From De Novo to Xeno: Advancing Macromolecule Design beyond Proteins.

Protein synthesis methods have been adapted to incorporate an ever-growing level of non-natural components. Meanwhile, design of de novo protein structure and function has rapidly emerged as a viable capability. Yet, these two exciting trends have yet to intersect in a meaningful way. The ability to perform de novo design with non-proteinogenic components requires that synthesis and computation align on common targets and applications. This perspective examines the state of the art in these areas and identifies specific, consequential applications to advance the field toward generalized macromolecule design.

Locuaz: an in silico platform for protein binders optimization.

MOTIVATION: Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations, evaluating affinity, and pruning unproductive mutation paths. RESULTS: In response to these challenges, we introduce a new computational platform for optimizing protein binders towards their targets. The platform is organized as a series of modules, performing mutation selection and application, molecular dynamics simulations to sample conformations around interaction poses, and mutation prioritization using suitable scoring functions. Notably, the platform supports parallel exploration of different mutation streams, enabling in silico high-throughput screening on High Performance Computing (HPC) systems. Furthermore, the platform is highly customizable, allowing users to implement their own protocols. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/pgbarletta/locuaz and documentation is at https://locuaz.readthedocs.io/. The data underlying this article are available at https://github.com/pgbarletta/suppl_info_locuaz.

Enzyme Engineering by Force: DNA Springs for the Modulation of Biocatalytic Trajectories.

The engineering of enzymatic activity generally involves alteration of the protein primary sequences, which introduce structural changes that give rise to functional improvements. Mechanical forces have been used to interrogate protein biophysics, leading to deep mechanistic insights in single-molecule studies. Here, we use simple DNA springs to apply small pulling forces to perturb the active site of a thermostable alcohol dehydrogenase. Methods were developed to enable the study of different spring lengths and spring orientations under bulk catalysis conditions. Tension applied across the active site expanded the binding pocket volume and shifted the preference of the enzyme for longer chain-length substrates, which could be tuned by altering the spring length and the resultant applied force. The substrate specificity changes did not occur when the DNA spring was either severed or rotated by ∼90°. These findings demonstrate an alternative approach in protein engineering, where active site architectures can be dynamically and reversibly remodeled using applied mechanical forces.

Computational studies on the catalytic potential of the double active site for enzyme engineering.

Proteins possessing double active sites have the potential to revolutionise enzyme design strategies. This study extensively explored an enzyme that contains both a natural active site (NAS) and an engineered active site (EAS), focusing on understanding its structural and functional properties. Metadynamics simulations were employed to investigate how substrates interacted with their respective active sites. The results revealed that both the NAS and EAS exhibited similar minimum energy states, indicating comparable binding affinities. However, it became apparent that the EAS had a weaker binding site for the substrate due to its smaller pocket and constrained conformation. Interestingly, the EAS also displayed dynamic behaviour, with the substrate observed to move outside the pocket, suggesting the possibility of substrate translocation. To gain further insights, steered molecular dynamics (SMD) simulations were conducted to study the conformational changes of the substrate and its interactions with catalytic residues. Notably, the substrate adopted distinct conformations, including near-attack conformations, in both the EAS and NAS. Nevertheless, the NAS demonstrated superior binding minima for the substrate compared to the EAS, reinforcing the observation that the engineered active site was less favourable for substrate binding due to its limitations. The QM/MM (Quantum mechanics and molecular mechanics) analyses highlight the energy disparity between NAS and EAS. Specifically, EAS exhibited elevated energy levels due to its engineered active site being located on the surface. This positioning exposes the substrate to solvents and water molecules, adding to the energy challenge. Consequently, the engineered enzyme did not provide a significant advantage in substrate binding over the single active site protein. Further, the investigation of internal channels and tunnels within the protein shed light on the pathways facilitating transport between the two active sites. By unravelling the complex dynamics and functional characteristics of this double-active site protein, this study offers valuable insights into novel strategies of enzyme engineering. These findings establish a solid foundation for future research endeavours aimed at harnessing the potential of double-active site proteins in diverse biotechnological applications.

Redesigning amino/carboxyl ends of DARPin G3 for high thermostability and production in tobacco transplastomic plants.

