3D-Based RNA Function Prediction Tools in rnaglib.

Understanding the connection between complex structural features of RNA and biological function is a fundamental challenge in evolutionary studies and in RNA design. However, building datasets of RNA 3D structures and making appropriate modeling choices remain time-consuming and lack standardization. In this chapter, we describe the use of rnaglib, to train supervised and unsupervised machine learning-based function prediction models on datasets of RNA 3D structures.

Structure-based prediction of protein-nucleic acid binding using graph neural networks.

Protein-nucleic acid (PNA) binding plays critical roles in the transcription, translation, regulation, and three-dimensional organization of the genome. Structural models of proteins bound to nucleic acids (NA) provide insights into the chemical, electrostatic, and geometric properties of the protein structure that give rise to NA binding but are scarce relative to models of unbound proteins. We developed a deep learning approach for predicting PNA binding given the unbound structure of a protein that we call PNAbind. Our method utilizes graph neural networks to encode the spatial distribution of physicochemical and geometric properties of protein structures that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein, and using local encodings, they predict the location of individual NA binding residues. Our models can discriminate between specificity for DNA or RNA binding, and we show that predictions made on computationally derived protein structures can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92-0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and showed that our model predictions are consistent with and help explain experimental RNA binding data. Supplementary information: The online version contains supplementary material available at 10.1007/s12551-024-01201-w.

Community structure of soil microorganisms and endophytes of honeysuckle at different ecological niche specificities.

BACKGROUND: The plant microbiome is one of the key determinants of healthy plant growth. However, the complexity of microbial diversity in plant microenvironments in different regions, especially the relationship between subsurface and aboveground microorganisms, is not fully understood. The present study investigated the diversity of soil microorganisms in different regions and the diversity of microorganisms within different ecological niches, and compared soil microorganisms and endophytic microorganisms. METHODS: 16 S and ITS sequencing was used to sequence the soil and endophytes microbiome of honeysuckle. Alpha diversity analysis and principal component analysis (PCoA) were used to study the soil and endophyte microbial communities, and the function of endophyte bacteria and fungi was predicted based on the PICRUST2 process and FUNGuild. RESULTS: In total, there were 382 common bacterial genera and 139 common fungal genera in the soil of different producing areas of honeysuckle. There were 398 common bacterial genera and 157 common fungal genera in rhizosphere soil. More beneficial bacteria were enriched in rhizosphere soil. Endophytic bacteria were classified into 34 phyla and 770 genera. Endophytic fungi were classified into 11 phyla and 581 genera, among which there were significant differences in the dominant genera of roots, stems, leaves, and flowers, as well as in community diversity and richness. Endophytic fungal functions were mainly dominated by genes related to saprophytes, functional genes that could fight microorganisms were also found in KEGG secondary functional genes. CONCLUSION: More beneficial bacteria were enriched in rhizosphere soil of honeysuckle, and the microbial network of the rhizosphere is more complex than that of the soil. Among the tissues of honeysuckle, the flowers have the richest diversity of endophytes. The endogenous dominant core bacteria in each part of honeysuckle plant have a high degree of overlap with the dominant bacteria in soil. Functional prediction suggested that some dominant core bacteria have antibacterial effects, providing a reference for further exploring the strains with antibacterial function of honeysuckle. Understanding the interaction between honeysuckle and microorganisms lays a foundation for the study of growth promotion, quality improvement, and disease and pests control of honeysuckle from the perspective of microorganisms.

Design and Characterization of a Transcriptional Repression Toolkit for Plants.

Regulation of gene expression is essential for all life. Tools to manipulate the gene expression level have therefore proven to be very valuable in efforts to engineer biological systems. However, there are few well-characterized genetic parts that reduce gene expression in plants, commonly known as transcriptional repressors. We characterized the repression activity of a library consisting of repression motifs from approximately 25% of the members of the largest known family of repressors. Combining sequence information with our trans-regulatory function data, we next generated a library of synthetic transcriptional repression motifs with function predicted in advance. After characterizing our synthetic library, we demonstrated not only that many of our synthetic constructs were functional as repressors but also that our advance predictions of repression strength were better than random guesses. Finally, we assessed the functionality of known transcriptional repression motifs from a wide range of eukaryotes. Our study represents the largest plant repressor motif library experimentally characterized to date, providing unique opportunities for tuning transcription in plants.

