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Deep learning models show promise in accelerating the design and optimization of antimicrobial peptides (AMPs), but current methods face challenges, such as low success rates, or large virtual library scales. In this study, we introduce DLFea4AMPGen, a bioactive peptide design strategy that leverages deep learning models to identify and extract key features associated with antimicrobial peptide activity. This approach enables the generation of peptide sequences with potential bioactivities. Using the SHapley Additive exPlanations (SHAP) method, we quantify the contribution of each amino acid in multifunctional peptides with potential antibacterial, antifungal, and antioxidant activities. Key feature fragments (KFFs) with the highest average contributions are extracted and classified into four subfamilies based on amino acid frequency. These high-frequency amino acids are systematically arranged to generate a plausible sequence subspace for candidate peptides, from which 16 representative sequences were selected for experimental validation. The results show that 75% (12/16) of the sequences exhibited at least two types of activity. Notably, D1 exhibits broad-spectrum antimicrobial activity, including efficacy against multidrug-resistant clinical pathogenic isolates both in vitro and in vivo. This proof-of-concept study underscores the potential of the DLFea4AMPGen platform for efficient design and screening of bioactive peptides, showcasing its value in AMP research. This study reports DLFea4AMPGen, a peptide design strategy that leverages deep learning to identify and extract key features for the generation of new antimicrobial peptides (AMPs), which allowed for the identification of 12 AMPs with bioactivity.

Background Advances in DNA sequencing technologies have transformed our capacity to perform life science research, decipher the dynamics of complex soil microbial communities and exploit them for plant disease management. However, soil is a complex conglomerate, which makes functional metagenomics studies very challenging. Results Metagenomes were assembled by long-read (PacBio, PB), short-read (Illumina, IL), and mixture of PB and IL (PI) sequencing of soil DNA samples were compared. Ortholog analyses and functional annotation revealed that the PI approach significantly increased the contig length of the metagenomic sequences compared to IL and enlarged the gene pool compared to PB. The PI approach also offered comparable or higher species abundance than either PB or IL alone, and showed significant advantages for studying natural product biosynthetic genes in the soil microbiomes. Conclusion Our results provide an effective strategy for combining long and short-read DNA sequencing data to explore and distill the maximum information out of soil metagenomics.

Background Cadmium (Cd) is a severely toxic heavy metal to most microorganisms. Many bacteria have developed Cd 2+ resistance. Results In this study, we isolated two different Cd 2+ resistance Bacillus sp . strains, Bacillus vietamensis 151–6 and Bacillus marisflavi 151–25, which could be grown in the presence of Cd 2+ at concentration up to 0.3 mM and 0.8 mM, respectively. According to the genomic sequencing, transcriptome analysis under cadmium stress, and other related experiments, a gene cluster in plasmid p25 was found to be a major contributor to Cd 2+ resistance in B. marisflavi 151–25. The cluster in p25 contained orf4802 and orf4803 which encodes an ATPase transporter and a transcriptional regulator protein, respectively. Although 151–6 has much lower Cd 2+ resistance than 151–25, they contained similar gene cluster, but in different locations. A gene cluster on the chromosome containing orf4111 , orf4112 and orf4113 , which encodes an ATPase transporter, a cadmium efflux system accessory protein and a cadmium resistance protein, respectively, was found to play a major role on the Cd 2+ resistance for B. vietamensis 151–6. Conclusions This work described cadmium resistance mechanisms in newly isolated Bacillus vietamensis 151–6 and Bacillus marisflavi 151–25. Based on homologies to the cad system (CadA-CadC) in Staphylococcus aureus and analysis of transcriptome under Cd 2+ induction, we inferred that the mechanisms of cadmium resistance in B. marisflavi 151–25 was as same as the cad system in S. aureus . Although Bacillus vietamensis 151–6 also had the similar gene cluster to B. marisflavi 151–25 and S. aureus , its transcriptional regulatory mechanism of cadmium resistance was not same. This study explored the cadmium resistance mechanism for B. vietamensis 151–6 and B. marisflavi 151–25 and has expanded our understanding of the biological effects of cadmium.

