Protein Secondary Structure Prediction by capturing local patterns from attention map

(a) Whole Architecture (b) Transformer encoder layer (c) 2D Segment Detector

Environment

GeForce RTX 3090

• python == 3.8.12
• pytorch == 1.9.0
• cudatoolkit == 11.1.1
• argparse

Evaluation

• For model trained with NetsurfP-2.0 DSSP8 dataset

python evaluation.py --train_data_type Netsurf --str_type dssp8 --model MASKSecondary_Netsurf_dssp8_T5_emb_dropout_0.3_16_RPE_seg_feature_block3.jit.pt

• For model trained with Proteinnet DSSP8 dataset

python evaluation.py --train_data_type Proteinnet --str_type dssp8 --model MASKSecondary_Proteinnet_dssp8_T5_emb_dropout_0.3_16_RPE_seg_feature_block3.jit.pt

• For model trained with NetsurfP-2.0 DSSP3 dataset

python evaluation.py --train_data_type Netsurf --str_type dssp3 --model MASKSecondary_Netsurf_dssp3_T5_emb_dropout_0.3_16_RPE_seg_feature_block3.jit.pt

• For model trained with Proteinnet DSSP3 dataset

python evaluation.py --train_data_type Proteinnet --str_type dssp3 --model MASKSecondary_Proteinnet_dssp3_T5_emb_dropout_0.3_16_RPE_seg_feature_block3.jit.pt