Patterny: A Troupe of Decipherment Helpers for Intrinsic Disorder, Low Complexity and Compositional Bias in Proteins

Area of Science:

• Proteomics
• Computational Biology
• Bioinformatics

Background:

• Intrinsically disordered regions (IDRs) and low-complexity regions (LCRs) are under-appreciated protein segments.
• Understanding the functional features of these regions is challenging for researchers.

Purpose of the Study:

• To introduce Patterny, a novel computational toolkit for protein sequence analysis.
• To aid in the cryptanalysis of protein regions, particularly IDRs and LCRs/CBRs.

Main Methods:

• Patterny comprises five modules: Blocky, Bandy, Moduley, Repeaty, and Runny.
• These modules analyze amino acid composition, banding, modularity, repetitiveness, and homopeptide content.
• The analysis compares protein regions to idealized random distributions.

Main Results:

• Patterny successfully analyzes protein sequences and identified compositional modules (CModules).
• The toolkit was applied to yeast proteome and DISPROT database samples.
• Illustrative data demonstrates Patterny's capability in feature discernment.

Conclusions:

• Patterny provides a framework for analyzing distributed protein function in regions like IDRs and LCRs.
• The toolkit can enhance hypothesis generation for under-studied protein segments.
• Patterny is available on Github to support protein cryptanalysis efforts.