# Antimicrobial Peptide Screening from Microbial Genomes in Sludge Based on Deep Learning
## 1 Introduction  
Antimicrobial resistance (AMR) has emerged as a formidable challenge in the realm of global health, precipitated by the misuse or overuse of antibiotics [1]. This overreliance on antibiotics complicates infection treatment, as bacteria evolve resistance to commonly utilized antimicrobial agents, potentially culminating in treatment failures, aggravated illness, or even mortality [2]. In addition to curbing antibiotic resistance, the development of new antimicrobial drugs has become a crucial strategy in addressing this challenge. Antimicrobial peptides (AMPs), favored by researchers for their low acquisition resistance rate and low bacterial mutagenesis rate, are considered as potential alternatives to antibiotics [3]. Therefore, the screening of novel AMPs has become a critical research direction in the field of biomedicine. Generally, AMPs can be obtained through chemical synthesis or from animals, plants, and microorganisms [4]. Recent studies have found that sludge microbiota exhibit rich biodiversity, making them a significant resource for obtaining antimicrobial peptides. Therefore, exploring novel antimicrobial peptides from sludge microbiota offers new possibilities for addressing the issue of antibiotic resistance. Traditional AMP screening methodologies involve laborious and time-intensive separation and identification processes, hampering high-throughput capabilities [5]. However, with the advent of machine learning and bioinformatics, an array of machine learning-based peptide screening techniques has surfaced. Presently, these include neural networks, random forests, support vector machines, decision trees, Bayesian methods, and ensemble learning, among others [6; 7; 8; 9]. Neural networks adeptly capture non-linear data dependencies, while random forests provide an interpretable ensemble learning approach. Supportvector machines excel in managing high-dimensional data, aiding in the differentiation between AMPs and non-AMPs. Comprehensive research underscores the efficacy of machine learning in predicting AMPs, marking a significant improvement in screening efficiency over traditional methods [10]. The interdependence relationships within gene sequences are crucial information for distinguishing AMPs from non-AMPs in subsequent analyses. Accurately analyzing these relationships is a key factor in improving screening performance. These interdependence relationships exist not only in adjacent genomic regions but are also manifested in more distant gene sequences [11; 12]. Therefore, the critical genomic interdependence relationships of AMPs typically manifest in a small portion of the genome. It is essential to accurately identify and focus on these genomic regions. Long short-term memory network (LSTM) is a commonly used type of recurrent neural network. Compared to traditional RNNs, LSTM is better at preserving long- and short-term dependencies when handling sequence data, mitigating issues such as vanishing gradients and exploding gradients. Building on LSTM and convolutional neural networks, [PERSON] et al. proposed the CL-ACP model for screening anticancer peptides [13]. Similarly, [PERSON] proposed a model that combines LSTM and convolutional neural networks to classify antimicrobial peptides, enhancing prediction accuracy [14]. [PERSON] et al. developed the HemoNet model, which utilizes neural network methods and LSTM to predict the hemolytic activity of peptides [9]. [PERSON] proposed the Samp-pfpDeep model, which combines LSTM with deep neural networks to accurately screen short antimicrobial peptides [15]. These studies suggest the effectiveness of deep network models based on LSTM in antimicrobial peptide prediction. Compared to traditional machine learning methods, they significantly improve accuracy. However, as mentioned earlier, the crucial dependency information in the genome is vital during the screening process, and treating all dependencies equally is not prudent. In this study, we first constructed the ALSTM model by adding an attention mechanism to the LSTM model.