# Antimicrobial Peptide Screening from Microbial Genomes in Sludge Based on Deep Learning
## 3 Experiments  
### Experimental Process  
In the experimental identification of novel antimicrobial peptides (AMPs) from sludge microbial communities using deep learning technology, the process includes training a combined deep network, constructing a microbial genome dataset for screening based on sludge samples, utilizing the combined deep network for AMP screening, and finally, validating the screening results of the deep model through molecular docking and dynamic simulations. Specifically, the process comprises the following four steps (refer to Figure 2):  
Figure 1: The structure of the combined AMP screening models. Gene sequences are encoded and predicted using both LSTM and ALSTM. The sigmoid function serves as the activation function, the linear function is employed, and the “AND” operation is used for element-wise conjunction to obtain the results. Figure 2: The experimental workflow for deep learning and biological validation. A large dataset of positive and negative samples was collected and divided into training and test sets. An attention-based LSTM (ALSTM) model was developed for processing sequence data to predict the antimicrobial activity of peptides. Post-training, the model’s efficacy was validated with the test set, and molecular docking simulations further verified the interactions between the peptides and the target proteins. Step 1: Following the method provided in Section 2, train the combined deep network using the dataset and evaluate the effectiveness of model training using metrics such as accuracy, precision, recall, and \(F1\) score. The calculation formulas are as follows:  
\[acc=\frac{TP+TN}{TP+TN+FN+FP} \tag{14}\]  
\[pre=\frac{TP}{TP+FP} \tag{15}\]  
\[recall=\frac{TP}{TP+FP} \tag{16}\]  
\[F1=\frac{pre\text{-}recall}{pre+recall} \tag{17}\]  
where \(TP\) represents True Positive, \(TN\) represents True Negative, \(FP\) represents False Positive, and \(FN\) represents False Negative. Step 2: Construction of microbial genome dataset based on sludge samples. A 10 L sludge sample was collected from Shandong Yingxuan Industrial Co., Ltd., (Shandong, China) Sterilized containers and sampling tools were used during sampling, avoiding direct contact with large chunks of sludge as much as possible. After sampling, the sample container was sealed promptly and transferred to the laboratory in the shortest time. To prevent the loss of microbial activity, the samples were stored under low-temperature and light-avoiding conditions. The OMEGA kit (Beijing Kinglith Technology Co., Ltd., Beijing, China) was employed to extract DNA from the samples. NEBNext dsDNA Fragmentase (Beijing Kinglith Technology Co., Ltd., Beijing, China) was used to fragmentize the DNA into approximately 300 base pairs through enzymatic cleavage at 37 \({}^{\circ}\)C for 30 min. A mixture of T4 DNA polymerase, T4 polynucleotide kinase, and Klenow DNA polymerase was incubated at 20 \({}^{\circ}\)C for 30 min to smooth and phosphorylate the ends of the fragments. Klenow fragment (3\({}^{\prime}\) to 5\({}^{\prime}\) exo-) was then used to incubate at 37 \({}^{\circ}\)C for 30 min, adding an \({}^{\prime}\)A' base at the 3\({}^{\prime}\) end of all fragments. Subsequently, Illumina TruSeq DNA adapters with \({}^{\prime}\)T' overhangs and T4 DNA ligase were used to connect the adapters at room temperature for 15 min. SPRI beads were employed to select fragments of 300-500 bp based on bead-to-sample ratios of 0.8X and 0.1X, removing shorter fragments and adapter dimers. The DNA fragments with connected adapters were PCR-amplified using high-fidelity DNA polymerase, dNTPs, MgCl2, and PCR buffer, with a forward primer (5\({}^{\prime}\)-AATGATACGGCGCACCGCAGATCTACAC-3\({}^{\prime}\)) and a reverse primer (5\({}^{\prime}\)-CAAGCAGAAGACGCATACGAGAT-3\({}^{\prime}\)).