Supercharged Protein Analysis in the Era of Accurate Structure Prediction
Martin Steinegger, Seoul National University, South Korea
Speaker |
Martin Steinegger, Seoul National University, South Korea
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When |
Jan 28, 2025
from 04:00 PM to 05:00 PM |
Where | Via zoom |
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COLLOQUIUM TALK
Title: Supercharged Protein Analysis in the Era of Accurate Structure Prediction
Abstract: Protein analysis has been transformed by machine-learning methods, with highly accurate structure prediction tools like AlphaFold2 and ESMFold leading the way. These methods have generated an unprecedented number of publicly available protein structures, with the AlphaFold database and ESMatlas now containing over 214 and 620 million predicted structures, respectively. To utilize this wealth of structural data, we have developed advanced tools like Foldseek, Foldseek-multimer and FoldMason to efficiently search and analyze these massive datasets. Additionally, our BFVD database significantly improves viral protein structure predictions by leveraging homology searches across petabases of sequencing data. This expanding landscape of structural information is revolutionizing genomic and proteomic annotations. In this talk, I will explore how these new tools and resources are enabling researchers to uncover novel biological insights and accelerate discovery across diverse fields of biology.
Title: Supercharged Protein Analysis in the Era of Accurate Structure Prediction
Abstract: Protein analysis has been transformed by machine-learning methods, with highly accurate structure prediction tools like AlphaFold2 and ESMFold leading the way. These methods have generated an unprecedented number of publicly available protein structures, with the AlphaFold database and ESMatlas now containing over 214 and 620 million predicted structures, respectively. To utilize this wealth of structural data, we have developed advanced tools like Foldseek, Foldseek-multimer and FoldMason to efficiently search and analyze these massive datasets. Additionally, our BFVD database significantly improves viral protein structure predictions by leveraging homology searches across petabases of sequencing data. This expanding landscape of structural information is revolutionizing genomic and proteomic annotations. In this talk, I will explore how these new tools and resources are enabling researchers to uncover novel biological insights and accelerate discovery across diverse fields of biology.
Speaker Bio: Martin Steinegger is a faculty member in the Biology Department at Seoul National University, South Korea. He works on large-scale sequence data analysis and method development, with a focus on open science and open-source initiatives. He received his Ph.D. in Computer Science in 2018 from the Technical University Munich, Germany, collaborating with the Max Planck Institute for Biophysical Chemistry on computational methods for metagenomic sequencing data. He worked at Johns Hopkins University, United States, where he developed methods for pathogen identification, contamination detection, and proteome annotation. His research focuses on developing algorithms to search, cluster, and assemble sequence data, pathogen detection in sequencing data, metagenomic analysis, and protein function and structure prediction.
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https://zoom.us/j/93160371789?pwd=GViD20dFqg8hXjCvz2bEvRGO0Dp5r7.1
Meeting ID: 931 6037 1789
Passcode: 052104