Diversified crop rotations improve soil microbial communities and functions in a six-year field experiment.

Diversified crop rotations can help mitigate the negative impacts of increased agricultural intensity on the sustainability of agroecosystems. However, the impact of crop rotation diversity on the complexity of soil microbial association networks and ecological functions is still not well understood. In this study, a 6-year field experiment was conducted to evaluate how six different crop rotations change the composition and network complexity of soil microbial communities, as well as their related ecological functions. Microbial traits were measured in six crop rotations with different crop diversity index (CDI) during 2016-2022, including winter wheat-summer maize (CDI 1, WM) as the control, sweet potato→winter wheat-summer maize (CDI 1.5, SpWM), peanut→winter wheat-summer maize (CDI 1.5, PWM), soybean→winter wheat-summer maize (CDI 1.5, SWM), spring maize→winter wheat-summer maize (CDI 1.5, SmWM), and ryegrass-sweet sorghum→winter wheat-summer maize (CDI 2, RSWM). The study findings indicated that diversified crop rotations significantly increased ASV richness of both bacterial and fungal communities after 6-year treatments, and the ß-diversity profiles of bacterial and fungal communities significantly distinguished at the year of 2022 from 2016. The relative abundance of Acidobacteria and Chloroflexi was significantly enriched in SpWM rotation at 2022, while Basidiomycota significantly declined in five diversified rotations compared to WM. Diversified crop rotations at 2022 increased the complexity and density of bacterial and fungal networks than 2016. SpWM and PWM rotations had the highest functional groups involved in chemoheterotrophy and saprotroph, respectively. Structural equation modelling (SEM) also revealed that diversified crop rotations increased soil nutrients through improving the composition of bacterial communities and the augmented intricacy of the interconnections within both bacterial and fungal communities. This research underscores the importance of preserving the diversity and ecological functions of soil microorganisms in the nutrient-recycling processes for efficient agricultural practices.

Dual mechanism of membrane covering on GHG and NH3 mitigation during industrial-scale experiment on dairy manure composting: Inhibiting formation and blocking emissions.

An industrial-scale experiment on dairy manure composting with the control group (Ctrl) and the membrane covering group (CM) was conducted to explore the effects of functional membrane covering on gas emissions, the conversion of carbon and nitrogen, and revealing the underlying mechanisms. Results indicated that CM achieved the synergistic effects on gas mitigation and improved compost product quality. CO2, CH4, N2O, and NH3 emissions were reduced by 81.8%, 87.0%, 82.6%, and 82.2%, respectively. The micro-aerobic condition formed in membrane covering compost pile together with the covering inhibiting effect dominated the mitigation effect. CM significantly downregulated the mcrA gene copies and the value of mcrA/pmoA (p < 0.01), which reduced CH4 emission. CM decreased the nirS and nirK gene copies and increased the nosZ gene copies to reduce N2O emission. Functional Annotation of Prokaryotic Taxa showed that membrane covering effectively amended part of carbon and nitrogen cycles, which stimulated the degradation of organic matter, accelerated compost maturity and reduced the gaseous emissions.

Perfluorooctanoic acid effect and microbial mechanism to methane production in anaerobic digestion.

Perfluorooctanoic acid (PFOA) as emerging pollutants was largely produced and stable in nature environment. Its fate and effect to the wasted sludge digestion process and corresponding microbial mechanism was rarely reported. This study investigated the different dose of PFOA to the wasted sludge digestion process, where the methane yield and microbial mechanism was illustrated. The PFOA added before digestion were 0-10000 µg/L, no significant variation in daily and accumulated methane production between each group. The 9th day methane yield was significantly higher than other days (p < 0.05). The soluble protein was significantly decreased after 76 days digestion (p < 0.001). The total PFOA in sludge (R2 = 0.8817) and liquid (R2 = 0.9083) phase after digestion was exponentially correlated with PFOA dosed. The PFOA in liquid phase was occupied 54.10 ± 18.38% of the total PFOA in all reactors. The dewatering rate was keep decreasing with the increase of PFOA added (R2 = 0.7748, p < 0.001). The mcrA abundance was significantly correlated with the pH value and organic matter concentration in the reactors. Chloroflexi was the predominant phyla, Aminicenantales, Bellilinea and Candidatus_Cloacimonas were predominant genera in all reactors. Candidatus_Methanofastidiosum and Methanolinea were predominant archaea in all reactors. The function prediction by FAPROTAX and Tax4fun implied that various PFOA dosage resulted in significant function variation. The fermentation and anaerobic chemoheterotrophy function were improved with the PFOA dose. Co-occurrence network implied the potent cooperation among the organic matter degradation and methanogenic microbe in the digestion system. PFOA has little impact to the methane generation while affect the microbe function significantly, its remaining in the digested sludge should be concerned to reduce its potential environmental risks.

