I am a research associate in the Bioinformatics Group at UCL. The group is based at the Francis Crick Institute. Previously I was a PhD student in the Structural Bioinformatics Group at Imperial College London.
My interests include protein structures, software development and open science. Currently my research involves developing computational methods to predict and design protein structures, with a focus on deep learning.
Google Scholar page
Greener JG, Moffat L and Jones DT, Design of metalloproteins and novel protein folds using variational autoencoders, Scientific Reports (2018) 8:16189 - Link
Craven GB, Affron DP, Allen CE, Matthies S, Greener JG, Morgan RML, Tate EW, Armstrong A and Mann DJ. High-Throughput Kinetic Analysis for Target-Directed Covalent Ligand Discovery, Angewandte Chemie International Edition (2018) 57 - Link
Greener JG and Sternberg MJE. Structure-based prediction of protein allostery, Current Opinion in Structural Biology (2018) 50, 1-8 - Link
Greener JG, Filippis I and Sternberg MJE. Predicting protein dynamics and allostery using multi-protein atomic distance constraints, Structure (2017) 25, 546-558 - Link
Greener JG and Sternberg MJE. AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis, BMC Bioinformatics (2015) 16:335 - Link - Website
Warren DeLano Structural Bioinformatics and Computational Biophysics Award for best presentation at 3DSIG 2016.
Prize for highest mark in MSc class (2014).
Runner-up prize for best presentation at Imperial College London Department of Life Sciences Research Day (2017).
2014-2017 - Imperial College London - PhD in Structural Bioinformatics - Funded by BBSRC
2013-2014 - Imperial College London - MSc Bioinformatics and Theoretical Systems Biology - Funded by BBSRC - Distinction
2009-2013 - Emmanuel College, University of Cambridge - MSci and BA Hons Natural Sciences (Chemistry) - 2.1 classification in all years
Experience and skills
- Developed the ExProSE computational procedure to generate ensembles of protein structures from multiple input structures. This method provides good coverage of conformational space and can be used, for example, to predict allosteric sites on proteins and explore protein dynamics.
- Developed a computational procedure, AlloPred, to predict allosteric sites on proteins. Normal mode analysis was used to model the effect of perturbations at potential allosteric sites and these features were combined in a machine learning approach to predict allosteric pockets. Performance is similar and complementary to existing methods and the website has had submissions from around the world.
- Three-month PIPS placement at benevolent.ai, a growing company that uses machine learning approaches to predict drug candidates. My project involved developing an in-house tool as part of the biomedical team for use by drug discoverers.
- Eight-week placement at the University of São Paulo (summer 2013) on virtual screening to re-purpose existing drugs.
- Eight-week placement at Imperial College London (summer 2011) on computational modelling of ionic liquids.
- Member of the open source BioJulia project - wrote the BioStructures.jl package that deals with parsing and manipulating macromolecular structures. Have also made contributions to the Biopython project.
- Lead web developer for the Write on Point project that gives applicants to university the skills to write an effective personal statement.
- Experience with machine learning and SVMs.
- Supervision of two ten-week MSc projects and a ten-week BSc project at Imperial, involving project design and regular meetings with the students.
- Demonstration in undergraduate and graduate computational practicals.
- Gave a talk on my PhD work at the Bioinformatics London Meetup group (April 2016).
- Gave a talk on my PhD work at the 3DSIG meeting at ISMB (July 2016).
- Peer reviewed 3 scientific papers, 1 funding proposal and was on the review committee for JuliaCon 2018.
Email: j.greener at ucl.ac.uk