Thema der Dissertation:
Computational interpretation of disease-causing, structural, and non-coding human genetic variants Thema der Disputation:
Computational prediction of protein structures using a neural-network based model
Computational interpretation of disease-causing, structural, and non-coding human genetic variants Thema der Disputation:
Computational prediction of protein structures using a neural-network based model
Abstract: Praised as nothing less than a revolution to molecular biological research, a novel method to predict protein structures computationally was introduced in 2021. As proteins are one of the most diverse and fundamental building blocks of life, predicting their structures is challenging and crucial to understanding human health. AlphaFold shifts the focus from laborious experimental protocols to predicting the 3D-structures of more than 200 million proteins computationally, including almost all known human proteins. Using machine learning AlphaFold integrates features such as multiple sequence alignments as well as value sets derived from experimental datasets into a deep learning algorithm, greatly outperforming existing methods in accurately predicting protein shapes. Researchers are now able to answer long standing biological questions, such as modelling the nuclear pore complex by integrating the predicted structures of about 1000 pieces of 30 different proteins. AlphaFold shows the potential of computational approaches to be used in aiding basic research that ultimately benefits human health.
Zeit & Ort
07.10.2022 | 09:00
Seminarraum 032
(FB Mathematik und Informatik, Arnimalle 6, 14195 Berlin)