PDFll: Predictors of Disorder and Function of Proteins from the Language of Life.

The identification of intrinsically disordered proteins and their functional roles is largely dependent on the performance of computational predictors, necessitating a high standard of accuracy in these tools. In this context, we introduce a novel series of computational predictors, termed PDFll (Predictors of Disorder and Function of proteins from the Language of Life), which are designed to offer precise predictions of protein disorder and associated functional roles based on protein sequences. PDFll is developed through a two-step process. Initially, it leverages large-scale protein language models (pLMs), trained on an extensive dataset comprising billions of protein sequences. Subsequently, the embeddings derived from pLMs are integrated into streamlined, yet sophisticated, deep-learning models to generate predictions. These predictions notably surpass the performance of existing state-of-the-art predictors, particularly those that forecast disorder and function without utilizing evolutionary information.

EC number prediction of protein sequences based on combination of hierarchical and global features.

The identification of enzyme functions plays a crucial role in understanding the mechanisms of biological activities and advancing the development of life sciences. However, existing enzyme EC number prediction methods did not fully utilize protein sequence information and still had shortcomings in identification accuracy. To address this issue, we proposed an EC number prediction network using hierarchical features and global features (ECPN-HFGF). This method first utilized residual networks to extract generic features from protein sequences, and then employed hierarchical feature extraction modules and global feature extraction modules to further extract hierarchical and global features of protein sequences. Subsequently, the prediction results of both feature types were combined, and a multitask learning framework was utilized to achieve accurate prediction of enzyme EC numbers. Experimental results indicated that the ECPN-HFGF method performed best in the task of predicting EC numbers for protein sequences, achieving macro F1 and micro F1 scores of 95.5% and 99.0%, respectively. The ECPN-HFGF method effectively combined hierarchical and global features of protein sequences, allowing for rapid and accurate EC number prediction. Compared to current commonly used methods, this method offers significantly higher prediction accuracy, providing an efficient approach for the advancement of enzymology research and enzyme engineering applications.

Microbial enhanced manganese-autotrophic denitrification in reactor: performance, microbial diversity, potential functions.

Autotrophic denitrification technology has gained increasing attention in recent years owing to its effectiveness, economical, and environmentally friendly nature. However, the sluggish reaction rate has emerged as the primary impediment to its widespread application. Herein, a bio-enhanced autotrophic denitrification reactor with modified loofah sponge (LS) immobilized microorganisms was established to achieve efficient denitrification. Under autotrophic conditions, a nitrate removal efficiency of 59.55 % (0.642 mg/L/h) and a manganese removal efficiency of 86.48 % were achieved after bio-enhance, which increased by 20.92 % and 36.34 %. The bioreactor achieved optimal performance with denitrification and manganese removal efficiencies of 99.84 % (1.09 mg/L/h) and 91.88 %. ETSA and 3D-EEM analysis reveled manganese promoting electron transfer and metabolic activity of microorganisms. High-throughput sequencing results revealed as the increase of Mn(II) concentration, Cupriavidus became one of the dominant strains in the reactor. Prediction of metabolic functions results proved the great potential for Mn(II)-autotrophic denitrification of LS bioreactor.

Synergism of endophytic microbiota and plants promotes the removal of polycyclic aromatic hydrocarbons from the Alfalfa rhizosphere.

