Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Joe G. Greener"'
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
Prediction of protein structures on the scale of genomes remains a challenge. Here the authors introduce a protein structure prediction method that uses deep learning to predict inter-atomic distances, torsion angles and hydrogen bonds, and apply it
Externí odkaz:
https://doaj.org/article/ccdc53de641e49ecb1dcddc994cc5c94
Autor:
Joe G Greener, Kiarash Jamali
Comparing and searching protein structures independent of primary sequence has proved useful for remote homology detection, function annotation and protein classification. With the recent leap in accuracy of protein structure prediction methods and i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0be165cb4f40564d1cf62b7a2ccef31
https://doi.org/10.1101/2022.11.28.518224
https://doi.org/10.1101/2022.11.28.518224
Publikováno v:
Nature Reviews Molecular Cell Biology. 23:40-55
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models
Autor:
Elisabeth Roesch, Joe G. Greener, Adam L. MacLean, Huda Nassar, Christopher Rackauckas, Timothy E. Holy, Michael P. H. Stumpf
Publikováno v:
Nature Methods. 20:771-771
Publikováno v:
PNAS
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Significance We present a deep learning-based predictor of protein tertiary structure that uses only a multiple sequence alignment (MSA) as input. To date, most emphasis has been on the accuracy of such deep learning methods, but here we show that ac
The prediction of protein structure and the design of novel protein sequences and structures have long been intertwined. The recently released AlphaFold has heralded a new generation of accurate protein structure prediction, but the extent to which t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96f2a163a1999ea08169025caa3428dd
https://doi.org/10.1101/2021.08.24.457549
https://doi.org/10.1101/2021.08.24.457549
Publikováno v:
Nature reviews. Molecular cell biology. 23(1)
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
Nature Communications
Nature Communications
The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even f
Publikováno v:
Proteins: Structure, Function, and Bioinformatics
Proteins
Proteins
In this article, we describe our efforts in contact prediction in the CASP13 experiment. We employed a new deep learning-based contact prediction tool, DeepMetaPSICOV (or DMP for short), together with new methods and data sources for alignment genera
Autor:
Joe G Greener, David T. Jones
Publikováno v:
PLoS ONE
PLoS ONE, Vol 16, Iss 9, p e0256990 (2021)
PLoS ONE, Vol 16, Iss 9, p e0256990 (2021)
Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simula
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::665c5f7fd06b14dd3f9ced3519e305f8
https://doi.org/10.1101/2021.02.05.429941
https://doi.org/10.1101/2021.02.05.429941