Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.

Functional peptides play crucial roles in various biological processes and hold significant potential in many fields such as drug discovery and biotechnology. Accurately predicting the functions of peptides is essential for understanding their diverse effects and designing peptide-based therapeutics. Here, we propose CELA-MFP, a deep learning framework that incorporates feature Contrastive Enhancement and Label Adaptation for predicting Multi-Functional therapeutic Peptides. CELA-MFP utilizes a protein language model (pLM) to extract features from peptide sequences, which are then fed into a Transformer decoder for function prediction, effectively modeling correlations between different functions. To enhance the representation of each peptide sequence, contrastive learning is employed during training. Experimental results demonstrate that CELA-MFP outperforms state-of-the-art methods on most evaluation metrics for two widely used datasets, MFBP and MFTP. The interpretability of CELA-MFP is demonstrated by visualizing attention patterns in pLM and Transformer decoder. Finally, a user-friendly online server for predicting multi-functional peptides is established as the implementation of the proposed CELA-MFP and can be freely accessed at http://dreamai.cmii.online/CELA-MFP.

Critically infected patients with COVID-19 (coronavirus disease 2019) are prone to develop sepsis-related coagulopathy as a result of a robust immune response. The mechanism underlying the relationship between sepsis and COVID-19 is largely unknown. LMWH (low molecular weight heparin) exhibits both anti-inflammatory and anti-coagulating properties that result in a better prognosis of severely ill patients with COVID-19 co-associated with sepsis-induced coagulopathy or with a higher D-dimer value. Heparin-associated molecular targets and their mechanism of action in sepsis/COVID-19 are not well understood. In this work, we characterize the pharmacological targets, biological functions and therapeutic actions of heparin in sepsis/COVID-19 from the perspective of network pharmacology. A total of 38 potential targets for heparin action against sepsis/COVID-19 and 8 core pharmacological targets were identified, including IL6, KNG1, CXCL8, ALB, VEGFA, F2, IL10 and TNF. Moreover, enrichment analysis showed that heparin could help in treating sepsis/COVID-19 through immunomodulation, inhibition of the inflammatory response, regulation of angiogenesis and antiviral activity. The pharmacological effects of heparin against these targets were further confirmed by molecular docking and simulation analysis, suggesting that heparin exerts effective binding capacity by targeting the essential residues in sepsis/COVID-19. Prospective clinical practice evaluations may consider the use of these key prognostic indicators for the treatment of sepsis/COVID-19.Communicated by Ramaswamy H. Sarma

Recently, peptide-based drugs have gained unprecedented interest in discovering and developing antifungal drugs due to their high efficacy, broad-spectrum activity, low toxicity and few side effects. However, it is time-consuming and expensive to identify antifungal peptides (AFPs) experimentally. Therefore, computational methods for accurately predicting AFPs are highly required. In this work, we develop AFP-MFL, a novel deep learning model that predicts AFPs only relying on peptide sequences without using any structural information. AFP-MFL first constructs comprehensive feature profiles of AFPs, including contextual semantic information derived from a pre-trained protein language model, evolutionary information, and physicochemical properties. Subsequently, the co-attention mechanism is utilized to integrate contextual semantic information with evolutionary information and physicochemical properties separately. Extensive experiments show that AFP-MFL outperforms state-of-the-art models on four independent test datasets. Furthermore, the SHAP method is employed to explore each feature contribution to the AFPs prediction. Finally, a user-friendly web server of the proposed AFP-MFL is developed and freely accessible at http://inner.wei-group.net/AFPMFL/, which can be considered as a powerful tool for the rapid screening and identification of novel AFPs.

Motivation Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information.Results In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite.Availability and implementation The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.

