Rigapollo

Rigapollo performs protein sequence alignment using a pairwise Hidden Markov Model (HMM) integrated with Support Vector Machine (SVM) classification. It combines multiple biological features to improve alignment accuracy, particularly for highly divergent protein sequences.

Key Features:

• Pairwise HMM-SVM Integration: Constructs alignments using a hybrid framework that combines Hidden Markov Models with Support Vector Machine-based scoring.
• Customizable Feature Selection: Allows user-defined integration of biological features, including sequence conservation and secondary structure information, optimized for specific protein classes.
• Enhanced Divergent Sequence Alignment: Demonstrates improved performance on benchmark datasets, particularly for highly divergent protein sequences.

Scientific Applications:

• Comparative Protein Analysis: Enables accurate alignment of diverse protein families to support evolutionary studies and functional annotation.

Methodology:

Rigapollo constructs pairwise protein alignments by integrating selected biological features into a Hidden Markov Model framework, with alignment scoring refined through Support Vector Machine classification to optimize feature weighting and alignment accuracy.