MAPPIS：Efficient and Fast Multi-dimension Attention Protein-Protein Interaction Site Prediction

Introduction

(a) Input a protein's 3D structure and its amino acid sequence. Extract sequence information and structural information.
(b) Construct a directed weighted graph, where each node represents a residue, each edge represents the connection between nodes, and the weight indicates the strength of the connection.
(c) Apply a channel attention mechanism to assign attention weights to features propagated from different layers (hop distances). The SE-Aggregation (Squeeze-and-Excitation) module is used to aggregate neighbor information across different hops and learn their corresponding importance weights 𝑤1,𝑤2,...,𝑤8, which are then combined through a weighted sum.
(d) Employ a graph convolutional neural network (GCN) with the incorporation of Initial Residual Connections and Identity Mapping.
(e) Overall Workflow of MAPPIS

Dependency

python                    3.10.18
dgl                       2.2.1
freesasa                  2.2.1
matplotlib                3.10.0
numpy                     2.1.2
pandas                    2.3.1
scikit-learn              1.6.1
torch                     2.3.0
torch-cluster             1.6.3
torch-geometric           2.5.0
torch-scatter             2.1.2
torch-sparse              0.6.18
torch-spline-conv         1.2.2
torchaudio                2.3.0
torchdata                 0.8.0
torchvision               0.18.0

Train and Test

Train

start training if use AttPreSite_model.py

python AttPreSite_model.py

output

./Model/fold1_best_model.pkl
./Model/fold2_best_model.pkl
...
./Model/full_model_30.pkl

if use AttPreSite-Ligand_model.py

python AttPreSite-Ligand_model.py --ligand RNA --trans

output

./Model/fold1_best_model.pkl
./Model/fold2_best_model.pkl
...
./Model/full_model_30.pkl

Test

start testing if use AttPreSite_model.py

python AttPreSite_model.py

output

Test_60:
Test loss:  0.35188861563801765
Test binary acc:  0.8532410225197808
Test precision: 0.5292
Test recall:  0.6375903614457832
Test f1:  0.5783606557377049
Test AUC:  0.8730095882672436
Test AUPRC:  0.5954371390159193
Test mcc:  0.49356660367384997
Threshold:  0.29

Test_315-28:
Test loss:  0.3352209431109528
Test binary acc:  0.8613687557970054
Test precision: 0.50909428359317
Test recall:  0.6404389446649544
Test f1:  0.5672629510908903
Test AUC:  0.881639916372179
Test AUPRC:  0.5846846871636862
Test mcc:  0.4905450545869246
Threshold:  0.4

BTest_31:
Test loss:  0.3097736287501551
Test binary acc:  0.8623839244938347
Test precision: 0.48627450980392156
Test recall:  0.713463751438435
Test f1:  0.5783582089552238
Test AUC:  0.8915595663497061
Test AUPRC:  0.6005335030075699
Test mcc:  0.5127453818159148
Threshold:  0.17

BTest_31-6:
Test loss:  0.29347263753414154
Test binary acc:  0.8743178717598908
Test precision: 0.5009505703422054
Test recall:  0.713125845737483
Test f1:  0.588498045784478
Test AUC:  0.8971802369715172
Test AUPRC:  0.60762077430879
Test mcc:  0.5282226538371553
Threshold:  0.17

UBtest_31-6:
Test loss:  0.3668563061952591
Test binary acc:  0.8299814094980564
Test precision: 0.36176194939081535
Test recall:  0.5428973277074542
Test f1:  0.4341957255343082
Test AUC:  0.8118437397506826
Test AUPRC:  0.4116572608571926
Test mcc:  0.3485157642515198
Threshold:  0.18

if use AttPreSite-Ligand_model.py

python AttPreSite-Ligand_model.py --ligand RNA --trans

output

DNA-Test_129:
Test loss:  0.17306546022205851
Test binary acc:  0.9235505797680927
Test precision: 0.41608765366114375
Test recall:  0.6950892857142857
Test f1:  0.5205616850551655
Test AUC:  0.932489375569505
Test AUPRC:  0.5233944364174145
Test mcc:  0.5006401050722077
Threshold:  0.32

Visualization Results

This figure shows the comparison of binding site prediction performance between MAPPIS and eight existing methods on the same protein sample. Green regions represent non-binding residues; Red regions represent predicted binding residues; Yellow regions represent predicted binding residues with disagreement among methods; Purple regions represent true binding residues.

This figure shows multiple visualization styles and rotational views of the 3D structure of the protein.