LambdaPP: Fast and accessible protein-specific phenotype predictions.

TitleLambdaPP: Fast and accessible protein-specific phenotype predictions.
Publication TypeJournal Article
Year of Publication2022
AuthorsOlenyi, T, Marquet, C, Heinzinger, M, Kröger, B, Nikolova, T, Bernhofer, M, Sändig, P, Schütze, K, Littmann, M, Mirdita, M, Steinegger, M, Dallago, C, Rost, B
JournalProtein Sci
Date Published2022 Dec 01

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.

Alternate JournalProtein Sci
PubMed ID36454227