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Updated: Aug 26, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet

Farman Ali¹, Omar Barukab², Ajay B Gadicha³

¹Department of Elementary and Secondary Education, Peshawar, Khyber Pakhtunkhwa, Pakistan.

Computational Intelligence and Neuroscience

|October 10, 2022

Summary

This summary is machine-generated.

A new predictor accurately identifies DNA-binding proteins (DBPs) involved in critical cellular processes and diseases. This method enhances DBP identification, potentially leading to improved therapeutic strategies for fatal diseases.

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Published on: January 26, 2024

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

DNA-binding proteins (DBPs) are essential for DNA replication, recombination, and transcription.
DBPs play roles in chronic diseases and are targets for antibiotic and steroid production.
Existing DBP identification methods require further enhancement for accuracy.

Purpose of the Study:

To design and validate a novel, highly accurate predictor for identifying DNA-binding proteins.
To improve upon existing methods for DBP identification using advanced feature engineering and machine learning.

Main Methods:

Feature extraction using Filtered position-specific scoring matrix (F-PSSM), Position specific scoring matrix-dipeptide composition (PSSM-DPC), and Reduced position-specific scoring matrix (R-PSSM).
Application of discrete wavelet transform (DWT) to F-PSSM, PSSM-DPC, and R-PSSM to create novel descriptors: F-PSSM-DWT, PSSM-DPC-DWT, and R-PSSM-DWT.
Training and evaluation of machine learning models including Light eXtreme gradient boosting (LiXGB), eXtreme gradient boosting (XGB), extremely randomized trees (ERT), and Adaboost.

Main Results:

The LiXGB model combined with the R-PSSM-DWT descriptor achieved superior performance.
This novel predictor demonstrated a 6.55% increase in training accuracy and a 5.93% increase in testing accuracy compared to existing methods.
The developed predictor, DBP-iDWT, significantly outperforms previous predictors.

Conclusions:

The novel predictor exhibits excellent performance, surpassing existing methods for DBP identification.
The DBP-iDWT predictor holds promise for developing more effective therapeutic strategies against fatal diseases.
This research contributes to advancing DBP identification techniques in bioinformatics and molecular biology.