PRALINE

PRALINE performs multiple sequence alignment of protein sequences by integrating position-specific profiles, secondary structure predictions, and iterative progressive alignment strategies to improve alignment accuracy for structural and functional analyses.

Key Features:

• Iterative Progressive Alignment: PRALINE employs an iterative progressive alignment strategy that allows multiple rounds of refinement to improve alignment quality.
• Profile Pre-processing: It applies global and local profile pre-processing using position-specific profiles derived from database searches to capture evolutionary information between distant sequences.
• Secondary Structure Integration: It integrates secondary structure predictions derived from multiple-sequence information into the alignment process to enhance alignment of structurally relevant residues.
• Reliability Measures: PRALINE computes residue-specific reliability indices for aligned positions that can be used iteratively to assess and optimize alignment confidence.
• Advanced Weighting Schemes: It implements three weighting schemes—global profile pre-processing, local pre-processing on non-overlapping regions, and local–global alignment based on double dynamic programming (DDP)—that contribute to reliability indices.
• Dendrogram Representation: The grouping of sequences used for alignment is represented as a dendrogram to reflect sequence relationships and evolutionary distances.
• Customizable Visualization: PRALINE provides visualization color schemes based on residue type, conservation, reliability, hydrophobicity, and secondary structure and supports custom color schemes to highlight specific amino acids.

Scientific Applications:

• Local Structure Prediction: High-accuracy alignments support prediction of local protein structural elements.
• Comparative Modeling and Threading: Precise alignments produced by PRALINE are applicable to comparative modeling and threading for structural biology.
• Identification of Functionally Important Regions: Enhanced alignment sensitivity aids identification of structurally and functionally significant regions within proteins.

Methodology:

PRALINE combines sequence similarity searches and position-specific profiles from database searches with global and local profile pre-processing, integrates secondary structure predictions, applies an iterative progressive alignment strategy, uses hierarchical/double dynamic programming (DDP) and multiple weighting schemes, and computes residue-specific reliability indices.