KEY MESSAGE: Redesigning the N- and C-capping repeats of the native DARPin G3 significantly improved its stability, and may facilitate its purification from the total soluble proteins of high-temperature dried leaf materials of transplastomic plants. Designed ankyrin repeat proteins (DARPins) constitute a promising class of binding molecules that can overcome the limitations of monoclonal antibodies and enable the development of novel therapeutic approaches. Despite their inherent stability, detailed studies have revealed that the original capping repeats derived from natural ankyrin repeat proteins impair the stability of the initial DARPin design. Consequently, the development of thermodynamically stabilized antibody mimetics may facilitate the development of innovative drugs in the future. In this study, we replaced the original N- and C-capping repeats with improved caps to enhance the thermostability of native DARPin G3. Computational analyses suggested that the redesigned thermostable DARPin G3 structure possessed optimal quality and stability. Molecular dynamics simulations verified the stability of the redesigned thermostable DARPin G3 at high temperatures. The redesigned thermostable DARPin G3 was expressed at high levels in tobacco transplastomic plants and subsequently purified from high-temperature dried leaf materials. Thermal denaturation results revealed that the redesigned thermostable DARPin G3 had a higher Tm value than the native DARPin G3, with a Tm of 35.51 °C greater than that of native DARPin G3. The results of the in vitro bioassays confirmed that the purified thermostable DARPin G3 from high-temperature dried leaf materials maintained its binding activity without any loss of affinity and specifically bound to the HER2 receptor on the cell surface. These findings demonstrate the successful improvement in the thermostability of DARPin G3 without compromising its biological activity.

Probing PAC1 receptor activation across species with an engineered sensor.

Class-B1 G-protein-coupled receptors (GPCRs) are an important family of clinically relevant drug targets that remain difficult to investigate via high-throughput screening and in animal models. Here, we engineered PAClight1P78A, a novel genetically encoded sensor based on a class-B1 GPCR (the human PAC1 receptor, hmPAC1R) endowed with high dynamic range (ΔF/F0 = 1100%), excellent ligand selectivity, and rapid activation kinetics (τON = 1.15 s). To showcase the utility of this tool for in vitro applications, we thoroughly characterized and compared its expression, brightness and performance between PAClight1P78A-transfected and stably expressing cells. Demonstrating its use in animal models, we show robust expression and fluorescence responses upon exogenous ligand application ex vivo and in vivo in mice, as well as in living zebrafish larvae. Thus, the new GPCR-based sensor can be used for a wide range of applications across the life sciences empowering both basic research and drug development efforts.

Improving the catalytic activity and thermostability of Aspergillus niger xylanase through computational design.

Xylanase plays the most important role in catalyzing xylan to xylose moieties. GH11 xylanases have been widely used in many fields, but most GH11 xylanases are mesophilic enzymes. To improve the catalytic activity and thermostability of Aspergillus niger xylanase (Xyn-WT), we predicted potential key mutation sites of Xyn-WT through multiple computer-aided enzyme engineering strategies. We introduce a simple and economical Ni affinity chromatography purification method to obtain high-purity xylanase and its mutants. Ten mutants (Xyn-A, Xyn-B, Xyn-C, E45T, Q93R, E45T/Q93R, A161P, Xyn-D, Xyn-E, Xyn-F) were identified. Among the ten mutants, four (Xyn-A, Xyn-C, A161P, Xyn-F) presented improved thermal stability and activity, with Xyn-F(A161P/E45T/Q93R) being the most thermally stable and active. Compared with Xyn-WT, after heat treatment at 55 °C and 60 °C for 10 min, the remaining enzyme activity of Xyn-F was 12 and 6 times greater than that of Xyn-WT, respectively, and Xyn-F was approximately 1.5 times greater than Xyn-WT when not heat treated. The pH adaptation of Xyn-F was also significantly enhanced. In summary, an improved catalytic activity and thermostability of the design variant Xyn-F has been reported.

Application of artificial backbone connectivity in the development of metalloenzyme mimics.

Metal-dependent enzymes are abundant and vital catalytic agents in nature. The functional versatility of metalloenzymes has made them common targets for improvement by protein engineering as well as mimicry by de novo designed sequences. In both strategies, the incorporation of non-canonical cofactors and/or non-canonical side chains has proved a useful tool. Less explored-but similarly powerful-is the utilization of non-canonical covalent modifications to the polypeptide backbone itself. Such efforts can entail either introduction of limited artificial monomers in natural chains to produce heterogeneous backbones or construction of completely abiotic oligomers that adopt defined folds. Herein, we review recent research applying artificial protein-like backbones in the construction of metalloenzyme mimics, highlighting progress as well as open questions in this emerging field.

Tunable crystalline assemblies using surface-engineered protein cages.

