A single-domain protein catenane of dihydrofolate reductase

Computational discovery and systematic analysis of protein entangling motifs in nature: from algorithm to database

Nontrivial protein topology has the potential to revolutionize protein engineering by enabling the manipulation of proteins' stability and dynamics. However, the rarity of topological proteins in nature poses a challenge for their design, synthesis and application, primarily due to the limited number of available entangling motifs as synthetic templates. Discovering these motifs is particularly difficult, as entanglement is a subtle structural feature that is not readily discernible from protein sequences. In this study, we developed a streamlined workflow enabling efficient and accurate identification of structurally reliable and applicable entangling motifs from protein sequences. Through this workflow, we automatically curated a database of 1115 entangling protein motifs from over 100 thousand sequences in the UniProt Knowledgebase. In our database, 73.3% of C2 entangling motifs and 80.1% of C3 entangling motifs exhibited low structural similarity to known protein structures. The entangled structures in the database were categorized into different groups and their functional and biological significance were analyzed. The results were summarized in an online database accessible through a user-friendly web platform, providing researchers with an expanded toolbox of entangling motifs. This resource is poised to significantly advance the field of protein topology engineering and inspire new research directions in protein design and application.

Deep Learning-Assisted Discovery of Protein Entangling Motifs

Natural topological proteins exhibit unique properties including enhanced stability, controlled quaternary structures, and dynamic switching properties, highlighting topology as a unique dimension in protein engineering. Although artificial design and synthesis of topological proteins have achieved certain success, their diversity and complexity remain rather limited due to the scarcity of available entangling motifs essential for the construction of nontrivial protein topologies. In this work, we developed a deep-learning model to predict the entanglement features of a homodimer based solely on its amino acid sequence via the Gauss linking number matrices. The model achieved a search speed that was dozens of times faster than AlphaFold-Multimer, while maintaining comparable mean squared error. It was used to screen for entangling motifs from the genome of a hyperthermophilic archaeon. We demonstrated the effectiveness of our model by successful wet-lab synthesis of protein catenanes using two candidate entangling motifs. These findings show the great potential of our model for advancing the design and synthesis of novel topological proteins.

Folds from fold: Exploring topological isoforms of a single-domain protein

Expanding the protein fold space beyond linear chains is of fundamental significance, yet remains largely unexplored. Herein, we report the creation of seven topological isoforms (i.e., linear, cyclic, knot, lasso, pseudorotaxane, and catenane) from a single protein fold precursor by rewiring the connectivity of secondary structure elements of the SpyTag-SpyCatcher complex and mutating the reactive residue on SpyTag to abolish the isopeptide bonding. These topological isoforms can be directly expressed in cells. Their topologies were confirmed by combined techniques of proteolytic digestion, fluorescence correlation spectroscopy (FCS), size-exclusion chromatography (SEC), and topological transformation. To study the effects of topology on their structures and properties, their biophysical properties were characterized by differential scanning calorimetry (DSC), heteronuclear single quantum coherence nuclear magnetic resonance spectroscopy (HSQC-NMR), and circular dichroism (CD) spectroscopy. Molecular dynamics (MD) simulations were further performed to reveal the atomic details of structural changes upon unfolding. Both experimental and simulation results suggest that they share a similar, well-folded hydrophobic core but exhibit distinct folding/unfolding dynamic behaviors. These results shed light onto the folding landscape of topological isoforms derived from the same protein fold. As a model system, this work improves our understanding of protein structure and dynamics beyond linear chains and suggests that protein folds are highly amenable to topological variation.

Probing the Topological Effects on Stability Enhancement and Therapeutic Performance of Protein Bioconjugates: Tadpole, Macrocycle versus Figure-of-Eight

Chemical topology provides a unique dimension for making therapeutic protein bioconjugates with native structure and intact function, yet the effects of topology remain elusive. Herein, the design, synthesis, and characterization of therapeutic protein bioconjugates in three topologies (i.e., tadpole, macrocycle, and figure-of-eight), are reported. The interferon α2b (IFN) and albumin binding domain (ABD) are selected as the model proteins for bioconjugation and proof-of-concept. The biosynthesis of these topological isoforms is accomplished via direct expression in cells using SpyTag-SpyCatcher chemistry and/or split-intein-mediated ligation for topology diversification. The corresponding topologies are proven with combined techniques of LC-MS, SDS-PAGE, and controlled proteolytic digestion. While the properties of these topological isoforms are similar in most cases, the figure-of-eight-shaped bioconjugate, f8-IFN-ABD, exhibits the best thermal stability and anti-aggregation properties along with prolonged half-life and enhanced tumor retention relative to the tadpole-shaped control, tadp-IFN-ABD, and the macrocyclic control, c-IFN-ABD, showcasing considerable topological effects. The work expands the topological diversity of proteins and demonstrates the potential advantages of leveraging chemical topology for functional benefits beyond multi-function integration in protein therapeutics.