HHalign-Kbest

HHalign-Kbest improves protein structure prediction by generating and evaluating multiple suboptimal HMM-HMM alignments (k-best) derived from HHsearch to identify alignments that produce higher-quality comparative models for low sequence-identity targets.

Key Features:

• HMM-HMM alignment (HHsearch): Uses HHsearch's hidden Markov model–hidden Markov model alignment method for remote homology detection and template-target alignment.
• K-best suboptimal alignments: Systematically generates multiple suboptimal HMM-HMM alignments rather than relying solely on the single optimal alignment.
• Directed acyclic graph implementation: Implements a directed acyclic graph representation to manage memory usage for large proteins.
• Model generation per alignment: Produces corresponding structural models for each suboptimal alignment for downstream evaluation.
• Model quality evaluation (Qmean): Ranks and selects alignments based on Qmean scores calculated for the generated structural models.
• Benchmarking on SCOP30: Validated on 420 targets from the SCOP30 database with reported TM-score improvements across HHsearch probability ranges.
• Performance in low identity regimes: Targets scenarios with sequence identity below 35% and the twilight zone around 20% where alignment errors critically affect structural prediction.

Scientific Applications:

• Template-based protein structure prediction: Enhances comparative modeling for remote homologs with low target-template sequence identity.
• Alignment selection and ranking: Identifies alternate alignments that yield higher-quality structural models via Qmean-based evaluation.
• Method benchmarking and development: Provides a framework and benchmark results (SCOP30, TM-score) for evaluating alignment and modeling strategies.

Methodology:

HHalign-Kbest generates k-best HMM-HMM alignments using HHsearch, implements a directed acyclic graph for memory efficiency, builds corresponding structural models for each alignment, and evaluates model quality using Qmean with TM-score benchmarking on 420 SCOP30 targets across HHsearch probabilities 20–99%.