Journal of Proteomics & Bioinformatics
Open Access

Nouman Rasool

Publications

• Research Article
  Prediction of Protein Solubility using Primary Structure Compositional Features: A Machine Learning Perspective
  Author(s): Nouman Rasool, Waqar Hussain and Sajid MahmoodNouman Rasool, Waqar Hussain and Sajid Mahmood
  
  It is a recurring limiting factor to obtain sufficient concentrations of soluble proteins using in vitro methodologies. Solubility is an independent characteristic of a protein which can be determined using amino acid compositions under specific experimental conditions. The present study aims at the prediction of protein solubility by adapting machine learning based approaches using the primary structure information. The features involve amino acid compositional features as well as the physiochemical properties of the amino acids i.e. canonical value, hydrophobicity, solubility index and solubility score. For a dataset of 6372 protein sequences (4850 soluble protein sequences and 1522 insoluble protein sequences), all the four features were calculated. Using the calculated values, four different prediction models were developed based on Multilayer Perceptron (MLP), Random For.. View More»
  DOI: 10.4172/jpb.1000458

  Abstract PDF

Relevant Topics