LambdaPP: Fast and accessible protein-specific phenotype predictions

Area of Science:

• Computational Biology
• Bioinformatics
• Artificial Intelligence in Molecular Biology

Background:

• The first AI protein prediction server, PredictProtein, launched in 1992.
• Accurate AI solutions for protein prediction are transforming molecular biology.
• LambdaPP aims to advance protein prediction capabilities.

Purpose of the Study:

• To introduce LambdaPP, a novel webserver for rapid and comprehensive protein analysis.
• To provide accessible visualizations of protein 3D structure and residue-level predictions.
• To offer predictions for protein function, including Gene Ontology and subcellular localization.

Main Methods:

• LambdaPP utilizes ColabFold and MMseqs2 for protein structure prediction.
• Other predictions leverage the pLM ProtT5 model.
• Input is a protein sequence; output includes 3D structure, protein-level, and residue-level predictions.

Main Results:

• LambdaPP provides near-instantaneous predictions for protein structure and function.
• It offers detailed residue-level insights like binding sites, conservation, and disorder.
• Accessible visualizations and downloadable results are available.

Conclusions:

• LambdaPP offers a significant advancement over existing protein prediction tools.
• The webserver is freely available, promoting wider accessibility in research.
• Local execution options via Python package or Docker image support high-throughput applications.