Additional file 2 of HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes
Creators: name: Li, Yu
name: Xu, Zeling
name: Han, Wenkai
name: Cao, Huiluo
name: Umarov, Ramzan
name: Yan, Aixin
name: Fan, Ming
name: Chen, Huan
name: Duarte, Carlos M.
name: Li, Lihua
name: Ho, Pak-Leung
name: Gao, Xin
Publisher: name: figshare
Year: 2021
Subjects: subject: Microbiology
subject: FOS: Biological sciences
subject: Genetics
subject: Environmental Sciences not elsewhere classified
subject: Chemical Sciences not elsewhere classified
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Figure S1: ROC curve comparison. For ARG/Non-ARG prediction, we set different thresholds for the last layer of the HMD-ARG model and different parameters for DeepARG, drawing the above ROC curves. The figure suggests that HMD-ARG is more robust than DeepARG. Figure S2: Human intestinal microbiota prediction. After removing overlaps between HMD-ARG training data and the Mustard database, we trained a new model and applied the model to this human gut dataset. In the figure, we show the number of correctly predicted ARGs, by HMD-ARG and DeepARG, across different classes in the Mustard dataset. Compared to DeepARG, HMD-ARG can recover much more correct ARGs in more diverse classes, which suggests the robustness and the sensitivity of the proposed method. Figure S3: Growth curves of E. coli host with the expression of the predicted novel ARGs that inactivate antibiotics in the presence of antibiotics. a. The growth curve of E. coli under the presence of Ampicillins (50 μg/ml) b. The same to a, while removes the AXX01_04100, which is a predicted novel ARGs shares high similarity compared with known ones. c. The growth curve of E. coli under the presence of Carbenicillin (10 μg/ml) d. The same to c, while removes the AXX01_04100, which is a predicted novel ARG that shares high similarity compared with known ones. Figure S4: Correlation between saliency map and PSSM. a. For each position in a sequence, which is shown as the columns, we mutated the amino acid to the other amino acids, which are shown as the rows, and fed the mutated sequence to HMD-ARG model, determining the probability of the sequence being an ARG and filling in the value into the corresponding position in the saliency map with the probability. The figure shows the averaged saliency map of those sequences, which can be aligned to AFB78806. b. The figure shows the position-specific scoring matrix (PSSM) of AFB78806, which indicates the evolutionary information of that ARG. We can find a clear correlation between a) and b), especially for row c (horizontal rectangle) and column 226 (vertical rectangle). This correlation suggests that although we only used the protein sequence as input, without resorting to sequence alignment, HMD-ARG can capture the evolutional information of ARG sequences, which demonstrates the effectiveness of the proposed method. Figure S5: The saliency map built with PSSM. The PSSM predicted conserved sites and the corresponding sequence logo from 319 to 393 in AXX01_04100. Table S1: The list of ARGs predicted in the study and validated using heterogenous expression in E. coli host. Table S2: Primers used for constructing overexpression plasmids.