Endophytic bacteria can promote plant growth and accelerate pollutant degradation. However, it is unclear whether endophytic consortia (Consortium_E) can stabilize colonisation and degradation. We inoculated Consortium_E into the rhizosphere to enhance endophytic bacteria survival and promote pollutant degradation. Rhizosphere-inoculated Consortium_E enhanced polycyclic aromatic hydrocarbon (PAH) degradation rates by 11.5-13.1 % compared with sole bioaugmentation and plant treatments. Stable-isotope-probing (SIP) showed that the rhizosphere-inoculated Consortium_E had the largest number of degraders (8 amplicon sequence variants). Furthermore, only microbes from Consortium_E were identified among the degraders in bioaugmentation treatments, indicating that directly participated in phenanthrene metabolism. Interestingly, Consortium_E reshaped the community structure of degraders without significantly altering the rhizosphere community structure, and strengthened the core position of degraders in the network, facilitating close interactions between degraders and non-degraders in the rhizosphere, which were crucial for ensuring stable functionality. The synergistic effect between plants and Consortium_E significantly enhanced the upregulation of aromatic hydrocarbon degradation and auxiliary degradation pathways in the rhizosphere. These pathways showed a non-significant increasing trend in the uninoculated rhizosphere compared with the control, indicating that Consortium_E primarily promotes rhizosphere effects. Our results explore the Consortium_E bioaugmentation mechanism, providing a theoretical basis for the ecological restoration of contaminated soils.

[Advances in bioinformatics-based protein function prediction].

With the increasing of computer power and rapid expansion of biological data, the application of bioinformatics tools has become the mainstream approach to address biological problems. The accurate identification of protein function by bioinformatics tools is crucial for both biomedical research and drug discovery, making it a hot topic of research. In this paper, we categorize bioinformatics-based protein function prediction methods into three categories: protein sequence-based methods, protein structure-based methods, and protein interaction networks-based methods. We further analyze these specific algorithms, highlighting the latest research advancements and providing valuable references for the application of bioinformatics-based protein function prediction in biomedical research and drug discovery.

A comprehensive review and comparison of existing computational methods for protein function prediction.

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

Deep learning methods for protein function prediction.

Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in-depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting-edge methods to advance protein function prediction.

A BERT-based approach for identifying anti-inflammatory peptides using sequence information.

The use of anti-inflammatory peptides (AIPs) as an alternative therapeutic approach for inflammatory diseases holds great research significance. Due to the high cost and difficulty in identifying AIPs with experimental methods, the discovery and design of peptides by computational methods before the experimental stage have become promising technology. In this study, we present BertAIP, a bidirectional encoder representation from transformers (BERT)-based method for predicting AIPs directly from their amino acid sequence without using any other information. BertAIP implements a BERT model to extract features of a protein, and uses a fully connected feed-forward network for AIP classification. It was constructed and evaluated using the AIP datasets that were reconstructed from the latest Immune Epitope Database. The experimental results showed that BertAIP achieved an accuracy of 0.751 and a Matthews correlation coefficient of 0.451, which were higher than other commonly used methods. The results of the independent test suggested that BertAIP outperformed the existing AIP predictors. In addition, to enhance the interpretability of BertAIP, we explored and visualized the amino acids that the model considered important for AIP prediction. We believe that the BertAIP proposed herein will be a useful tool for large-scale screening and identifying novel AIPs for drug development and therapeutic research related to inflammatory diseases.

A large-scale assessment of sequence database search tools for homology-based protein function prediction.

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.

Application of a novel nested ensemble algorithm in predicting motor function recovery in patients with traumatic cervical spinal cord injury.

Traumatic cervical spinal cord injury (TCSCI) often causes varying degrees of motor dysfunction, common assessed by the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), in association with the American Spinal Injury Association (ASIA) Impairment Scale. Accurate prediction of motor function recovery is extremely important for formulating effective diagnosis, therapeutic and rehabilitation programs. The aim of this study is to investigate the validity of a novel nested ensemble algorithm that uses the very early ASIA motor score (AMS) of ISNCSCI examination to predict motor function recovery 6 months after injury in TCSCI patients. This retrospective study included complete data of 315 TCSCI patients. The dataset consisting of the first AMS at ≤ 24 h post-injury and follow-up AMS at 6 months post-injury was divided into a training set (80%) and a test set (20%). The nested ensemble algorithm was established in a two-stage manner. Support Vector Classification (SVC), Adaboost, Weak-learner and Dummy were used in the first stage, and Adaboost was selected as second-stage model. The prediction results of the first stage models were uploaded into second-stage model to obtain the final prediction results. The model performance was evaluated using precision, recall, accuracy, F1 score, and confusion matrix. The nested ensemble algorithm was applied to predict motor function recovery of TCSCI, achieving an accuracy of 80.6%, a F1 score of 80.6%, and balancing sensitivity and specificity. The confusion matrix showed few false-negative rate, which has crucial practical implications for prognostic prediction of TCSCI. This novel nested ensemble algorithm, simply based on very early AMS, provides a useful tool for predicting motor function recovery 6 months after TCSCI, which is graded in gradients that progressively improve the accuracy and reliability of the prediction, demonstrating a strong potential of ensemble learning to personalize and optimize the rehabilitation and care of TCSCI patients.

