For years, microbes have been widely applied as chassis in the construction of synthetic metabolic pathways. However, the lack of in vivo enzyme clustering of heterologous metabolic pathways in these organisms often results in low local concentrations of enzymes and substrates, leading to a low productive efficacy. In recent years, multiple methods have been applied to the construction of small metabolic clusters by spatial organization of heterologous metabolic enzymes. These methods mainly focused on using engineered molecules to bring the enzymes into close proximity via different interaction mechanisms among proteins and nucleotides and have been applied in various heterologous pathways with different degrees of success while facing numerous challenges. In this paper, we mainly reviewed some of those notable advances in designing and creating approaches to achieve spatial organization using different intermolecular interactions. Current challenges and future aspects in the further application of such approaches are also discussed in this paper.

Background: Pinene is a monoterpene, that is used in the manufacture of fragrances, insecticide, fine chemicals, and renewable fuels. Production of pinene by metabolic-engineered microorganisms is a sustainable method. Purple non-sulfur photosynthetic bacteria belong to photosynthetic chassis that are widely used to synthesize natural chemicals. To date, researches on the synthesis of pinene by purple non-sulfur photosynthetic bacteria has not been reported, leaving the potential of purple non-sulfur photosynthetic bacteria synthesizing pinene unexplored. Results: Rhodobacter sphaeroides strain was applied as a model and engineered to express the fusion protein of heterologous geranyl diphosphate synthase (GPPS) and pinene synthase (PS), hence achieving pinene production. The reaction condition of pinene production was optimized and 97.51 μg/L of pinene was yielded. Then, genes of 1-deoxy-d-xylulose 5-phosphate synthase, 1-deoxy-d-xylulose 5-phosphate reductoisomerase and isopentenyl diphosphate isomerase were overexpressed, and the ribosome binding site of GPPS-PS mRNA was optimized, improving pinene titer to 539.84 μg/L. Conclusions: In this paper, through heterologous expression of GPPS-PS, pinene was successfully produced in R. sphaeroides, and pinene production was greatly improved by optimizing the expression of key enzymes. This is the first report on pinene produce by purple non-sulfur photosynthetic bacteria, which expands the availability of photosynthetic chassis for pinene production.

Protein folding is an important and yet challenging topic in current molecular biology. In this work, the folding dynamics and mechanisms of the Trp-cage mini-protein were studied with molecular dynamics simulations, in the absence and presence of water solvents. The important intermediates during the Trp-cage folding were determined by gradually decreasing the simulation temperature. The folding transition temperature was identified to be approximately 400 K, and the folding pathway was decomposed into six steps: U ↔ I ₁ ↔ I ₂ ↔ I ₃ ↔ I ₄ ↔ F ₁ ↔ F ₂, where U, I and F represent the unfolded, intermediate and folded states, respectively. The finding that the two helical subunits are successively formed is consistent with the experimental observations, and the Asp9/Arg16 salt bridge forms at the final stage and does not play a significant role during folding kinetics. The presence of water solvents induces hydrophobic collapse as the whole cage comparatively closes. Within aqueous solutions, the Trp-cage folding begins to contract into the meta-stable state, and by traversing the transition state it arrives at the native-like structure, which resembles the experimental structure closely.

Accurate prediction of protein function is fundamental to progress in biotechnology and biomedicine, yet progress remains severely hampered by the widening chasm between exponentially growing genomic data and the limited capacity for functional annotation. High-throughput sequencing and metagenomics have driven an explosion in sequence data that far outstrips experimental characterization. UniProt now contains over 203 million protein entries, of which only ~2% have been experimentally validated. This widening “sequence–function gap” exceeds the reach of traditional homology-based tools such as BLAST (v2.17.0) and HMMER (v3.2), which are inherently constrained by sequence identity thresholds. The emergence of Protein Language Models (PLMs), including ESM and ProtTrans, has introduced a transformative paradigm, thereby shifting functional inference from similarity-based retrieval to geometric reasoning within learned semantic spaces. Nevertheless, current approaches remain largely confined to unimodal or narrowly bimodal frameworks, failing to capture the inherently multidimensional determinants of enzymatic function, including active-site geometry, chemical reaction logic, and literature-embedded semantic context. This review systematically adopts a multimodal global-fusion perspective, elucidating how three-dimensional geometric features, chemical reaction semantics, and textual knowledge graphs are synergistically integrated around PLMs as a core backbone. We delineate complementary mechanisms and integration strategies that together enable fine-grained protein function annotation beyond the performance ceiling of single-sequence methods. Furthermore, we survey the translational potential of such frameworks from computational prediction to real biological applications, and critically examine persistent bottlenecks including activity cliffs, transition-state inference, and conformational dynamics. We identify the integration of physics-informed machine learning with dynamics-aware architectures as a pivotal direction toward a causal, mechanism-level understanding of protein function.

The pivotal roles of miRNAs in carcinogenesis, metastasis, and prognosis have been demonstrated recently in various cancers. This study intended to investigate the specific roles of hsa-miR-654-5p in lung cancer, which is, in general, rarely discussed. A series of closed-loop bioinformatic functional analyses were integrated with in vitro experimental validation to explore the overall biological functions and pan-cancer regulation pattern of miR-654-5p. We found that miR-654-5p abundance was significantly elevated in LUAD tissues and correlated with patients’ survival. A total of 275 potential targets of miR-654-5p were then identified and the miR-654-5p-RNF8 regulation axis was validated in vitro as a proof of concept. Furthermore, we revealed the tumor-suppressing roles of miR-654-5p and demonstrated that miR-654-5p inhibited the lung cancer cell epithelial-mesenchymal transition (EMT) process, cell proliferation, and migration using target-based, abundance-based, and ssGSEA-based bioinformatic methods and in vitro validation. Following the construction of a protein–protein interaction network, 11 highly interconnected hub genes were identified and a five-genes risk scoring model was developed to assess their potential prognostic ability. Our study does not only provide a basic miRNA-mRNA-phenotypes reference map for understanding the function of miR-654-5p in different cancers but also reveals the tumor-suppressing roles and prognostic values of miR-654-5p.