Background An important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (ΔΔ G ) of thermal denaturation. Results We have developed predictive software, Prethermut, based on machine learning methods, to predict the effect of single- or multi-site mutations on protein thermostability. The input vector of Prethermut is based on known structural changes and empirical measurements of changes in potential energy due to protein mutations. Using a 10-fold cross validation test on the M-dataset, consisting of 3366 mutants proteins from ProTherm, the classification accuracy of random forests and the regression accuracy of random forest regression were slightly better than support vector machines and support vector regression, whereas the overall accuracy of classification and the Pearson correlation coefficient of regression were 79.2% and 0.72, respectively. Prethermut performs better on proteins containing multi-site mutations than those with single mutations. Conclusions The performance of Prethermut indicates that it is a useful tool for predicting changes in protein thermostability brought about by single- or multi-site mutations and will be valuable in the rational design of proteins.

Background Amyloid fibrillar aggregates of proteins or polypeptides are known to be associated with many human diseases. Recent studies suggest that short protein regions trigger this aggregation. Thus, identifying these short peptides is critical for understanding diseases and finding potential therapeutic targets. Results We propose a method, named Pafig ( P rediction of a myloid fi bril-forming se g ments) based on support vector machines, to identify the hexpeptides associated with amyloid fibrillar aggregates. The features of Pafig were obtained by a two-round selection from AAindex. Using a 10-fold cross validation test on Hexpepset dataset, Pafig performed well with regards to overall accuracy of 81% and Matthews correlation coefficient of 0.63. Pafig was used to predict the potential fibril-forming hexpeptides in all of the 64,000,000 hexpeptides. As a result, approximately 5.08% of hexpeptides showed a high aggregation propensity. In the predicted fibril-forming hexpeptides, the amino acids – alanine, phenylalanine, isoleucine, leucine and valine occurred at the higher frequencies and the amino acids – aspartic acid, glutamic acid, histidine, lysine, arginine and praline, appeared with lower frequencies. Conclusion The performance of Pafig indicates that it is a powerful tool for identifying the hexpeptides associated with fibrillar aggregates and will be useful for large-scale analysis of proteomic data.

Pullulanase (EC 3.2.1.41) plays an important role in the specific hydrolysis of branch points in amylopectin. Enhancing its thermostability is required for its industrial application. In this study, rational protein design was used to improve the thermostability of PulB from Bacillus naganoensis (AB231790.1), which has strong enzymatic properties. Three positive single-site mutants (PulB-D328H, PulB-N387D, and PulB-A414P) were selected from six mutants. After incubation at 65°C for 5 min, the residual activities of PulB-D328H, PulB-N387D, and PulB-A414P were 4.5-, 1.7-, and 1.47-fold higher than PulB-WT, and their Tm values (the temperature at which half protein molecule denature) were 1.8°C, 0.4°C, and 0.9°C higher than PulB-WT, respectively. Then the final combined mutant PulB-328/387/414 was constructed. The t1/2 of it was 12.9-fold longer than that of PulB-WT at 65°C and the total increase in Tm of it (5.0°C) was almost 60% greater than the sum of individual increases (3.1°C). In addition, kinetic studies revealed that the kcat and the kcat/Km of PulB-328/387/414 increased by 38.8% and 12.9%. The remarkable improvement in thermostability and the high catalytic efficiency of PulB-328/387/414 make it suitable for industrial applications.

Chitin is abundant in nature and its degradation products are highly valuable for numerous applications. Thermophilic chitinases are increasingly appreciated for their capacity to biodegrade chitin at high temperatures and prolonged enzyme stability. Here, using deep learning approaches, we developed a prediction tool, Preoptem, to screen thermophilic proteins. A novel thermophilic chitinase, Chi304, was mined directly from the marine metagenome. Chi304 showed maximum activity at 85 ℃, its Tm reached 89.65 ± 0.22℃, and exhibited excellent thermal stability at 80 and 90 °C. Chi304 had both endo- and exo-chitinase activities, and the (GlcNAc)2 was the main hydrolysis product of chitin-related substrates. The product yields of colloidal chitin degradation reached 97% within 80 min, and 20% over 4 days of reaction with crude chitin powder. This study thus provides a method to mine the novel thermophilic chitinase for efficient chitin biodegradation.