Serum uric acid elevation has been found in long-term nickel (Ni) exposure occupational workers, but the mechanism is unclear. In this study, the relationship between Ni exposure and uric acid elevation was explored in a cohort of 109 participants composed of a Ni-exposed workers group and a control group. The results showed that Ni concentration (5.70 ± 3.21 μg/L) and uric acid level (355.95 ± 67.87 μmol/L) in the serum were increased in the exposure group with a significant positive correlation (r = 0.413, p < 0.0001). The composition of gut microbiota and metabolome revealed that the abundance of uric acid-lowering bacteria, such as Lactobacillus, Lachnospiraceae_Unclassfied and Blautia were reduced while pathogenic bacteria including Parabacteriadies and Escherichia-Shigella were enriched in Ni group, accompanied by impaired intestinal degradation of purines and upregulated biosynthesis of primary bile acids. Consistent with human results, the mice experiments showed that Ni treatment significantly promotes uric acid elevation and systemic inflammation. Lactobacillus and Blautia in gut microbiota were reduced and inflammation-related taxa Alistipes and Mycoplasma were enriched in the Ni treatment. In addition, LC-MS/MS metabolomic analysis indicated that purine nucleosides were accumulated in mice feces, which increased purine absorption and uric acid elevation in the serum. In summary, this study provides evidence that UA elevation was correlated with heavy metals exposure and highlighted the role of gut microbiota in intestinal purine catabolism and in the pathogenesis of heavy metal-induced hyperuricemia.

Bio-degradation is the most affordable method of azo dye decontamination, while its drawbacks such as aromatic amines accumulation and low degradation efficiency must be overcome. In this study, a novel mechanism of azo dye degradation by a fungus was discovered. At a concentration of 400 mg/L, the decolorization efficiency of Acid Red 73 (AR73) by Aspergillus tabacinus LZ-M was 90.28%. Metabolite analysis and transcriptome sequencing analysis revealed a self-redox process of AR73 degradation, where the electrons generated in carbon oxidation were transferred to the reduction of -C-N = and -NN. The metabolites, 2-hydroxynaphthalene and N-phenylnitrous amide were mineralized into CO2 through catechol pathway and a glycolytic process. Furthermore, the mineralization ratio of dye was computed to be 31.8% by the carbon balance and electron balance. By using comparative transcriptome, a novel decoloring enzyme Ord95 was discovered in unknown genes through gene cloning. It hydrolyzed AR73 into 2-hydroxynaphthalene and N-phenylnitrous amide, containing a glutathione S-transferase domain with three arginines as key active sites. Here the new mechanism of azo dye degradation was discovered with identification of a novel enzyme in Aspergillus tabacinus LZ-M.

Metronidazole (MNZ) accumulation inhibits municipal wastewater treatment bio-systems, and an effective solution to augment anaerobic activated sludge (AAS) is required. This research discovered that Aspergillus tabacinus LZ-M could degrade 77.39% of MNZ at 5 mg/L. MNZ was metabolized into urea, and the enzymes involved in its degradation were aminotransferase, methyltransferase, monooxygenase, and CN cleavage hydrolase. The strain was immobilized in polyurethane foam and used in AAS for the treatment of MNZ-containing municipal wastewater. The results showed that, using immobilized LZ-M, MNZ was completely removed, and the degradation efficiency of wastewater's chemical oxygen demand (COD) was increased from 11.7% to 83.31%. The extracellular polymer and ROS levels indicated that MNZ's toxicity on AAS was reduced. Furthermore, bioaugmentation stabilized its microbial community, and decreased MNZ resistance genes. These observations confirm that the immobilized fungi are effective in protecting AAS against antibiotic contamination in the treatment process of municipal wastewater.

IntroductionThe COVID-19 pandemic has posed a major burden on healthcare and economic systems across the globe for over 3 years. Even though vaccines are available, the pathogenesis is still unclear. Multiple studies have indicated heterogeneity of immune responses to SARS-CoV-2, and potentially distinct patient immune types that might be related to disease features. However, those conclusions are mainly inferred by comparing the differences of pathological features between moderate and severe patients, some immunological features may be subjectively overlooked.MethodsIn this study, the relevance scores(RS), reflecting which features play a more critical role in the decision-making process, between immunological features and the COVID-19 severity are objectively calculated through neural network, where the input features include the immune cell counts and the activation marker concentrations of particular cell, and these quantified characteristic data are robustly generated by processing flow cytometry data sets containing the peripheral blood information of COVID-19 patients through PhenoGraph algorithm.ResultsSpecifically, the RS between immune cell counts and COVID-19 severity with time indicated that the innate immune responses in severe patients are delayed at the early stage, and the continuous decrease of classical monocytes in peripherial blood is significantly associated with the severity of disease. The RS between activation marker concentrations and COVID-19 severity suggested that the down-regulation of IFN-γ in classical monocytes, Treg, CD8 T cells, and the not down-regulation of IL_17a in classical monocytes, Tregs are highly correlated with the occurrence of severe disease. Finally, a concise dynamic model of immune responses in COVID-19 patients was generalized.DiscussionThese results suggest that the delayed innate immune responses in the early stage, and the abnormal expression of IL-17a and IFN-γ in classical monocytes, Tregs, and CD8 T cells are primarily responsible for the severity of COVID-19.

