Martin Steinegger

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Milot MirditaSeoul National UniversityVerified email at snu.ac.kr
Johannes SödingMax-Planck-Institute for Multidisciplinary Sciences, GöttingenVerified email at mpinat.mpg.de
Burkhard RostProfessor of Computer Science, Technical University MunichVerified email at tum.de
Michael HeinzingerTechnische Universität MünchenVerified email at tum.de
Sergey OvchinnikovMITVerified email at mit.edu
Christian DallagoNVIDIA & Technische Universität MünchenVerified email at tum.de
Ahmed ElnaggarTechnical University of MunichVerified email at tum.de
Steven SalzbergBloomberg Distinguished Professor, Johns Hopkins UniversityVerified email at jhu.edu
Debsindhu BhowmikComputational Scientist, Oak Ridge National LaboratoryVerified email at ornl.gov
Antonio Fernàndez-GuerraCentre for Ancient Environmental GenomicsVerified email at sund.ku.dk
Clovis GaliezGrenoble-Alpes University, Grenoble INP, LJKVerified email at grenoble-inp.fr
Tom O. DelmontCNRS - Genoscope - TARA anvi-OceansVerified email at genoscope.cns.fr
John JumperDeepMindVerified email at google.com
Prof. Dr. Frank Oliver GlöcknerAlfred Wegener Institute Bremerhaven, MARUM, University of BremenVerified email at awi.de
Maria J. MartinEuropean Bioinformatics InstituteVerified email at ebi.ac.uk
Vikram AlvaGroup Leader, Max Planck Institute for BiologyVerified email at tuebingen.mpg.de
Andrei LupasDirector of the Department of Protein Evolution, Max-Planck-Institute for BiologyVerified email at tuebingen.mpg.de
Christof AngermuellerGoogleVerified email at google.com
koray kavukcuogluDeepMindVerified email at kavukcuoglu.org
Demis HassabisDeepMind

Martin Steinegger

Associate Professor, Seoul National University, Korea

Verified email at snu.ac.kr - Homepage

Bioinformatics Computational Biology


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Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Nature, 2021	33001	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	6621	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	3015	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	1875*	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	1270*	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	1030	2019
Clustering huge protein sequence sets in linear time M Steinegger, J Söding Nature communications 9 (1), 2542, 2018	800	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	693	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	669	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	576	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	476	2019
Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold M Steinegger, M Mirdita, J Söding Nature Methods 16, 603–606, 2019	424	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	369	2021
Metagenome analysis using the Kraken software suite J Lu, N Rincon, DE Wood, FP Breitwieser, C Pockrandt, B Langmead, ... Nature Protocols, 2022	344	2022
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	236	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	228	2016
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	208*	2020
Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank M Steinegger, SL Salzberg Genome Biology 21, 2020	200	2020
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	195	2023
Fast and sensitive taxonomic assignment to metagenomic contigs M Mirdita, M Steinegger, F Breitwieser, J Söding, E Levy Karin Bioinformatics, 2021	193	2021

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