The impact of AlphaFold2 on experimental structure solution.
Faraday Discuss
; 240(0): 184-195, 2022 Nov 08.
Article
in English
| MEDLINE | ID: covidwho-1984449
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Proteins
/
Computational Biology
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Faraday Discuss
Journal subject:
Chemistry
Year:
2022
Document Type:
Article
Affiliation country:
D2fd00072e
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