LambdaPP: Fast and accessible protein-specific phenotype predictions

被引:9
|
作者
Olenyi, Tobias [1 ,2 ]
Marquet, Celine [1 ,2 ]
Heinzinger, Michael [1 ,2 ]
Kroeger, Benjamin [1 ]
Nikolova, Tiha [1 ]
Bernhofer, Michael [2 ]
Saendig, Philip [1 ]
Schuetze, Konstantin [1 ]
Littmann, Maria [1 ]
Mirdita, Milot [3 ]
Steinegger, Martin [3 ,4 ,5 ]
Dallago, Christian [1 ,6 ]
Rost, Burkhard [1 ,7 ,8 ]
机构
[1] TUM Tech Univ Munich, Dept Informat Bioinformat & Computat Biol I12, Boltzmannstr 3, D-85748 Garching, Germany
[2] TUM Grad Sch, Ctr Doctoral Studies Informat & Its Applicat CeDo, Garching, Germany
[3] Seoul Natl Univ, Sch Biol Sci, Seoul, South Korea
[4] Seoul Natl Univ, Korea Artificial Intelligence Inst, Seoul, South Korea
[5] Seoul Natl Univ, Korea Inst Mol Biol & Genet, Seoul, South Korea
[6] VantAI, New York, NY USA
[7] Inst Adv Study TUM IAS, Lichtenbergstr 2a, D-85748 Garching, Germany
[8] TUM Sch Life Sci Weihenstephan WZW, Freising Weihenstephan, Germany
关键词
artificial intelligence; protein annotation; protein function prediction; protein language models; protein structure prediction; web server; SECONDARY STRUCTURE; PREDICTPROTEIN; LANGUAGE;
D O I
10.1002/pro.4524
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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. LambdaPP is freely available for everyone to use under , the interactive results for the case study can be found under . The frontend of LambdaPP can be found on GitHub (), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings () python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.
引用
收藏
页数:11
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