LOMETS2

LOMETS2 performs meta-threading for template-based protein structure prediction to identify structural templates, improve remote-homology detection, and support structure-based function annotation.

Key Features:

• Meta-threading integration: Integrates multiple threading programs into a meta-threading framework for template-based protein structure prediction.
• Advanced threading programs: Incorporates state-of-the-art threading programs, notably contact-map-based approaches.
• Deep sequence search-based profile construction: Constructs sequence profiles using deep sequence search techniques to improve template matching.
• Structure-based function annotations: Integrates structure-based function annotations derived from predicted models.
• Improved template detection: Demonstrates detection of 176% more templates with TM-scores greater than 0.5 for challenging targets lacking homologous templates compared to the previous LOMETS version, based on large-scale benchmarks.

Scientific Applications:

• Template-based protein structure prediction: Identifies and aligns structural templates to generate protein models, including distant-homology templates.
• Remote-homology modeling: Improves modeling of proteins lacking close homologs through enhanced template detection.
• Structure-based function prediction: Supports functional inference by providing structure-based annotations from predicted models.
• Method benchmarking and development: Serves as a basis for large-scale benchmarking of threading and template-detection methods using TM-score metrics.

Methodology:

LOMETS2 applies meta-threading by integrating multiple state-of-the-art threading programs (including contact-map-based approaches), constructs sequence profiles via deep sequence search, and was evaluated by large-scale benchmark tests using TM-score > 0.5 as a template-detection metric.