Search Results - "Greener, Joe G"

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints by Greener, Joe G., Kandathil, Shaun M., Jones, David T.

“…The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently,…”
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Design of metalloproteins and novel protein folds using variational autoencoders by Greener, Joe G., Moffat, Lewis, Jones, David T

“…The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies…”
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Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins by Greener, Joe G, Jones, David T

“…Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning…”
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Structure-based prediction of protein allostery by Greener, Joe G, Sternberg, Michael JE

“…•The structure-based prediction of allostery will realise the potential of allostery.•Modern computational methods can help predict allosteric sites and…”
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High‐Throughput Kinetic Analysis for Target‐Directed Covalent Ligand Discovery by Craven, Gregory B., Affron, Dominic P., Allen, Charlotte E., Matthies, Stefan, Greener, Joe G., Morgan, Rhodri M. L., Tate, Edward W., Armstrong, Alan, Mann, David J.

“…Cysteine‐reactive small molecules are used as chemical probes of biological systems and as medicines. Identifying high‐quality covalent ligands requires…”
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A guide to machine learning for biologists by Greener, Joe G., Kandathil, Shaun M., Moffat, Lewis, Jones, David T.

“…The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive…”
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Differentiable simulation to develop molecular dynamics force fields for disordered proteins by Greener, Joe G

“…Implicit solvent force fields are computationally efficient but can be unsuitable for running molecular dynamics on disordered proteins. Here I improve the…”
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Prediction of interresidue contacts with DeepMetaPSICOV in CASP13 by Kandathil, Shaun M., Greener, Joe G., Jones, David T.

“…In this article, we describe our efforts in contact prediction in the CASP13 experiment. We employed a new deep learning‐based contact prediction tool,…”
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Recent developments in deep learning applied to protein structure prediction by Kandathil, Shaun M., Greener, Joe G., Jones, David T.

“…Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted…”
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Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins by Kandathil, Shaun M, Greener, Joe G, Lau, Andy M, Jones, David T

“…Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologs of the target…”
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BioStructures.jl: read, write and manipulate macromolecular structures in Julia by Greener, Joe G, Selvaraj, Joel, Ward, Ben J

“…Abstract Summary Robust, flexible and fast software to read, write and manipulate macromolecular structures is a prerequisite for productively doing structural…”
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AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis by Greener, Joe G, Sternberg, Michael J E

“…Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a…”
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Predicting Protein Dynamics and Allostery Using Multi-Protein Atomic Distance Constraints by Greener, Joe G., Filippis, Ioannis, Sternberg, Michael J.E.

“…The related concepts of protein dynamics, conformational ensembles and allostery are often difficult to study with molecular dynamics (MD) due to the…”
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Julia for biologists by Roesch, Elisabeth, Greener, Joe G., MacLean, Adam L., Nassar, Huda, Rackauckas, Christopher, Holy, Timothy E., Stumpf, Michael P. H.

“…Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the…”
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Author Correction: Julia for biologists by Roesch, Elisabeth, Greener, Joe G., MacLean, Adam L., Nassar, Huda, Rackauckas, Christopher, Holy, Timothy E., Stumpf, Michael P. H.

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High‐Throughput Kinetic Analysis for Target‐Directed Covalent Ligand Discovery by Craven, Gregory B., Affron, Dominic P., Allen, Charlotte E., Matthies, Stefan, Greener, Joe G., Morgan, Rhodri M. L., Tate, Edward W., Armstrong, Alan, Mann, David J.

“…Cysteine‐reactive small molecules are used as chemical probes of biological systems and as medicines. Identifying high‐quality covalent ligands requires…”
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On the design space between molecular mechanics and machine learning force fields by Wang, Yuanqing, Takaba, Kenichiro, Chen, Michael S, Wieder, Marcus, Xu, Yuzhi, Zhu, Tong, Zhang, John Z. H, Nagle, Arnav, Yu, Kuang, Wang, Xinyan, Cole, Daniel J, Rackers, Joshua A, Cho, Kyunghyun, Greener, Joe G, Eastman, Peter, Martiniani, Stefano, Tuckerman, Mark E

“…A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently…”
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Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints by Greener, Joe G, Kandathil, Shaun M, Jones, David T

“…Nature Communications 10:3977 (2019) The inapplicability of amino acid covariation methods to small protein families has limited their use for structural…”
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Julia for Biologists by Roesch, Elisabeth, Greener, Joe G, MacLean, Adam L, Nassar, Huda, Rackauckas, Christopher, Holy, Timothy E, Stumpf, Michael P. H

“…Increasing emphasis on data and quantitative methods in the biomedical sciences is making biological research more computational. Collecting, curating,…”
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Design of metalloproteins and novel protein folds using variational autoencoders by Greener, Joe G, Moffat, Lewis, Jones, David T

“…Scientific Reports 8:16189 (2018) The design of novel proteins has many applications but remains an attritional process with success in isolated cases…”
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