| Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Nature, 2021 | 32152 | 2021 |
| ColabFold: making protein folding accessible to all M Mirdita, K Schütze, Y Moriwaki, L Heo, S Ovchinnikov, M Steinegger Nature Methods, 679–682, 2022 | 6399 | 2022 |
| MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets M Steinegger, J Söding Nature biotechnology 35 (11), 1026-1028, 2017 | 2911 | 2017 |
| ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing A Elnaggar, M Heinzinger, C Dallago, G Rehawi, Y Wang, L Jones, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 14 (8), 2021 | 1792* | 2021 |
| Fast and accurate protein structure search with Foldseek M van Kempen, SS Kim, C Tumescheit, M Mirdita, J Lee, CLM Gilchrist, ... Nature Biotechnology 42, 243–246, 2023 | 1178* | 2023 |
| HH-suite3 for fast remote homology detection and deep protein annotation M Steinegger, M Meier, M Mirdita, H Vöhringer, SJ Haunsberger, J Söding BMC Bioinformatics 20, 2019 | 998 | 2019 |
| Clustering huge protein sequence sets in linear time M Steinegger, J Söding Nature communications 9 (1), 2542, 2018 | 778 | 2018 |
| Uniclust databases of clustered and deeply annotated protein sequences and alignments M Mirdita, L von den Driesch, C Galiez, MJ Martin, J Söding, M Steinegger Nucleic Acids Research, 2016 | 677 | 2016 |
| Protein sequence analysis using the MPI bioinformatics toolkit F Gabler, SZ Nam, S Till, M Mirdita, M Steinegger, J Söding, AN Lupas, ... Current Protocols in Bioinformatics 72 (1), e108, 2020 | 645 | 2020 |
| AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences M Varadi, D Bertoni, P Magana, U Paramval, I Pidruchna, ... Nucleic Acids Research, 2023 | 497 | 2023 |
| MMseqs2 desktop and local web server app for fast, interactive sequence searches M Mirdita, M Steinegger, J Söding Bioinformatics 35 (16), 2856–2858, 2019 | 465 | 2019 |
| Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold M Steinegger, M Mirdita, J Söding Nature Methods 16, 603–606, 2019 | 411 | 2019 |
| Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021 | 361 | 2021 |
| Metagenome analysis using the Kraken software suite J Lu, N Rincon, DE Wood, FP Breitwieser, C Pockrandt, B Langmead, ... Nature Protocols, 2022 | 329 | 2022 |
| High accuracy protein structure prediction using deep learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, ... Fourteenth critical assessment of techniques for protein structure …, 2020 | 241* | 2020 |
| PredictProtein - Predicting Protein Structure and Function for 29 Years M Bernhofer, C Dallago, T Karl, V Satagopam, M Heinzinger, M Littmann, ... Nucleic Acids Research, 2021 | 231 | 2021 |
| MMseqs software suite for fast and deep clustering and searching of large protein sequence sets M Hauser, M Steinegger, J Söding Bioinformatics 32 (9), 1323-1330, 2016 | 226 | 2016 |
| Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank M Steinegger, SL Salzberg Genome Biology 21, 2020 | 194 | 2020 |
| Fast and sensitive taxonomic assignment to metagenomic contigs M Mirdita, M Steinegger, F Breitwieser, J Söding, E Levy Karin Bioinformatics, 2021 | 190 | 2021 |
| Clustering predicted structures at the scale of the known protein universe I Barrio-Hernandez, J Yeo, J Jänes, M Mirdita, CLM Gilchrist, T Wein, ... Nature, 2023 | 180 | 2023 |
Milot MirditaSeoul National Universitysnu.ac.kr의 이메일 확인됨