TwinCons

TwinCons analyzes composite multiple sequence alignments to quantify position-specific transformation costs between predefined groups and detect conserved, variable, and signature positions in proteins and nucleic acids.

Key Features:

• Composite alignment input: Accepts composite MSAs that include predefined groups for comparative analysis.
• Position-specific transformation cost calculation: Mathematically calculates the cost associated with transforming one group into another at each alignment position.
• Detection of conserved, variable, and signature positions: Distinguishes conserved positions (similar across all sequences), variable positions (differing among sequences), and signature positions (conserved within groups but differing between them).
• Segment identification: Automatically identifies continuous characteristic stretches or segments within alignments.
• Single score representation: Provides a unified per-position score representing conserved, variable, and signature characteristics.
• Structural mapping and visualization: Facilitates mapping scores onto structures and highlights alternative sequences that maintain conserved structural features.

Scientific Applications:

• Functional analysis of rRNA and ribosomal proteins: Detects highly similar segments between proteins involved in translation and transcription and identifies conserved residues within functionally important regions of rRNA, with signature positions distributed across the entire rRNA structure.
• Combined sequence and structural analysis: Evaluates both nucleic acid and protein alignments for integrated sequence–structure investigations of signatures and conservation in rRNA and rProteins.
• Co-evolution studies: Reveals deep co-evolutionary relationships between rRNA and rProteins, including strong sequence conservation signals between bacterial and archaeal rProteins related by circular permutation and their colocalization with conserved rRNA regions.

Methodology:

Computes position-specific transformation costs between predefined groups within composite MSAs, automatically detects continuous characteristic segments, and outputs a unified per-position score.