Imperial College London
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DrAlessiaDavid

Faculty of Natural Sciences, Department of Life Sciences

Lecturer in Bioinformatics and Data Intensive Biology
 
 
 
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Contact

 

+44 (0)20 7594 5333alessia.david09

 
 
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Location

 

Department of BioinformaticsSir Ernst Chain BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{David:2023:10.1016/j.sbi.2023.102600,
author = {David, A and Sternberg, MJE},
doi = {10.1016/j.sbi.2023.102600},
journal = {Current Opinion in Structural Biology},
pages = {1--8},
title = {Protein structure-based evaluation of missense variants: Resources, challenges and future directions.},
url = {http://dx.doi.org/10.1016/j.sbi.2023.102600},
volume = {80},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We provide an overview of the methods that can be used for protein structure-based evaluation of missense variants. The algorithms can be broadly divided into those that calculate the difference in free energy (ΔΔG) between the wild type and variant structures and those that use structural features to predict the damaging effect of a variant without providing a ΔΔG. A wide range of machine learning approaches have been employed to develop those algorithms. We also discuss challenges and opportunities for variant interpretation in view of the recent breakthrough in three-dimensional structural modelling using deep learning.
AU  - David,A
AU  - Sternberg,MJE
DO  - 10.1016/j.sbi.2023.102600
EP  - 8
PY  - 2023///
SN  - 0959-440X
SP  - 1
TI  - Protein structure-based evaluation of missense variants: Resources, challenges and future directions.
T2  - Current Opinion in Structural Biology
UR  - http://dx.doi.org/10.1016/j.sbi.2023.102600
UR  - https://www.ncbi.nlm.nih.gov/pubmed/37126977
UR  - https://www.sciencedirect.com/science/article/pii/S0959440X2300074X
UR  - http://hdl.handle.net/10044/1/104224
VL  - 80
ER  -
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