Joe Greener

@jgreener64.bsky.social

Computational chemist/structural bioinformatician working on improving molecular simulation at MRC Laboratory of Molecular Biology. jgreener64.github.io

I have often seen Microsoft software spread within organisations due to IT and procurement people being "captured" by effective lobbying and sales pitches. Microsoft software is not particularly good, safe or cheap, and researchers benefit from having choice over what they use. @ukri.org

Sjors Scheres@sjorsscheres.bsky.social · 2mo ago

Some inside the @ukri.org wish to push @microsoft.com software too far down into research. This is a risk that didn't work out well for the ICC! @ukri.org should focus on funding good science rather than homogenise for the sake of homogenisation. www.theguardian.com/commentisfre...

Function of a protein: something the protein does. Function in maths: a mapping from X to Y. Function in code: re-use code to do a task. Function at a conference: have a drink and look at posters.

Exploiting internal symmetry can be a powerful approach to analyse convergence in MD simulations. If two conformational states are symmetry related, their kinetic and thermodynamic properties should be the same. If one doesn't enforce this during analysis this provides a useful test. 1/n

Proud to share the yeast telomerase structure, led by the talented @hongmiaohu.bsky.social in collaboration with the Wellinger and Chartrand labs. Discovered 37 years ago and took us nearly 7 years but totally worth the wait 😍. www.science.org/doi/10.1126/... www.youtube.com/watch?v=gFE4...

Cryo-EM structure of yeast telomerase

YouTube video by MRC Laboratory of Molecular Biology

youtube.com

MRC Laboratory of Molecular Biology@mrclmb.ac.uk · 6mo ago

Led by Investigator Scientist @hongmiaohu.bsky.social, @kellythd-nguyen.bsky.social’s group in the LMB’s Structural Studies Division have produced the first ever structure of telomerase from yeast. Read more here: mrclmb.ac.uk/news-events/... #LMBResearch #cryoEM 🧪

Check out our pre-print, where we train a protein and small molecule force field from scratch with a graph neural network. We show comparable performance to existing, manually-tuned force fields on a range of tasks including binding free energy prediction. (1/4) arxiv.org/abs/2603.16770

Training a force field for proteins and small molecules from scratch

Force fields for molecular dynamics are usually developed manually, limiting their transferability and making systematic exploration of functional forms challenging. We developed a graph neural networ...

arxiv.org