SynBio, Volume 3, Issue 4 (December 2025) – 5 articles

Protein function emerges from dynamic conformational changes, yet structure prediction methods provide only static snapshots. While AlphaFold3 (AF3) predicts protein structures, the potential for extracting dynamic information from its ensemble predictions has remained underexplored. Here, we demonstrate that AF3 structural ensembles contain substantial dynamic information that correlates remarkably well with molecular dynamics simulations (MD). We developed ChronoSort, a novel algorithm that organizes static structure predictions into temporally coherent trajectories by minimizing structural differences between neighboring frames. Through systematic analysis of four diverse protein targets, we show that root-mean-square fluctuations derived from AF3 ensembles can correlate strongly with those from MD (r = 0.53 to 0.84). Principal component analysis reveals that AF3 predictions capture the same collective motion patterns observed in molecular dynamics trajectories, with eigenvector similarities significantly exceeding random distributions. ChronoSort trajectories exhibit structural evolution profiles comparable to MD. These findings suggest that modern AI-based structure prediction tools encode conformational flexibility information that can be systematically extracted without expensive MD. We provide ChronoSort as open-source software to enable broad community adoption. This work offers a novel approach to extracting functional insights from structure prediction tools in minutes, with significant implications for synthetic biology, protein engineering, drug discovery, and structure–function studies. Full article

Synthetic biology, an emergent interdisciplinary field integrating principles from biology, engineering, and computer science, endeavors to rationally design and construct novel biological systems or reprogram extant ones to achieve predefined functionalities. The conventional approach relies on an iterative Design-Build-Test-Learn (DBTL) cycle, a process frequently hampered by the intrinsic complexity, non-linear interactions, and vast design space inherent to biological systems. The advent of Artificial Intelligence (AI), and particularly its subfields of Machine Learning (ML) and Deep Learning (DL), is fundamentally reshaping this paradigm by offering robust computational frameworks to navigate these formidable challenges. This review elucidates the strategic integration of AI/ML/DL across the synthetic biology workflow, detailing the specific algorithms and mechanisms that enable rational design, autonomous experimentation, and pathway optimization. Their advanced applications are specifically underscored across critical facets, including de novo rational design, enhanced predictive modeling, intelligent high-throughput data analysis, and AI-driven laboratory automation. Furthermore, pivotal challenges, such as data sparsity, model interpretability, the “black box” problem, computational resource demands, and ethical considerations, have been addressed, while concurrently forecasting future trajectories for this rapidly advancing and convergent domain. The synergistic convergence of these disciplines is demonstrably accelerating biological discovery, facilitating the creation of innovative and scalable biological solutions, and fostering a more predictable and efficient paradigm for biological engineering. Full article

Researchers strive to exploit kinetic potentials of multistep reactions by positioning enzymes in a regulated fashion. Therein, the proliferating cell nuclear antigen (PCNA) from Sulfolobus solfataricus is a promising biomolecular tool due to its extraordinary architecture. PCNA is a circular DNA sliding clamp, which can bind and move along DNA and thus, be applied for the immobilization and transport of biomolecules on versatile DNA scaffolds. Additionally, its heterotrimeric character facilitates the colocalization of enzyme cascades with defined stoichiometry. This study provides insights into the in vitro binding behavior of PCNA and its potential as protein scaffold for DNA functionalization and controlled biocatalysis: (1) PCNA was capable of binding circular DNA and wireframe DNA nanostructures. (2) DNA binding was predominantly mediated by the PCNA1 subunit. (3) PCNA assembly around DNA was compromised when cysteines were introduced at the PCNA–PCNA interfaces to stabilize the ring via disulfide bonds. (4) A two-enzyme cascade, comprising a pseudo-monomeric cytochrome P450 BM3 monooxygenase and a monomeric alcohol dehydrogenase (ADH), was successfully fused to PCNA, retaining catalytic activity. (5) When immobilized on DNA, the cascade performance was not assessable, due to nearly complete loss of ADH activity in proximity to DNA. Full article

While synthetic biology has traditionally focused on creating biological systems often through genetic engineering, emerging technologies, for example, implantable pacemakers with integrated piezo-electric and tribo-electric materials are beginning to enlarge the classical domain into what we call symbiotic synthetic biology. These devices are permanently attached to a body, although non-living or genetically unaltered, and closely mimic biological behavior by harvesting biomechanical energy and providing functions, such as autonomous heart pacing. They form active interfaces with human tissues and operate as hybrid systems, similar to synthetic organs. In this context, the present paper first presents a short summary of previous in vivo studies on piezo-electric composites in relation to their deployment as battery-less pacemakers. This is then followed by a summary of a recent theoretical work using a damped harmonic resonance model, which is being extended to mimic the functioning of such devices. We then extend the theoretical study further to include new solutions and obtain a sum rule for the power output per cycle in such systems. In closing, we present our quantitative understanding to explore the modulation of the quantum vacuum energy (Casimir effect) by periodic body movements to power pacemakers. Taken together, the present work provides the scientific foundation of the next generation bio-integrated intelligent implementation. Full article

Polyproline residues are well known to induce ribosomal stalling during translation. Our previous work demonstrated that inserting a short translation-enhancing peptide, Ser-Lys-Ile-Lys (SKIK), immediately upstream of such difficult-to-translate sequences can significantly alleviate ribosomal stalling in Escherichia coli. In this study, we provide a quantitative evaluation of its translational effect by kinetically analyzing the influence of the SKIK peptide on polyproline motifs using a reconstituted E. coli in vitro translation system. Translation rates estimated under reasonable assumptions fitted well to a Hill equation within a Michaelis–Menten-like kinetic framework. We further revealed that repetition of the SKIK tag did not provide any positive effect on translation. Moreover, introduction of the SKIK tag increased the production of polyproline-containing proteins, including human interleukin 11, human G protein signaling modulator 3, and DUF58 domain–containing protein from Streptomyces sp. in E. coli cell-free protein synthesis. These findings not only provide new insight into the fundamental regulation of translation by nascent peptides but also demonstrate the potential of the SKIK peptide as a practical tool for synthetic biology, offering a strategy to improve the production of difficult-to-express proteins. Full article