Meta-BASIC

Meta-BASIC detects distant homologous relationships between proteins by comparing consensus alignments of meta-profiles that integrate sequence positional variability and predicted secondary structure to reveal remote similarities.

Key Features:

• Consensus meta-profile alignment: Uses consensus alignments of meta-profiles to enhance detection of remote homology.
• Integration of sequence positional variability and secondary structure: Combines sequence positional variability with predicted secondary structure in profile comparisons.
• Profile and structure-integrated comparisons: Compares sequence profiles combined with secondary structural predictions during similarity assessment.
• Multiple scoring systems and alignment algorithms: Employs multiple scoring systems and alignment algorithms for robust comparison of profiles and structural predictions.
• Detection without known tertiary structure: Identifies very distant evolutionary relationships even when the reference protein's tertiary structure is unknown.
• High-throughput capability: Designed to support large-scale analyses for protein family annotation.
• Application to PfamA DUFs: Has been applied to 860 PfamA protein families classified as domains of unknown function (DUF), producing structure–function assignments including predictions that DUF271 is a nucleotide-diphospho-sugar transferase and DUF431 is an alpha/beta-knot SAM-dependent RNA methyltransferase.

Scientific Applications:

• Remote homology detection: Identification of distant homologous relationships between proteins, including cases lacking solved tertiary structures.
• Fold-recognition complementarity: Serves as a complementary approach to fold-recognition and meta-predictors that rely on structural model comparisons.
• Function hypothesis generation for DUFs: Generation of structure–function hypotheses for domains of unknown function, exemplified by assignments for DUF271 and DUF431 following analysis of 860 PfamA DUFs.

Methodology:

Performs consensus alignments of meta-profiles and compares sequence profiles combined with predicted secondary structure using multiple scoring systems and alignment algorithms while integrating sequence positional variability.