MODAN (https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00090g) is a multi-objective Bayesian framework for the design of antimicrobial peptides (AMPs) considering various non-proteinogenic amino acids (NPAAs) containing α,α-disubstituted NPAAs and side-chain stapling.
Python: 3.9.12
pip install rdkit-pypi==2021.3.5 scikit-learn==1.1.0 pandas==1.4.2 matplotlib==3.5.2 pyyaml==6.0 physbo==1.0.1 mordred==1.2.0 openpyxl==3.0.10git clone git@github.com:ycu-iil/MODAN.git
cd MODANThe Recommndataion of options are described in the "Advaced usage" section. For details, please refer to a sample file (config/setting.yaml).
python main.py -c config/setting_paper.yaml| Option | Description |
|---|---|
data |
You can specify a dataset file. There are two default datasets, Dataset_MODAN_initial.xlsx and Dataset_MODAN_initial_round1.xlsx, in the "data" directory. |
AA_dict_update |
You can register various amino acids used in MODAN. The items to be registered are a code, a name, and a type of amino acid used in MODAN. For the type of amino acid, there are three types that can be registered. The first type is a as an α-amino acid. The second type is a_a as an α,α-disubstituted α-amino acid. The third type is ring as an α,α-disubstituted α-amino acid whose side chain is a cyclic structure. For α-aminoisobutyric acid, the code, the name, and the type are registered as U, Aib, and a_a, respectively. |
AA_joint_update |
You can register the code and SMILES strings of the side chain of the amino acid registered in AA_dict_update. For 'a_a', you need to register two types of SMILES strings of the side chain of the amino acid as a list format. For α-aminoisobutyric acid, the code and the two types of SMILES strings are registered as U, C, and C, respectively. |
base_atom |
You can select substituted atoms of α carbon to use Skip-7 representation. If you select phosphorus or sulfur, you set P and S, respectively. |
Morgan_descriptor_dimension |
You can specify a descriptor dimension of a Morgan fingerprint. |
fold_n |
You can select a fold number to use cross-validation to validate surrogate models. |
value_log |
You can choose whether or not to handle activity values in the common logarithms. If you choose the common logarithms, you set True. |
base_index |
You can specify the index number of a lead sequence in data. |
mutatable_AA_list |
You can specify the code of amino acids prepared for the substitution. |
mutation_num |
You can specify the number of times a lead sequence is converted into amino acids prepared for the substitution. |
smiles_select |
You can choose whether or not to select a SMILES representation used to construct a surrogate model yourself. If you select yourself, you set True. |
fingerprint_select |
You can choose whether or not to select a molecular fingerprint used to construct a surrogate model yourself. If you select yourself, you set True. |
target_list |
You can select a predicted item, a criterion, a SMILES representation, and a molecular fingerprint for each of the predicted items. For a criterion, you select the upper or lower bound as <= or >= besides a value. For a SMILES representation, if you select a standard representation or a Skip-7 representation, you set original and smiles_repP_skip7, respectively. For a molecular fingerprint, if you select a MACCS fingerprint, a Morgan fingerprint (maximal radii is two), or a Morgan fingerprint (maximal radii is four), you set MACCS, Morgan_r2_count, and Morgan_r4_count, respectively. You do not need to select a SMILES representation and a molecular fingerprint, if you set False in smiles_select and fingerprint_select, respectively. |
result_type |
You can choose how a result is displayed. If you want to display a peptide with the highest total probability of improvement score including each amino acid prepared for the substitution, you set Each_AA. |
display_number |
You can choose the number to display a recommended peptide. |
・Kei terayama (terayama@yokohama-cu.ac.jp)