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Identification of coenzyme-binding proteins with machine learning algorithms.

Yong Liu1, Cristian R Munteanu2, Zhiwei Kong3

  • 1Key Laboratory for Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, South Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, The Chinese Academy of Sciences, Changsha, Hunan, 410125, PR China; Hunan Co-Innovation Center of Animal Production Safety, CICAPS, Changsha, Hunan, 410128, PR China.

Computational Biology and Chemistry
|March 10, 2019
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method to predict coenzyme-binding proteins using Star Graph Topological Indices (SGTIs) and machine learning. This model accurately identifies proteins crucial for cellular metabolism and drug development.

Keywords:
Classification modelCoenzyme-bindingProtein sequenceRandom ForestTopological indices

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Coenzyme-binding proteins are essential for numerous cellular metabolism processes, including fatty acid biosynthesis, lipid synthesis, and enzyme regulation.
  • Accurate identification of coenzyme-binding proteins is crucial for understanding metabolic pathways and for applications in drug development.

Purpose of the Study:

  • To develop and validate a novel computational model for predicting coenzyme-binding proteins based on their primary amino acid sequences.
  • To establish a classification method utilizing Star Graph Topological Indices (SGTIs) for identifying proteins with coenzyme-binding activity.

Main Methods:

  • A dataset of 2897 proteins, including 456 with known coenzyme-binding activity, was compiled.
  • Star Graph Topological Indices (SGTIs) were calculated for peptide sequences using the Sequence to Star Network (S2SNet) application.
  • Machine learning algorithms in Weka software were employed, with a Random Forest classifier selected as the optimal model.

Main Results:

  • The Random Forest model, utilizing 3 graph-based features, achieved a high predictive performance.
  • The model demonstrated a true positive (TP) rate of 91.7% and a false positive (FP) rate of 7.6%.
  • An Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.971 indicated excellent discriminatory power.

Conclusions:

  • The proposed SGTIs-based Random Forest model is effective for predicting coenzyme-binding proteins.
  • This computational approach offers a valuable tool for future research in enzyme metabolism and drug discovery.
  • The model's high accuracy facilitates the identification of novel coenzyme-binding proteins for therapeutic and metabolic studies.