaffolds, followed by design of the surrounding residues to optimize binding affinity. Co-crystal structures of the highest affinity binder reveal a highly preorganized binding site, and an overall architecture and ligand placement in close agreement with the design model. We use the designs to generate plant sensors for fentanyl by coupling ligand binding to design stability. The method should be generally useful for detecting toxic hydrophobic compounds in the environment.

Overcoming an optimization plateau in the directed evolution of highly efficient nerve agent bioscavengers.

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 × 106 M-1 min-1. However, effective in vivo detoxification demands an enzyme with a catalytic efficiency of >107 M-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 × 107 M-1 min-1 and Russian VX (RVX) with a kcat/KM ≥ 107 M-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 × 107 M-1 min-1) and can thus serve as candidates for broad-spectrum nerve-agent prophylaxis and post-exposure therapy using low enzyme doses.

Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules.

Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking have been parametrized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties.

Accurate de novo design of hyperstable constrained peptides.

Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes that have evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small-molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for accurate de novo design of conformationally restricted peptides, and the use of these methods to design 18-47 residue, disulfide-crosslinked peptides, a subset of which are heterochiral and/or N-C backbone-cyclized. Both genetically encodable and non-canonical peptides are exceptionally stable to thermal and chemical denaturation, and 12 experimentally determined X-ray and NMR structures are nearly identical to the computational design models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs.

Catalytic efficiencies of directly evolved phosphotriesterase variants with structurally different organophosphorus compounds in vitro.

The nearly 200,000 fatalities following exposure to organophosphorus (OP) pesticides each year and the omnipresent danger of a terroristic attack with OP nerve agents emphasize the demand for the development of effective OP antidotes. Standard treatments for intoxicated patients with a combination of atropine and an oxime are limited in their efficacy. Thus, research focuses on developing catalytic bioscavengers as an alternative approach using OP-hydrolyzing enzymes such as Brevundimonas diminuta phosphotriesterase (PTE). Recently, a PTE mutant dubbed C23 was engineered, exhibiting reversed stereoselectivity and high catalytic efficiency (k cat/K M) for the hydrolysis of the toxic enantiomers of VX, CVX, and VR. Additionally, C23's ability to prevent systemic toxicity of VX using a low protein dose has been shown in vivo. In this study, the catalytic efficiencies of V-agent hydrolysis by two newly selected PTE variants were determined. Moreover, in order to establish trends in sequence-activity relationships along the pathway of PTE's laboratory evolution, we examined k cat/K M values of several variants with a number of V-type and G-type nerve agents as well as with different OP pesticides. Although none of the new PTE variants exhibited k cat/K M values >107 M-1 min-1 with V-type nerve agents, which is required for effective prophylaxis, they were improved with VR relative to previously evolved variants. The new variants detoxify a broad spectrum of OPs and provide insight into OP hydrolysis and sequence-activity relationships.

Computational redesign of metalloenzymes for catalyzing new reactions.

The ability to design novel activities in existing metalloenzyme active sites is a stringent test of our understanding of enzyme mechanisms, sheds light on enzyme evolution, and would have many practical applications. Here, we describe a computational method in the context of the macromolecular modeling suite Rosetta to repurpose active sites containing metal ions for reactions of choice. The required inputs for the method are a model of the transition state(s) for the reaction and a set of crystallographic structures of proteins containing metal ions. The coordination geometry associated with the metal ion (Zn(2+), for example) is automatically detected and the transition state model is aligned to the open metal coordination site(s) in the protein. Additional interactions to the transition state model are made using RosettaMatch and the surrounding amino acid side chain identities are optimized for transition state stabilization using RosettaDesign. Validation of the design is performed using docking and molecular dynamics simulations, and candidate designs are generated for experimental validation. Computational metalloenzyme repurposing is complementary to directed evolution approaches for enzyme engineering and allows large jumps in sequence space to make concerted sequence and structural changes for introducing novel enzymatic activities and specificities.