Joe G Greener
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Explore the profile of Joe G Greener including associated specialties, affiliations and a list of published articles.
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15
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Recent Articles
1.
Greener J
Chem Sci
. 2024 Mar;
15(13):4897-4909.
PMID: 38550690
Implicit solvent force fields are computationally efficient but can be unsuitable for running molecular dynamics on disordered proteins. Here I improve the a99SB- force field and the GBNeck2 implicit solvent...
2.
Roesch E, Greener J, MacLean A, Nassar H, Rackauckas C, Holy T, et al.
Nat Methods
. 2023 Apr;
20(5):771.
PMID: 37120675
No abstract available.
3.
Roesch E, Greener J, MacLean A, Nassar H, Rackauckas C, Holy T, et al.
Nat Methods
. 2023 Apr;
20(5):655-664.
PMID: 37024649
Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the simulation of larger and more realistic models...
4.
Kandathil S, Greener J, Lau A, Jones D
Proc Natl Acad Sci U S A
. 2022 Jan;
119(4).
PMID: 35074909
Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologs of the target protein. The most successful approaches combine large feature sets derived...
5.
Greener J, Kandathil S, Moffat L, Jones D
Nat Rev Mol Cell Biol
. 2021 Sep;
23(1):40-55.
PMID: 34518686
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....
6.
Greener J, Jones D
PLoS One
. 2021 Sep;
16(9):e0256990.
PMID: 34473813
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...
7.
Greener J, Selvaraj J, Ward B
Bioinformatics
. 2020 May;
36(14):4206-4207.
PMID: 32407511
Summary: Robust, flexible and fast software to read, write and manipulate macromolecular structures is a prerequisite for productively doing structural bioinformatics. We present BioStructures.jl, the first dedicated package in the...
8.
Kandathil S, Greener J, Jones D
Proteins
. 2019 Oct;
87(12):1179-1189.
PMID: 31589782
Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted considerable interest in recent years. Methods employing DNNs...
9.
Greener J, Kandathil S, Jones D
Nat Commun
. 2019 Sep;
10(1):3977.
PMID: 31484923
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...
10.
Kandathil S, Greener J, Jones D
Proteins
. 2019 Jul;
87(12):1092-1099.
PMID: 31298436
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...