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Martin Steinegger

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Assistant Professor

Seoul National University

South Korea

Research Interests

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About

Martin Steinegger is an Assistant Professor at Seoul National University, South Korea. His research primarily focuses on bioinformatics, particularly in protein structure prediction, phylogenetic reconstruction, and metagenomic analysis. Recent contributions include advancements in GPU-accelerated homology search and protein complex alignment, as well as developing models for protein sequence and structure analysis.

Recent Grants

Grant: Close

FoldU: 머신러닝을 사용한 메타유전체 스케일 대규모 단백질 폴딩

Open Date: 2021-03-01

Close Date: 2026-02-01

Grant: Close

고해상도 입체 구조 해석을 위한 Cryo-EM/ET 이미징 연계 기술개발연구

Open Date: 2021-01-01

Close Date: 2026-01-01

Grant: Open

Petasearch: surveilling pathogens on a global scale(페타탐색: 글로벌 스케일의 감염병 병원체 감시)

Open Date: 2020-09-01

Close Date: 2029-08-01

Grant: Close

In silico protein design by artificial intelligence and physical chemistry(인공지능과 물리화학을 결합한 인실리코 단백질 설계)

Open Date: 2020-01-01

Close Date: 2025-01-01

Grant: Close

Discovery of novel genomes through protein-guided assembly

Open Date: 2020-01-01

Close Date: 2022-01-01

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Collaborators (16)

Milot Mirdita

Assistant Professor

Sungkyunkwan University

SOUTH KOREA

Martin Steinegger

Research Interests

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About

Recent Grants

Articles (24)

Expanding the scope of protein language modeling to protein-protein interactions with MSA Pairformer

Structural motif search across the protein universe with Folddisco

Multiple Protein Structure Alignment at Scale with FoldMason

GPU-accelerated homology search with MMseqs2

Unicore Enables Scalable and Accurate Phylogenetic Reconstruction with Structural Core Genes

Rapid and sensitive protein complex alignment with Foldseek-Multimer

BFVD—a large repository of predicted viral protein structures

Easy and interactive taxonomic profiling with Metabuli App

Author Correction: Metagenome analysis using the Kraken software suite

Metabuli: sensitive and specific metagenomic classification via joint analysis of amino acid and DNA

Petabase-Scale Homology Search for Structure Prediction

Easy and accurate protein structure prediction using ColabFold

Bilingual language model for protein sequence and structure

UFCG: database of universal fungal core genes and pipeline for genome-wide phylogenetic analysis of fungi

Clustering-predicted structures at the scale of the known protein universe

Block Aligner: an adaptive SIMD-accelerated aligner for sequences and position-specific scoring matrices

Qualitative metabolomics-based characterization of a phenolic UDP-xylosyltransferase with a broad substrate spectrum from Lentinus brumalis

Fast and accurate protein structure search with Foldseek

Foldcomp: a library and format for compressing and indexing large protein structure sets

Novel machine learning approaches revolutionize protein knowledge

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

LambdaPP: Fast and accessible protein-specific phenotype predictions

LambdaPP: Fast and accessible protein-specific phenotype predictions

Metabuli: sensitive and specific metagenomic classification via joint analysis of amino-acid and DNA

Collaborators (16)

Milot Mirdita

Christine Orengo

Yoshitaka Moriwaki

Jongsik Chun

Su Datt Lam

Cameron Gilchrist

Steven Salzberg

Bertil Schmidt

Christian Dallago

Sergey Ovchinnikov

Wonyong Kim

Pedro Beltrao

Kyo Bin Kang

Burkhard Rost

Chang-Young Jang

Nicola Bordin

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