# Antimicrobial Peptide Screening from Microbial Genomes in Sludge Based on Deep Learning
## 3 Experiments
### Experimental Process
The PCR conditions included an initial denaturation at 95 \({}^{\circ}\)C for 3 min, followed by 10 cycles of 95 \({}^{\circ}\)C for 30 s (denaturation), 55 \({}^{\circ}\)C for 30 s (annealing), and 72 \({}^{\circ}\)C for 30 s (extension), with a final extension at 72 \({}^{\circ}\)C for 5 min. Size distribution was assessed using an Agilent Bioanalyzer(Beijing Novogene Technology Co., Ltd., Beijing, China), and quantification was performed using Qubit to ensure an average size of approximately 300 bp and minimize adapter dimers. The concentration was adjusted based on the qPCR results for pooling. Finally, a mixing of equal molar amounts was performed, and the final concentration was adjusted to 4 nM. Raw sequencing files in FASTQ format were obtained on the Illumina sequencing platform. The experimental materials, including reagents and kits, were all supplied by Beijing Jingli Science and Technology Co., Ltd. (Beijing, China). Precision sequencing was entrusted to Beijing Novogene Technology Co., Ltd. (Beijing, China). The company was responsible for handling the sequencing process, and the specific materials used for sequencing were internally provided by the company. The getorf function in EMBOSS was used to search for open reading frames in the DNA sequence and predict protein sequences. BLASTp was employed to remove non-protein family sequences. Subsequently, in the Protein Digestion Simulator software (AMBER20), pepsin 1 and 2, and trypsin were selected for simulated hydrolysis. The resulting peptide segments from simulated hydrolysis were merged with the original protein sequencesto create a new dataset. CD-Hit was then used to remove known antimicrobial peptide sequences and duplicate sequences. The amino acid sequences were digitally encoded, mapping each amino acid letter to a unique number. Subsequently, a length check was performed on the generated numeric encoding string. If it was less than 300 numbers, \"0\" was added for padding to ensure the encoded sequence length was 300. Finally, positive and negative sample labels were added to the sequences. The obtained sequences for screening totaled 5763. Step 3: AMP screening based on the combined deep network. Use the to-be-screened sequences as input to obtain the output of the trained model from Step 1, which serves as the screening result based on the combined deep learning model. Step 4: Validate the screening results of the deep model using molecular docking and molecular dynamics simulations. Using AutoDock (1.5.7), perform docking of antimicrobial peptides based on amino acid sequences and structure them in ChemDraw (20.0) and Chem3D (20.0). Before setting the docking parameters, remove excess ions and water molecules. After docking, select the top three models with the highest scores. Further validation is conducted through dynamic simulations using Discovery Studio (2019). Finally, RMSD and RMSF plots are used to assess the interaction between the screened peptides and potential antibiotic target proteins, validating the accuracy of the screening results from the combined deep model. The code and raw data have been uploaded to GitHub, and the website is: [[https://github.com/LuckyCoder2023/Antimicrobial-Peptides-Screening-from-Microbial-Genomes-in-Sludge-Based-on-Deep-Learning.git](https://github.com/LuckyCoder2023/Antimicrobial-Peptides-Screening-from-Microbial-Genomes-in-Sludge-Based-on-Deep-Learning.git)]([https://github.com/LuckyCoder2023/Antimicrobial-Peptides-Screening-from-Microbial-Genomes-in-Sludge-Based-on-Deep-Learning.git](https://github.com/LuckyCoder2023/Antimicrobial-Peptides-Screening-from-Microbial-Genomes-in-Sludge-Based-on-Deep-Learning.git)) (accessed on 10 July 2023).