LABAMPsGCN: A framework for identifying lactic acid bacteria antimicrobial peptides based on graph convolutional neural network

Deep Learning Accelerates the Development of Antimicrobial Peptides Comprising 15 Amino Acids

The emergence of multidrug-resistant bacteria has led to an urgent need for novel antimicrobial agents. Antimicrobial peptides (AMPs) exhibit broad-spectrum and highly effective antibacterial activity and are less prone to resistance, making them potential candidates for the next generation of antimicrobial drugs. However, screening for AMPs from a vast library of peptides through wet lab experiments is a slow and laborious process. By leveraging large datasets of labeled peptides, researchers utilize deep learning algorithms to train models that capture complex patterns and features associated with antimicrobial activity, which advance the discovery and development of novel AMPs. Since the discovery of certain lengths of AMPs has been rarely reported, we applied deep learning to accelerate the discovery of AMPs consisting of 15 amino acids and developed a model named AMPPRED15 in this article. Wet lab experiments were also conducted to evaluate the performance of the model. Fortunately, we successfully identified two AMPs, one of which demonstrated antibacterial activities comparable to the marketed antibiotic cefoperazone sodium.

AI Methods for Antimicrobial Peptides: Progress and Challenges

Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models. Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure-guided AMP discovery and design. This review aims to fill that gap by providing a comprehensive overview of the latest advancements, challenges and opportunities in using AI methods, with a particular emphasis on LLMs, GNNs and structure-guided design. We discuss the limitations of current approaches and highlight the most relevant topics to address in the coming years for AMP discovery and design.

Antimicrobial peptides: An alternative to traditional antibiotics

As antibiotic-resistant bacteria and genes continue to emerge, the identification of effective alternatives to traditional antibiotics has become a pressing issue. Antimicrobial peptides are favored for their safety, low residue, and low resistance properties, and their unique antimicrobial mechanisms show significant potential in combating antibiotic resistance. However, the high production cost and weak activity of antimicrobial peptides limit their application. Moreover, traditional laboratory methods for identifying and designing new antimicrobial peptides are time-consuming and labor-intensive, hindering their development. Currently, novel technologies, such as artificial intelligence (AI) are being employed to develop and design new antimicrobial peptide resources, offering new opportunities for the advancement of antimicrobial peptides. This article summarizes the basic characteristics and antimicrobial mechanisms of antimicrobial peptides, as well as their advantages and limitations, and explores the application of AI in antimicrobial peptides prediction amd design. This highlights the crucial role of AI in enhancing the efficiency of antimicrobial peptide research and provides a reference for antimicrobial drug development.

Multi-Strain Probiotics Enhance the Bioactivity of Cascara Kombucha during Microbial Composition-Controlled Fermentation

Kombucha is a widely consumed fermented tea beverage with diverse health benefits. In a previous study, we demonstrated that the use of cascara as a substrate results in a special kombucha beverage with high bioactivity. Traditional kombucha fermentation using a symbiotic culture of bacteria and yeast (SCOBY) can lead to inconsistent product quality because of the lack of control over microbial composition. We successfully isolated and identified yeast and bacteria, including Saccharomyces cerevisiae, Komagataeibacter rhaeticus, and Lactobacillus brevis that are appropriate starter cultures for cascara kombucha fermentation. We also demonstrated that a supplementation with lactic acid bacteria (LAB) and a mixture of S. cerevisiae and K. rhaeticus resulted in higher total polyphenol and flavonoid content of cascara kombucha compared with the traditionally fermented product using SCOBY as the inoculum. The free radical scavenging activity, inhibitory effects on α-amylase, tyrosinase activity, and antibacterial properties of cascara kombucha were also enhanced as a result of LAB supplement. These findings provide valuable insights into the controlled microbiological composition required for the fermentation of cascara kombucha, thereby ensuring consistent quality and enhanced bioactivity of the product. Further, the use of cascara as a substrate for kombucha production not only offers various health benefits and biological effects, but also repurposes by-products from the coffee industry, which contributes to sustainable development and is eco-friendly.

Geometric deep learning as a potential tool for antimicrobial peptide prediction

Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.