Of the 20 common amino acids, 18 are encoded by multiple synonymous codons. These synonymous codons are not redundant; in fact, all of codons contribute substantially to protein expression, structure and function. In this study, the codon usage pattern of genes in the E. coli was learned from the sequenced genomes of E. coli . A machine learning based method, Presyncodon was proposed to predict synonymous codon selection in E. coli based on the learned codon usage patterns of the residue in the context of the specific fragment. The predicting results indicate that Presycoden could be used to predict synonymous codon selection of the gene in the E. coli with the high accuracy. Two reporter genes ( egfp and mApple ) were designed with a combination of low- and high-frequency-usage codons by the method. The fluorescence intensity of eGFP and mApple expressed by the ( egfp and mApple ) designed by this method was about 2.3- or 1.7- folds greater than that from the genes with only high-frequency-usage codons in E. coli . Therefore, both low- and high-frequency-usage codons make positive contributions to the functional expression of the heterologous proteins. This method could be used to design synthetic genes for heterologous gene expression in biotechnology.

Background para -Nitrophenol (PNP), a priority environmental pollutant, is hazardous to humans and animals. However, the information relating to the PNP degradation pathways and their enzymes remain limited. Results Pseudomonas sp.1-7 was isolated from methyl parathion (MP)-polluted activated sludge and was shown to degrade PNP. Two different intermediates, hydroquinone (HQ) and 4-nitrocatechol (4-NC) were detected in the catabolism of PNP. This indicated that Pseudomonas sp.1-7 degraded PNP by two different pathways, namely the HQ pathway, and the hydroxyquinol (BT) pathway (also referred to as the 4-NC pathway). A gene cluster ( pdcEDGFCBA ) was identified in a 10.6 kb DNA fragment of a fosmid library, which cluster encoded the following enzymes involved in PNP degradation: PNP 4-monooxygenase (PdcA), p -benzoquinone (BQ) reductase (PdcB), hydroxyquinol (BT) 1,2-dioxygenase (PdcC), maleylacetate (MA) reductase (PdcF), 4-hydroxymuconic semialdehyde (4-HS) dehydrogenase (PdcG), and hydroquinone (HQ) 1,2-dioxygenase (PdcDE). Four genes ( pdcDEFG ) were expressed in E. coli and the purified pdcDE , pdcG and pdcF gene products were shown to convert HQ to 4-HS, 4-HS to MA and MA to β-ketoadipate respectively by in vitro activity assays. Conclusions The cloning, sequencing, and characterization of these genes along with the functional PNP degradation studies identified 4-NC, HQ, 4-HS, and MA as intermediates in the degradation pathway of PNP by Pseudomonas sp.1-7. This is the first conclusive report for both 4-NC and HQ- mediated degradation of PNP by one microorganism.

The multicopper oxidase CueO is involved in copper homeostasis and copper (Cu) tolerance in Escherichia coli . The laccase activity of CueO G304K mutant is higher than wild-type CueO. To explain this increase in activity, we solved the crystal structure of G304K mutant at 1.49 Å. Compared with wild-type CueO, the G304K mutant showed dramatic conformational changes in methionine-rich helix and the relative regulatory loop (R-loop). We further solved the structure of Cu-soaked enzyme, and found that the addition of Cu ions induced further conformational changes in the R-loop and methionine-rich helix as a result of the new Cu-binding sites on the enzyme’s surface. We propose a mechanism for the enhanced laccase activity of the G304K mutant, where movements of the R-loop combined with the changes of the methionine-rich region uncover the T1 Cu site allowing greater access of the substrate. Two of the G304K double mutants showed the enhanced or decreased laccase activity, providing further evidence for the interaction between the R-loop and the methionine-rich region. The cuprous oxidase activity of these mutants was about 20% that of wild-type CueO. These structural features of the G304K mutant provide clues for designing specific substrate-binding mutants in the biotechnological applications.