In Protein science : a publication of the Protein Society
The availability of accurate and fast Artificial Intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP - leveraging ColabFold and computed in minutes - is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. This article is protected by copyright. All rights reserved.
Olenyi Tobias, Marquet Céline, Heinzinger Michael, Kröger Benjamin, Nikolova Tiha, Bernhofer Michael, Sändig Philip, Schütze Konstantin, Littmann Maria, Mirdita Milot, Steinegger Martin, Dallago Christian, Rost Burkhard
2022-Dec-01
artificial intelligence, protein annotation, protein function prediction, protein language models, protein structure prediction, web server
