LambdaPP

LambdaPP predicts protein- and residue-level phenotypes from amino acid sequences using the pLM ProtT5 model and ColabFold structure prediction.

Key Features:

• Protein- and residue-level phenotype prediction: Predicts Gene Ontology terms, subcellular localization, and residue-specific attributes including metal ion binding, small molecule interactions, nucleotide associations, conservation status, intrinsic disorder, secondary structure, transmembrane segments (alpha-helical and beta-barrel), signal peptides, and variant effects.
• Rapid prediction: Delivers protein- and residue-level phenotype predictions within seconds.
• 3D structure prediction: Produces protein 3D structures via ColabFold using MMseqs2 multiple sequence alignments, typically in minutes.
• AI backbone: Uses the protein language model ProtT5 to underpin all feature prediction methods except structure prediction.
• High-throughput/local execution: Methods can be executed locally via the bio-embeddings Python package or the Docker image ghcr.io/bioembeddings/bio_embeddings for integration into computational workflows.

Scientific Applications:

• Experimental molecular biology: Provides residue- and protein-level annotations to inform experimental design and interpretation of molecular assays.
• Computational molecular biology and functional genomics: Supports large-scale annotation of protein function, conservation, and variant effects across genomes and proteomes.
• Drug discovery and systems biology: Supplies structural, localization, and interaction-related predictions useful for target characterization and systems-level modeling.

Methodology:

Feature predictions are generated using the pLM ProtT5 model; structure predictions are generated with ColabFold using MMseqs2 multiple sequence alignments; methods are available for local execution via the bio-embeddings Python package or the Docker image ghcr.io/bioembeddings/bio_embeddings.