Publications

Found 19 results
Filters: Author is Heinzinger, Michael  [Clear All Filters]
2022
Heinzinger M, Littmann M, Sillitoe I, Bordin N, Orengo C, Rost B. Contrastive learning on protein embeddings enlightens midnight zone. NAR Genom Bioinform. 2022 ;4(2):lqac043.
Olenyi T, Marquet C, Heinzinger M, Kröger B, Nikolova T, Bernhofer M, Sändig P, Schütze K, Littmann M, Mirdita M, et al. LambdaPP: Fast and accessible protein-specific phenotype predictions. Protein Sci. 2022 :e4524.
Schütze K, Heinzinger M, Steinegger M, Rost B. Nearest neighbor search on embeddings rapidly identifies distant protein relations. Front Bioinform. 2022 ;2:1033775.
Bordin N, Dallago C, Heinzinger M, Kim S, Littmann M, Rauer C, Steinegger M, Rost B, Orengo C. Novel machine learning approaches revolutionize protein knowledge. Trends Biochem Sci. 2022 .
Weissenow K, Heinzinger M, Rost B. Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction. Structure. 2022 .
Ilzhöfer D, Heinzinger M, Rost B. SETH predicts nuances of residue disorder from protein embeddings. Front Bioinform. 2022 ;2:1019597.
2021
Littmann M, Bordin N, Heinzinger M, Schütze K, Dallago C, Orengo C, Rost B. Clustering FunFams using sequence embeddings improves EC purity. Bioinformatics. 2021 .
Littmann M, Heinzinger M, Dallago C, Olenyi T, Rost B. Embeddings from deep learning transfer GO annotations beyond homology. Sci Rep. 2021 ;11(1):1160.
Marquet C, Heinzinger M, Olenyi T, Dallago C, Erckert K, Bernhofer M, Nechaev D, Rost B. Embeddings from protein language models predict conservation and variant effects. Hum Genet. 2021 .
Dallago C, Schütze K, Heinzinger M, Olenyi T, Littmann M, Lu AX, Yang KK, Min S, Yoon S, Morton JT, et al. Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets. Curr Protoc. 2021 ;1(5):e113.
Bernhofer M, Dallago C, Karl T, Satagopam V, Heinzinger M, Littmann M, Olenyi T, Qiu J, Schütze K, Yachdav G, et al. PredictProtein - Predicting Protein Structure and Function for 29 Years. Nucleic Acids Res. 2021 ;49(W1):W535-W540.
Littmann M, Heinzinger M, Dallago C, Weissenow K, Rost B. Protein embeddings and deep learning predict binding residues for various ligand classes. Sci Rep. 2021 ;11(1):23916.
Heinzinger M, Dallago C, Rost B. Protein matchmaking through representation learning. Cell Syst. 2021 ;12(10):948-950.
Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Yu W, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M, et al. ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing. IEEE Trans Pattern Anal Mach Intell. 2021 ;PP.
2020
Zaucha J, Heinzinger M, Tarnovskaya S, Rost B, Frishman D. Family-specific analysis of variant pathogenicity prediction tools. NAR Genom Bioinform. 2020 ;2(2):lqaa014.
Zaucha J, Heinzinger M, Kulandaisamy A, Kataka E, Salvádor ÓLlorian, Popov P, Rost B, M Gromiha M, Zhorov BS, Frishman D. Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins. Brief Bioinform. 2020 .
Qiu J, Bernhofer M, Heinzinger M, Kemper S, Norambuena T, Melo F, Rost B. ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence. J Mol Biol. 2020 ;432(7):2428-2443.
2019
Heinzinger M, Elnaggar A, Wang Y, Dallago C, Nechaev D, Matthes F, Rost B. Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinformatics. 2019 ;20(1):723.
2018
Schafferhans A, O'Donoghue SI, Heinzinger M, Rost B. Dark Proteins Important for Cellular Function. Proteomics. 2018 ;18(21-22):e1800227.