Merge pull request #1 from iTidetron/main

testing_mkdoc_webpage

docs/project/background.md

@@ -1 +1,13 @@
-# Background
+# Background  
+
+Bio-functional peptides (BioPeps) are short chains of amino acids with diverse biological activities, such as antimicrobial, anti-inflammatory, and anticancer properties. They are derived from various natural sources, including plants, animals, and microorganisms, and play vital roles in regulating physiological processes and maintaining health. Due to their therapeutic potential and natural origin, BioPeps are highly attractive for applications in the food, pharmaceutical, and biotechnology industries.
+
+However, the identification and industrial-scale utilization of these peptides face significant challenges. Traditional methods of discovering and characterizing BioPeps are labor-intensive and time-consuming, relying heavily on experimental approaches that are not feasible for large-scale production. Computational models have been developed to predict BioPeps, but they often suffer from limited accuracy and generalizability. These models struggle to handle the vast diversity of peptide sequences and functions, leading to high rates of false positives and unreliable predictions.
+
+Among the various types of BioPeps, antimicrobial peptides (AMPs) are of particular interest due to their potential as alternatives to traditional antibiotics. AMPs exhibit broad-spectrum activity against a wide range of pathogens and are less prone to resistance development, making them promising candidates for addressing the global challenge of antibiotic resistance. However, the production of AMPs in sufficient quantities remains a bottleneck, with traditional synthesis methods facing issues such as low yields, high costs, and cytotoxicity in host cells.
+
+To address these limitations, our research focuses on developing a comprehensive deep learning model that integrates state-of-the-art Bi-directional Long-Short Term Memory (BiLSTM) networks for predicting 13 distinct categories of BioPeps. This model aims to improve prediction accuracy and reduce false-positive rates, making it more suitable for industrial applications. Additionally, we have engineered a cell-free synthesis platform to facilitate the rapid and scalable production of AMPs, overcoming the challenges associated with traditional methods.
+
+Furthermore, to enhance the antimicrobial properties of Lactobacillus plantarum, a promising probiotic strain identified through our model, we employed directed evolution techniques. Using atmospheric and room temperature plasma (ARTP) mutagenesis and fluorescence-activated droplet sorting (FADS), we successfully generated L. plantarum mutants with significantly improved broad-spectrum antimicrobial activity and organic acid production.
+
+This integrated approach combining advanced computational modeling, innovative synthesis methods, and experimental evolution represents a significant step forward in the field of BioPeps research. It offers a promising pathway for the discovery and industrial production of novel antimicrobial agents and other bio-functional peptides, potentially revolutionizing various sectors, from food preservation to pharmaceutical development.

docs/project/results.md

@@ -1 +1,29 @@
-# Results
+# Results  
+
+### Results
+
+#### 1. **Model Performance and Predictive Accuracy**
+
+Our advanced BiLSTM-based deep learning model demonstrated exceptional performance across multiple BioPeps categories. In blinded testing, where the data was not previously seen by the model, the overall predictive accuracy for the model reached 94.8%. For antimicrobial peptides (AMPs), which were a primary focus of this study, the model achieved a robust accuracy of 94.8% in blinded testing and 88% in experimental validation. This high level of precision and consistency across different testing conditions underscores the model’s reliability and its potential for practical applications in bio-functional peptide prediction.
+
+#### 2. **Comparison with State-of-the-Art Models**
+
+We conducted a comprehensive comparison between our model and several existing state-of-the-art models, including UniDL4BioPep and NLPNNM. Our model significantly outperformed these models in both predictive accuracy and specificity. For categories such as anti-inflammatory peptides (AIPs) and anti-cancer peptides (ACPs), our model showed a reduction in false-positive rates by up to 30%, indicating improved specificity and a lower likelihood of erroneous predictions. This enhancement is critical for reducing experimental costs associated with validating false-positive predictions and for increasing the model's utility in industrial settings.
+
+#### 3. **Comprehensive BioPeps Profiling**
+
+Using our model, we performed an extensive profiling of BioPeps across 40 probiotics and 94 medicinal herbs. This analysis resulted in a detailed map of the distribution and abundance of various peptide categories within these sources. Notably, *Lactobacillus plantarum* emerged as a top candidate for AMP production due to its high peptide diversity and abundance. The profiling also identified several medicinal herbs, such as *Jatropha curcas* and *Dendrobium officinale*, as promising sources of bio-functional peptides with potential therapeutic applications.
+
+#### 4. **High-Yield AMP Production Platform**
+
+To address the challenges of AMP production, we developed a cell-free synthesis platform capable of producing AMPs at yields of 0.5-2.1 grams per liter within hours. This system leverages a cell-free protein expression setup, which circumvents issues such as cytotoxicity and low yield associated with traditional microbial fermentation. The synthesized AMPs were tested against a panel of pathogenic microorganisms, demonstrating broad-spectrum antimicrobial activity and validating the effectiveness of our production platform. This method offers a scalable and efficient solution for the large-scale production of AMPs, facilitating their use in industrial and pharmaceutical applications.
+
+#### 5. **Directed Evolution and Enhanced *Lactobacillus plantarum* Strains**
+
+We employed atmospheric and room temperature plasma (ARTP) mutagenesis combined with fluorescence-activated droplet sorting (FADS) to evolve *L. plantarum* strains with enhanced antimicrobial properties. Among the isolated mutants, three strains, designated M1, M2, and M3, exhibited significantly improved broad-spectrum antimicrobial activity compared to the wild-type strain. These mutants also showed increased production of organic acids, such as lactic and phenyllactic acid, further enhancing their potential as bio-preservatives in the food industry. Whole-genome sequencing of these mutants revealed specific genetic mutations associated with the enhanced phenotypes, providing insights into the molecular basis of their improved functionalities.
+
+#### 6. **Validation in Industrial Applications**
+
+The enhanced *L. plantarum* strains were tested in various food matrices, including soy sauce, coconut juice, bread, and lean meats, to evaluate their effectiveness as bio-preservatives. The results showed a significant reduction in spoilage microorganisms and pathogenic bacteria, extending the shelf life of these products without altering their sensory qualities. In pharmaceutical applications, the cell-free synthesized AMPs demonstrated potent antimicrobial activity against multidrug-resistant bacterial strains, suggesting their potential as novel therapeutic agents. These findings highlight the broad applicability of our research outcomes in real-world scenarios.
+
+Overall, the results of our study demonstrate the power of combining advanced computational modeling with innovative experimental techniques to discover and produce bio-functional peptides with high industrial relevance. This integrated approach has the potential to transform the way bio-functional peptides are identified, synthesized, and utilized across various sectors.