Assembly of nanoparticles into superlattices yields nanomaterials with novel properties. We have recently shown that engineered protein cages are excellent building blocks for the assembly of inorganic nanoparticles into highly structured hybrid materials, with unprecedented precision. In this study, we show that the protein matrix, composed of surface-charged protein cages, can be readily tuned to achieve a number of different crystalline assemblies. Simply by altering the assembly conditions, different types of crystalline structures were produced, without the need to further modify the cages. Future work can utilize these new protein scaffolds to create nanoparticle superlattices with various assembly geometries and thus tune the functionality of these hybrid materials.

Strategies and procedures to generate chimeric DNA polymerases for improved applications.

Chimeric DNA polymerase with notable performance has been generated for wide applications including DNA amplification and molecular diagnostics. This rational design method aims to improve specific enzymatic characteristics or introduce novel functions by fusing amino acid sequences from different proteins with a single DNA polymerase to create a chimeric DNA polymerase. Several strategies prove to be efficient, including swapping homologous domains between polymerases to combine benefits from different species, incorporating additional domains for exonuclease activity or enhanced binding ability to DNA, and integrating functional protein along with specific protein structural pattern to improve thermal stability and tolerance to inhibitors, as many cases in the past decade shown. The conventional protocol to develop a chimeric DNA polymerase with desired traits involves a Design-Build-Test-Learn (DBTL) cycle. This procedure initiates with the selection of a parent polymerase, followed by the identification of relevant domains and devising a strategy for fusion. After recombinant expression and purification of chimeric polymerase, its performance is evaluated. The outcomes of these evaluations are analyzed for further enhancing and optimizing the functionality of the polymerase. This review, centered on microorganisms, briefly outlines typical instances of chimeric DNA polymerases categorized, and presents a general methodology for their creation. KEY POINTS: • Chimeric DNA polymerase is generated by rational design method. • Strategies include domain exchange and addition of proteins, domains, and motifs. • Chimeric DNA polymerase exhibits improved enzymatic properties or novel functions.

Engineering of a mammalian VMAT2 for cryo-EM analysis results in non-canonical protein folding.

Vesicular monoamine transporter 2 (VMAT2) belongs to the major facilitator superfamily (MFS), and mediates cytoplasmic monoamine packaging into presynaptic vesicles. Here, we present two cryo-EM structures of VMAT2, with a frog VMAT2 adopting a canonical MFS fold and an engineered sheep VMAT2 adopting a non-canonical fold. Both VMAT2 proteins mediate uptake of a selective fluorescent VMAT2 substrate into cells. Molecular docking, substrate binding and transport analysis reveal potential substrate binding mechanism in VMAT2. Meanwhile, caution is advised when interpreting engineered membrane protein structures.

Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors.

Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.

Machine learning-assisted substrate binding pocket engineering based on structural information.

Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-ligand binding sites. The network was integrated by DenseNet, UNet, and self-attention for extracting features and recovering sample size. We attempted to enlarge the dataset by data augmentation, and the model achieved success rates of 48.4%, 35.5%, and 43.6% at a precision of ≥50% and 52%, 47.6%, and 58.1%. The distance of predicted and real center is ≤4 Å, which is based on SC6K, COACH420, and BU48 validation datasets. The substrate binding sites of Klebsiella variicola acid phosphatase (KvAP) and Bacillus anthracis proline 4-hydroxylase (BaP4H) were predicted using DUnet, showing high competitive performance of 53.8% and 56% of the predicted binding sites that critically affected the catalysis of KvAP and BaP4H. Virtual saturation mutagenesis was applied based on the predicted binding sites of KvAP, and the top-ranked 10 single mutations contributed to stronger enzyme-substrate binding varied while the predicted sites were different. The advantage of DUnet for predicting key residues responsible for enzyme activity further promoted the success rate of virtual mutagenesis. This study highlighted the significance of correctly predicting key binding sites for enzyme engineering.

Addressing epistasis in the design of protein function.

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.

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifying optimal mutation sites for directed evolution remains challenging due to the vastness of the protein sequence landscape and the epistatic mutational effects across residues. To address this challenge, we introduce MLSmut, a deep learning-based approach that leverages multi-level structural features of proteins. MLSmut extracts salient information from protein co-evolution, sequence semantics, and geometric features to predict the mutational effect. Extensive benchmark evaluations on 10 single-site and two multi-site deep mutation scanning datasets demonstrate that MLSmut surpasses existing methods in predicting mutational outcomes. To overcome the limited training data availability, we employ a two-stage training strategy: initial coarse-tuning on a large corpus of unlabeled protein data followed by fine-tuning on a curated dataset of 40-100 experimental measurements. This approach enables our model to achieve satisfactory performance on downstream protein prediction tasks. Importantly, our model holds the potential to predict the mutational effects of any protein sequence. Collectively, these findings suggest that our approach can substantially reduce the reliance on laborious wet lab experiments and deepen our understanding of the intricate relationships between mutations and protein function.

