The impact of AlphaFold2 on experimental structure solution

By Maximilian Edich, D. Briggs, O. Kippes, Yunyun Gao, Andrea Thorn. 2022

14 citations recorded by the source corpus.

AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, post-translational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning-based fold prediction is a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Yi Zhao
2. Zhenyu Yang
3. Runsheng Chen
4. Xiaoxi Zeng

In Molecular Biology

1. Yi Zhao
2. Zhenyu Yang
3. Runsheng Chen
4. Xiaoxi Zeng

Citations in the corpus

• AlphaFold2 and its applications in the fields of biology and medicine (2023, background): “They enable researchers to better determine where the starting and ending points of a domain locate in the sequence, and avoid less ordered regions; 86 Neglecting less ordered regions from protein sequences Fig.”