AGeNNT: annotation of enzyme families by means of refined neighborhood networks

Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis

Biosynthesis-a process utilizing biological systems to synthesize chemical compounds-has emerged as a revolutionary solution to 21st-century challenges due to its environmental sustainability, scalability, and high stereoselectivity and regioselectivity. Recent advancements in artificial intelligence (AI) are accelerating biosynthesis by enabling intelligent design, construction, and optimization of enzymatic reactions and biological systems. We first introduce the molecular retrosynthesis route planning in biochemical pathway design, including single-step retrosynthesis algorithms and AI-based chemical retrosynthesis route design tools. We highlight the advantages and challenges of large language models in addressing the sparsity of chemical data. Furthermore, we review enzyme discovery methods based on sequence and structure alignment techniques. Breakthroughs in AI-based structural prediction methods are expected to significantly improve the accuracy of enzyme discovery. We also summarize methods for de novo enzyme generation for nonnatural or orphan reactions, focusing on AI-based enzyme functional annotation and enzyme discovery techniques based on reaction or small molecule similarity. Turning to enzyme engineering, we discuss strategies to improve enzyme thermostability, solubility, and activity, as well as the applications of AI in these fields. The shift from traditional experiment-driven models to data-driven and computationally driven intelligent models is already underway. Finally, we present potential challenges and provide a perspective on future research directions. We envision expanded applications of biocatalysis in drug development, green chemistry, and complex molecule synthesis.

Biosynthesis and function of 7-deazaguanine derivatives in bacteria and phages

SUMMARYDeazaguanine modifications play multifaceted roles in the molecular biology of DNA and tRNA, shaping diverse yet essential biological processes, including the nuanced fine-tuning of translation efficiency and the intricate modulation of codon-anticodon interactions. Beyond their roles in translation, deazaguanine modifications contribute to cellular stress resistance, self-nonself discrimination mechanisms, and host evasion defenses, directly modulating the adaptability of living organisms. Deazaguanine moieties extend beyond nucleic acid modifications, manifesting in the structural diversity of biologically active natural products. Their roles in fundamental cellular processes and their presence in biologically active natural products underscore their versatility and pivotal contributions to the intricate web of molecular interactions within living organisms. Here, we discuss the current understanding of the biosynthesis and multifaceted functions of deazaguanines, shedding light on their diverse and dynamic roles in the molecular landscape of life.

GM-lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning

In recent years, a large number of studies have shown that the subcellular localization of long non-coding RNAs (lncRNAs) can bring crucial information to the recognition of lncRNAs function. Therefore, it is of great significance to establish a computational method to accurately predict the subcellular localization of lncRNA. Previous prediction models are based on low-level sequences information and are troubled by the few samples problem. In this study, we propose a new prediction model, GM-lncLoc, which is based on the initial information extracted from the lncRNA sequence, and also combines the graph structure information to extract high level features of lncRNA. In addition, the training mode of meta-learning is introduced to obtain meta-parameters by training a series of tasks. With the meta-parameters, the final parameters of other similar tasks can be learned quickly, so as to solve the problem of few samples in lncRNA subcellular localization. Compared with the previous methods, GM-lncLoc achieved the best results with an accuracy of 93.4 and 94.2% in the benchmark datasets of 5 and 4 subcellular compartments, respectively. Furthermore, the prediction performance of GM-lncLoc was also better on the independent dataset. It shows the effectiveness and great potential of our proposed method for lncRNA subcellular localization prediction. The datasets and source code are freely available at https://github.com/JunzheCai/GM-lncLoc .

Effects of Polyethylene Microplastics and Phenanthrene on Soil Properties, Enzyme Activities and Bacterial Communities

Microplastics (MPs) or polycyclic aromatic hydrocarbons (PAHs) pollution has received increasing concern due to their ubiquitous distribution and potential risks in soils. However, nothing is known about the influences of PAHs-MPs combined pollution on soil ecosystems. To address the knowledge gap, a 1-year soil microcosm experiment was conducted to systematically investigate the single and combined effect of polyethylene (PE) /phenanthrene (PHE) on soil chemical properties, enzymatic activities and bacterial communities (i.e., diversity, composition and function). Results showed that PE and PHE-PE significantly decreased soil pH. The available phosphorus (AP) and neutral phosphatase activity were not considerably changed by PHE, PE and PHE-PE. Significant enhancement of dehydrogenase activity in a PHE-PE amended system might be due to the degradation of PHE by indigenous bacteria (i.e., Sphingomonas, Sphingobium), and PE could enhance this stimulative effect. PHE and PHE-PE led to a slight increase in soil organic matter (SOM) and fluorescein diacetate hydrolase (FDAse) activity but a decrease in available nitrogen (AN) and urease activity. PE significantly enhanced the functions of nitrogen cycle and metabolism, reducing SOM/AN contents but increasing urease/FDAse activities. There were insignificant impacts on overall community diversity and composition in treated samples, although some bacterial genera were significantly stimulated or attenuated with treatments. In conclusion, the addition of PHE and PE influenced the soil chemical properties, enzymatic activities and bacterial community diversity/composition to some extent. The significantly positive effect of PE on the nitrogen cycle and on metabolic function might lead to the conspicuous alterations in SOM/AN contents and urease/FDAse activities. This study may provide new basic information for understanding the ecological risk of PAHs-MPs combined pollution in soils.

Conserved genomic neighborhood is a strong but no perfect indicator for a direct interaction of microbial gene products

BACKGROUND

The order of genes in bacterial genomes is not random; for example, the products of genes belonging to an operon work together in the same pathway. The cotranslational assembly of protein complexes is deemed to conserve genomic neighborhoods even stronger than a common function. This is why a conserved genomic neighborhood can be utilized to predict, whether gene products form protein complexes.

RESULTS

We were interested to assess the performance of a neighborhood-based classifier that analyzes a large number of genomes. Thus, we determined for the genes encoding the subunits of 494 experimentally verified hetero-dimers their local genomic context. In order to generate phylogenetically comprehensive genomic neighborhoods, we utilized the tools offered by the Enzyme Function Initiative. For each subunit, a sequence similarity network was generated and the corresponding genome neighborhood network was analyzed to deduce the most frequent gene product. This was predicted as interaction partner, if its abundance exceeded a threshold, which was the frequency giving rise to the maximal Matthews correlation coefficient. For the threshold of 16%, the true positive rate was 45%, the false positive rate 0.06%, and the precision 55%. For approximately 20% of the subunits, the interaction partner was not found in a neighborhood of ± 10 genes.

CONCLUSIONS

Our phylogenetically comprehensive analysis confirmed that complex formation is a strong evolutionary factor that conserves genome neighborhoods. On the other hand, for 55% of the cases analyzed here, classification failed. Either, the interaction partner was not present in a ± 10 gene window or was not the most frequent gene product.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence-Function Space and Genome Context to Discover Novel Functions

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of “genomic enzymology” web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence–function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems.