DBP-PSSM

DNA-binding protein predicting

DataSet

The raw sequences of PDB1075 and PDB186 datasets can be obtained from original data

Feature extraction

you must use python3 version to extract features

Local_DPP method

Using the PSI-BLAST to obtain the PSSM matrix and localdpp.py for extracting features.

python localdpp.py countpositive countnege lamudamax n

The countpositive(countnege) is the amount of positive(negative) samples.

The suggested values of lamudamax and n are 2 and 2.

When used Local_DPP method,you need to cite the following papers:

Wei, L., et al. (2017). "Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information." Information Sciences 384: 135-144.

Liu, X.-J., et al. (2018). "A Model Stacking Framework for Identifying DNA Binding Proteins by Orchestrating Multi-View Features and Classifiers." Genes 9(8): 394.

PSSM-400 method

Using the PSI-BLAST to obtain the PSSM matrix and PSSM400.py for extracting features.

When used PSSM-400 method,you need to cite the following papers:

Kumar, M., et al. (2007). "Identification of DNA-binding proteins using support vector machines and evolutionary profiles." BMC bioinformatics 8(1): 463.

Sliding window method

Using the PSI-BLAST to obtain the PSSM matrix and Sliding window.py for extracting features.

aac method

Using the raw sequence and aac.py for extracting features.

feature selection

mRMR algorithm you can download from http://penglab.janelia.org/proj/mRMR/#c++.