Journal
JOURNAL OF MOLECULAR BIOLOGY
Volume 433, Issue 20, Pages -Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jmb.2021.167180
Keywords
protein structure; prediction; Alphafold; genetic variants; protein function
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Funding
- German Network for Bioinformatics Infrastructure (de.NBI) - Germany Federal Ministry of Education and Research (BMBF)
- Swedish Research Council [201805882]
- Wellcome Trust [210585/B/18/Z]
- Wellcome Trust [210585/B/18/Z] Funding Source: Wellcome Trust
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The necessity to interpret genetic variants in terms of pathology or biological mechanism is urgent, with many insights into protein function impacted by genetic changes obtainable from three-dimensional structures. The development of precise methods, like Alphafold2, to predict structures from amino acid sequences may greatly benefit those seeking to understand genetic changes. This paper examines the current state of protein structures known for human and other proteomes, as well as the potential impact of Alphafold2 on variant interpretation efforts, suggesting that the available structural data for the human proteome may have a smaller impact on interpretation than anticipated. Additional efforts in structure prediction are also discussed for aiding the understanding of genetic variants.
The need to make sense of the thousands of genetic variants uncovered every day in terms of pathology or biological mechanism is acute. Many insights into how genetic changes impact protein function can be gleaned if three-dimensional structures of the associated proteins are available. The availability of a highly accurate method of predicting structures from amino acid sequences (e.g. Alphafold2) is thus potentially a great boost to those wanting to understand genetic changes. In this paper we discuss the current state of protein structures known for the human and other proteomes and how Alphafold2 might impact on variant interpretation efforts. For the human proteome in particular, the state of the available structural data suggests that the impact on variant interpretation might be less than anticipated. We also discuss additional efforts in structure prediction that could further aid the understanding of genetic variants. (C) 2021 The Author(s). Published by Elsevier Ltd.
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