Recent advances in the design and optimization of artificial metalloenzymes.

Embedding a catalytically competent transition metal into a protein scaffold affords an artificial metalloenzyme (ArM). Such hybrid catalysts display features that are reminiscent of both homogeneous and enzymatic catalysts. Pioneered by Whitesides and Kaiser in the late 1970s, this field of ArMs has expanded over the past two decades, marked by ever-increasing diversity in reaction types, cofactors, and protein scaffolds. Recent noteworthy developments include i) the use of earth-abundant metal cofactors, ii) concurrent cascade reactions, iii) synergistic catalysis, and iv) in vivo catalysis. Thanks to significant progress in computational protein design, ArMs based on de novo-designed proteins and tailored chimeric proteins promise a bright future for this exciting field.

Assessing and harnessing updated polyketide synthase modules through combinatorial engineering.

The modular nature of polyketide assembly lines and the significance of their products make them prime targets for combinatorial engineering. The recently updated module boundary has been successful for engineering short synthases, yet larger synthases constructed using the updated boundary have not been investigated. Here we describe our design and implementation of a BioBricks-like platform to rapidly construct 5 triketide, 25 tetraketide, and 125 pentaketide synthases to test every module combination of the pikromycin synthase. Anticipated products are detected from 60% of the triketide synthases, 32% of the tetraketide synthases, and 6.4% of the pentaketide synthases. We determine ketosynthase gatekeeping and module-skipping are the principal impediments to obtaining functional synthases. The platform is also employed to construct active hybrid synthases by incorporating modules from the erythromycin, spinosyn, and rapamycin assembly lines. The relaxed gatekeeping of a ketosynthase in the rapamycin synthase is especially encouraging in the quest to produce designer polyketides.

Engineering a tumor-selective prodrug T-cell engager bispecific antibody for safer immunotherapy.

T-cell engaging (TCE) bispecific antibodies are potent drugs that trigger the immune system to eliminate cancer cells, but administration can be accompanied by toxic side effects that limit dosing. TCEs function by binding to cell surface receptors on T cells, frequently CD3, with one arm of the bispecific antibody while the other arm binds to cell surface antigens on cancer cells. On-target, off-tumor toxicity can arise when the target antigen is also present on healthy cells. The toxicity of TCEs may be ameliorated through the use of pro-drug forms of the TCE, which are not fully functional until recruited to the tumor microenvironment. This can be accomplished by masking the anti-CD3 arm of the TCE with an autoinhibitory motif that is released by tumor-enriched proteases. Here, we solve the crystal structure of the antigen-binding fragment of a novel anti-CD3 antibody, E10, in complex with its epitope from CD3 and use this information to engineer a masked form of the antibody that can activate by the tumor-enriched protease matrix metalloproteinase 2 (MMP-2). We demonstrate with binding experiments and in vitro T-cell activation and killing assays that our designed prodrug TCE is capable of tumor-selective T-cell activity that is dependent upon MMP-2. Furthermore, we demonstrate that a similar masking strategy can be used to create a pro-drug form of the frequently used anti-CD3 antibody SP34. This study showcases an approach to developing immune-modulating therapeutics that prioritizes safety and has the potential to advance cancer immunotherapy treatment strategies.

Enhancing extracellular vesicle cargo loading and functional delivery by engineering protein-lipid interactions.

Naturally generated lipid nanoparticles termed extracellular vesicles (EVs) hold significant promise as engineerable therapeutic delivery vehicles. However, active loading of protein cargo into EVs in a manner that is useful for delivery remains a challenge. Here, we demonstrate that by rationally designing proteins to traffic to the plasma membrane and associate with lipid rafts, we can enhance loading of protein cargo into EVs for a set of structurally diverse transmembrane and peripheral membrane proteins. We then demonstrate the capacity of select lipid tags to mediate increased EV loading and functional delivery of an engineered transcription factor to modulate gene expression in target cells. We envision that this technology could be leveraged to develop new EV-based therapeutics that deliver a wide array of macromolecular cargo.