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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.

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. Computationally designed enzymes often show lower activity or stability than their natural counterparts. Here, the authors present an evolution-inspired method for automated enzyme design, creating stable enzymes with accurate active site architectures and wild-type-like activities.

Computational protein design has made substantial progress over the past years, generating novel conformations, catalysts, binders, and oligomeric assemblies. Prevalent methods to design new conformations de novo have relied on 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 non-ideal features, including large and irregular loops, and buried polar interaction networks, which to date protein designers have failed to generate in a general reproducible manner. Currently, to design new function, protein designers repurpose scaffolds from naturally occurring proteins to carry out different functions. These designs are reminiscent of the designed folds mentioned above, since they relied on rigid protein scaffolds with high secondary-structure content whereas natural proteins encode functional elements in regions lacking secondary structure. Additionally, modifying these natural scaffold often compromises the protein’s stability. Herein, I describe a combinatorial backbone and sequence design algorithm, which addresses both issues: designing new scaffolds with non-ideal features which can be tailored for a function of choice, while simultaneously optimizing the protein’s stability. The method leverages the large number of sequences and experimentally determined molecular structures of natural proteins to construct novel protein binders and catalysts. To prove the generality of my design approach the algorithm was applied and experimentally tested on two unrelated protein folds and functions: antibody binders against human insulin and bacterial Acyl Carrier Protein (ACP) and TIM-barrel fold catalysts for hydrolysis of lactones and xylan sugar. 2 anti-ACP and 1 anti-insulin designed antibodies bound to their ligands with mid to high nanomolar affinities before directed evolution and demonstrated native like stability despite having over 30 mutations from mammalian antibody germlines. The designed binding modes were validated using site directed mutagenesis and crystallographic analysis of two of the anti-insulin binders revealed atomic accuracy throughout most of the structure. 43 glycoside hydrolase 10 (GH10) xylanases and 34 phosphotriesterase-like lactonases (PLLs) were also generated using this method. Twenty-one GH10 and seven PLL designs were active and four were as active as natural enzymes in these families. The designs exhibited thermostability on par with natural enzymes from thermophiles despite having over 100 mutations from their closest homologue.

The industrial application of enzymes is often hampered by poor stability and low expression yields. While computational tools can predict stabilizing mutations, many are bound by restrictive licenses that hinder their broader adoption. To address this, we developed StabilizeIT, a powerful, open-access webserver for enhancing protein stability and expression. StabilizeIT integrates a pipeline of curated open-source tools such as ProteinMPNN, AlphaFold2 and SaProt with our state-of-the-art model, SolvIT, which accurately predicts heterologous expression titers in E. coli. This unique combination allows for the simultaneous optimization of melting temperature (Tm) and solubility. The pipeline exhibits remarkable speed, generating dozens of high-quality candidates with predicted high titers and increased stability in under an hour, streamlining the path to experimental validation. To demonstrate its efficacy, StabilizeIT was used to engineer multiple enzymes in our novel biosynthetic pathway for Hyaluronic Acid. The resulting variants showed greatly enhanced thermal stability and expression, proving the pipeline’s real-world utility. StabilizeIT is now available to the community, offering an accessible and validated solution to accelerate the development of robust proteins for diverse applications. The webserver is freely available at https://stabilizeit.enzymit.com

The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters. To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes. Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme. Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.