Computational analysis of position-dependent disorder content in DisProt database

Area of Science:

• Bioinformatics
• Structural Biology
• Computational Biology

Background:

• Intrinsically disordered proteins (IDPs) play crucial roles in cellular processes.
• Understanding the distribution and characteristics of disordered regions is vital for protein function prediction.

Purpose of the Study:

• To analyze the positional distribution of disordered residues within protein chains.
• To investigate the relationship between amino acid properties (hydropathy, molecular weight) and disorder content.
• To develop a new scale for amino acid disorder propensity.

Main Methods:

• Bioinformatic analysis of protein disorder from the DisProt database.
• Division of protein chains into N-terminal, C-terminal (30 amino acids each), and middle regions.
• Analysis of disorder percentage for each of the 20 amino acids based on position and properties.

Main Results:

• Terminal regions exhibit a higher percentage of disordered residues (17%) compared to the overall protein sequence (11%).
• Disorder propensity varies across amino acids and is position-dependent, with middle regions showing lower disorder.
• A novel amino acid scale (CIFWMLYHRNVTAGQDSKEP) was derived, showing big hydrophobic AAs are less disordered and small hydrophilic AAs are more disordered.

Conclusions:

• Protein disorder is not uniformly distributed, with a preference for terminal regions.
• Amino acid properties significantly influence disorder propensity, particularly in different protein regions.
• Findings can inform the development and refinement of computational tools for predicting protein disorder.