Reproductive chemical database: a curated database of chemicals that modulate protein targets regulating important reproductive biological processes.

Recent studies have shifted the spotlight from adult disease to gametogenesis and embryo developmental events, and these are greatly affected by various environmental chemicals, such as drugs, metabolites, pollutants, and others. Growing research has highlighted the critical importance of identifying and understanding the roles of chemicals in reproductive biology. However, the functions and mechanisms of chemicals in reproductive processes remain incomplete. We developed a comprehensive database called the Reproductive Chemical Database (RCDB) ( https://yu.life.sjtu.edu.cn/ChenLab/RCDB ) to facilitate research on chemicals in reproductive biology. This resource is founded on rigorous manual literature extraction and precise protein target prediction methodologies. This database focuses on the delineation of chemicals associated with phenotypes, diseases, or endpoints intricately associated with four important reproductive processes: female and male gamete generation, fertilization, and embryo development in human and mouse. The RCDB encompasses 93 sub-GO processes, and it revealed 1447 intricate chemical-biological process interactions. To date, the RCDB has meticulously cataloged and annotated 830 distinct chemicals, while also predicting 614 target proteins from a selection of 3800 potential candidates. Additionally, the RCDB offers an online predictive tool that empowers researchers to ascertain whether specific chemicals play discernible functional roles in these reproductive processes. The RCDB is an exhaustive, cross-platform, manually curated database, which provides a user-friendly interface to search, browse, and use reproductive processes modulators and their comprehensive related information. The RCDB will help researchers to understand the whole reproductive process and related diseases and it has the potential to promote reproduction research in the pharmacological and pathophysiological areas.

Enhancing Gene Co-Expression Network Inference for the Malaria Parasite Plasmodium falciparum.

BACKGROUND: Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance. RESULTS: Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks' edges (gene co-expression relationships), as well as predicted functional knowledge. The networks' edges are overall complementary: 47-85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community. CONCLUSIONS: The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible. SUPPLEMENTARY DATA: Attached.

Changes in soil organic carbon components and microbial community following spent mushroom substrate application.

While spent mushroom substrate (SMS) has shown promise in increasing soil organic carbon (SOC) and improving soil quality, research on the interplay between SOC components and microbial community following the application of diverse SMS types remains scant. A laboratory soil incubation experiment was conducted with application of two types of SMSs from cultivation of Pleurotus eryngii (PE) and Agaricus bisporus (AB), each at three application rates (3, 5.5, and 8%). Advanced techniques, including solid-state 13C nuclear magnetic resonance (NMR) and high-throughput sequencing, were employed to investigate on SOC fractions and chemical structure, microbial community composition and functionality. Compared to SMS-AB, SMS-PE application increased the relative abundances of carbohydrate carbon and O-alkyl C in SOC. In addition, SMS-PE application increased the relative abundance of the bacterial phylum Proteobacteria and those of the fungal phyla Basidiomycota and Ascomycota. The relative abundances of cellulose-degrading bacterial (e.g., Flavisolibacter and Agromyces) and fungal genera (e.g., Myceliophthora, Thermomyces, and Conocybe) were increased as well. The application of SMS-AB increased the aromaticity index of SOC, the relative abundance of aromatic C, and the contents of humic acid and heavy fraction organic carbon. In addition, SMS-AB application significantly increased the relative abundances of the bacterial phyla Firmicutes and Actinobacteria. Notably, the genera Actinomadura, Ilumatobacter, and Bacillus, which were positively correlated with humic acid, experienced an increase in relative abundance. Functional prediction revealed that SMS-PE application elevated carbohydrate metabolism and reduced the prevalence of fungal pathogens, particularly Fusarium. The application of high-rate SMS-AB (8%) enhanced bacterial amino acid metabolism and the relative abundances of plant pathogenic fungi. Our research provides strategies for utilizing SMS to enrich soil organic carbon and fortify soil health, facilitating the achievement of sustainable soil management.