Comments (3)
Sure. I used PPDbench directly from previous study. I don't understand "he SSpro can only predict peptide longer than 30 aa". Why? I didn't have such length limitations. I think this accuracy is weired and I'll upload my PPDbench dataset, inference script and result log here this week. I hope this can help you solve the problem.
from camp.
Sure. I used PPDbench directly from previous study. I don't understand "he SSpro can only predict peptide longer than 30 aa". Why? I didn't have such length limitations. I think this accuracy is weired and I'll upload my PPDbench dataset, inference script and result log here this week. I hope this can help you solve the problem.
OK. Thank you so much.
from camp.
Sure. I used PPDbench directly from previous study. I don't understand "he SSpro can only predict peptide longer than 30 aa". Why? I didn't have such length limitations. I think this accuracy is weired and I'll upload my PPDbench dataset, inference script and result log here this week. I hope this can help you solve the problem.
OK. Thank you so much.
Hi, I hope the discussion yesterday can solve your concerns. There are several key points may cause the difference: 1. the SS feature you generated are derived from different version of SCRATCH, after comparing we noticed that there are many differences; 2. Although we used the same PDDbench list, we used different sampling methods to generate negatives. 3. CAMP adopts UniProt sequences instead of PDB fasta sequences. 4. The Intrinsic Disorder values are different. To solve these problems, I already send you my PDDbench data (including negatives), my inference results and evaluation scripts. Hope these stuff can help you.
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Related Issues (20)
- Could you check PLIPv2.2.2 output vs v1.4.2? current codes only for PLIPv1.4.2? HOT 1
- About blast database HOT 1
- Questions about creating "query_peptide.fasta" and "target_peptide.fasta" HOT 3
- step2_pepBDB_pep_bindingsites.py 'df_part_all' is not defined HOT 1
- dataset HOT 2
- error in preprocessing features HOT 3
- peptide_dense_feature_dict generated by step3 has only two features per AA whereas given example data has three features per AA
- interpretion of results HOT 1
- error in CAMP_BS.h5 modelling HOT 5
- We couldn't run program HOT 3
- Unknown loss function: conditional_BCE; predict_CAMP.py HOT 2
- Request for Guidance in Step 1 to Step 3 of step2_pepBDB_pep_bindingsites.py
- can not load 'CAMP_BS.h5' successfully HOT 4
- problem unpickling CAMP_pytorch files HOT 4
- analyzed file generated by PLIP HOT 1
- Step 4 of step1_pdb_process.py
- "query_peptide.fasta" and "target_peptide.fasta" HOT 2
- How to generate 'pep_concat_seq' and 'prot_concat_seq' in my own camp input data
- The peptide-binding residue prediction in CAMP_train_CV.py
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