Some insects can degrade both natural and synthetic plastic polymers, their host and gut microbes play crucial roles in this process. However, there is still a scientific gap in understanding how the insect adapted to the polystyrene (PS) diet from natural feed. In this study, we analyzed diet consumption, gut microbiota responses, and metabolic pathways of Tenebrio molitor larvae exposed to PS and corn straw (CS). T. molitor larvae were incubated under controlled conditions (25 ± 1 °C, 75 ± 5

Heavy metals (e.g. copper) in smelting wastewater inhibited microbial Cr(VI) reduction, previous report found that a novel chromate reductase NfoR exhibited Cu(II)-enhanced activity and broad-spectrum metal tolerance. In this study, magnetic biochar (MB) from pine sawdust was fabricated to immobilize NfoR via covalent binding. MB-NfoR showed a 2.6-and 2.1-folds higher activity in reducing Cr(VI) to Cr(III) compared to free NfoR and MB, respectively. XPS and EPR analysis showed that NfoR catalysis and persistent free radicals (PFRs) are responsible for Cr(VI) reduction mechanism of MB-NfoR. The addition of Cu(II) increased the MB-NfoR activity 2.3-fold compared to that of without Cu(II). The maximum Cr(VI) removal by MB-NfoR in the pure Cr(VI) solution was 98% at 45 C and pH 6.0, and the MB-NfoR retained 68.3% of its initial activity even after five consecutive cycles. Application of MB-NfoR in smelting wastewater can simultaneously remove 94% Cr(VI) and 52.1% Cu(II). Furthermore, treated smelting wastewater by MB-NfoR showed no phytotoxicity and enriched numerous oligotrophic bacteria. This study provides a novel 'one-stop' strategy for decontamination of effluents combined with heavy metals, utilizing synergies offered by constituents in such waste streams.

Existing studies reported higher altitudes reduce the COVID-19 infection rate in the United States, Colombia, and Peru. However, the underlying reasons for this phenomenon remain unclear. In this study, regression analysis and mediating effect model were used in a combination to explore the altitudes relation with the pattern of transmission under their correlation factors. The preliminary linear regression analysis indicated a negative correlation between altitudes and COVID-19 infection in China. In contrast to environmental factors from low-altitude regions (<1500 m), high-altitude regions (>1500 m) exhibited lower PM2.5, average temperature (AT), and mobility, accompanied by high SO2 and absolute humidity (AH). Non-linear regression analysis further revealed that COVID-19 confirmed cases had a positive correlation with mobility, AH, and AT, whereas negatively correlated with SO2, CO, and DTR. Subsequent mediating effect model with altitude-correlated factors, such as mobility, AT, AH, DTR and SO2, suffice to discriminate the COVID-19 infection rate between low- and high-altitude regions. The mentioned evidence advance our understanding of the altitude-mediated COVID-19 transmission mechanism.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug-drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision-recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.

Rooting is a key innovation during plant terrestrialization. RGFs/GLVs/CLELs are a family of secreted peptides, playing key roles in root stem cell niche maintenance and pattern formation. The origin of this peptide family is not well characterized. RGFs and their receptor genes, RGIs, were investigated comprehensively using phylogenetic and genetic analyses. We identified 203 RGF genes from 24 plant species, representing a variety of land plant lineages. We found that the RGF genes originate from land plants and expand via multiple duplication events. The lineage-specific RGF duplicates are retained due to their regulatory divergence, while a majority of RGFs experienced strong purifying selection in most land plants. Functional analysis indicated that RGFs and their receptor genes, RGIs, isolated from liverwort, tomato, and maize possess similar biological functions with their counterparts from Arabidopsis in root development. RGFs and RGIs are likely coevolved in land plants. Our studies shed light on the origin and functional conservation of this important peptide family in plant root development.