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DNA-binding proteins (DBPs) have crucial biotic activities including DNA replication, recombination, and transcription. DBPs are highly concerned with chronic diseases and are used in the manufacturing of antibiotics and steroids. A series of predictors were established to identify DBPs. However, researchers are still working to further enhance the identification of DBPs. This research designed a novel predictor to identify DBPs more accurately. The features from the sequences are transformed by F-PSSM (Filtered position-specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), and R-PSSM (Reduced position-specific scoring matrix). To eliminate the noisy attributes, we extended DWT (discrete wavelet transform) to F-PSSM, PSSM-DPC, and R-PSSM and introduced three novel descriptors, namely, F-PSSM-DWT, PSSM-DPC-DWT, and R-PSSM-DWT. Onward, the training of the four models were performed using LiXGB (Light eXtreme gradient boosting), XGB (eXtreme gradient boosting, ERT (extremely randomized trees), and Adaboost. LiXGB with R-PSSM-DWT has attained 6.55% higher accuracy on training and 5.93% on testing dataset than the best existing predictors. The results reveal the excellent performance of our novel predictor over the past studies. DBP-iDWT would be fruitful for establishing more operative therapeutic strategies for fatal disease treatment. [ABSTRACT FROM AUTHOR]

DNA-binding protein (DBPs) has many crucial functions like DNA transcription, recombination, and replication. DBPs are highly associated with AIDS/HIV, cancer, and asthma, while other types of DBPs are the active ingredients in the designing of anti-inflammatory, steroids, and antibiotics. Many approaches were introduced for DBPs detection. It is still highly desirable to establish predictor with high precision. This research encodes different views of features by several PSSM-based feature descriptors including EDF-PSSM (evolutionary difference formula-based position-specific scoring matrix), FPSSM (Filtered position-specific scoring matrix), PSSM-MAB (position-specific scoring matrix-based multiple average blocks), KSB-PSSM (k-separated bigram position-specific scoring matrix), PSSM-AAC (position-specific scoring matrix-amino acid composition), RPSSM (Reduced position-specific scoring matrix), and PSSM-DPC (position-specific scoring matrix-dipeptide composition). The noisy and redundant features were eliminated by two compression techniques named DWT (discrete wavelet transform) and DCT (discrete cosine transform) The obtained features were concatenated and provided to XGBoost (eXtreme Gradient Boosting) and ERT (Extremely Randomized Trees) classifiers for training the model. The XGBoost-based model (iDBP-PMD) acquired the highest 2.72% and 1.24% accuracies on the training and testing datasets, respectively. The results confirmed the superiority of current study over previous approaches. • Designed a novel predictor, iDBP-PMD for discrimination of DNA-binding proteins. • The local & global discriminative information was discovered by PSSM-based different feature methods. • XGBoost was used for training model and prediction. • Our novel predictor achieved the highest success rate. [ABSTRACT FROM AUTHOR]