Protein Science has a special issue on Tools for Protein Science and we have an article on "Visualizing correlated motion with HBDSCAN clustering" a collaboration between Dr. Salsbury's group, lead by Ryan Melvin, and a statistician, Dr. Berenhaut. We used a newer rigorous clustering method to answer the question of how to divide a protein based on fluctuations rather than spatially, and then map these divisions onto structures. Hopefully should be a useful tool for rigorously defining "dynamic domains."
The Salsbury group has had plenty of news with papers fellowships, etc, but you wouldn't know it from our website. Hopefully, we'll get better and maybe catch up on some old news.
This is actually over a week old, but Ryan Godwin, who also defended his dissertation, won the Outstanding TA award from the physics department!
Interestingly, Dr. Salsbury, his advisor, has never had him as a TA, so it was based solely on the other faculty.
Ryan Godwin has successfully defended his PhD thesis! Congrats!
His dissertation is entited: "Binding Nemo: Adventures in Molecular Dynamics."
He also won the Outstanding TA award among his honors.
William Thompson, who did research in the Salsbury group for a couple years, wrote his honors thesis based on his work, and co-authored two manuscripts, has been awarded an NSF graduate fellowship.
Good work William! He did change fields after graduation from computational biophysics to particle physics, but that shows the generality of a physics BS.
First, I have been very bad about posting news. It has been a productive half year in the Salsbury Group.
However, I will break the drought of news, by announcing our latest accepted manuscript in Frontiers in Physics, "MutSα’s Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning."
This article focuses on how the DNA complex, MutSalpha, responds differently when bound to DNA crosslinked by cisplatin, i.e., 1-2 cross-linking, carboplatin, more 1-3 cross-linking, and with DNA with an FDU-substitution. We have published on these on the past, except FDU, but there were GPU-enabled simulations an order of magnitude longer than before, and we used machine learning techniques to examine hydrogen bonds and to do unbiased clustering to really examine the conformational effects of binding these different damaged DNAs with minimal user bias. The selection of interface hydrogen bonds was motivated by previous work. Overall, using these machine learning on these long-scale simulations really cleanly identified different conformational responses and the hydrogen bonds associated with them, even though the changes were often non-local.
I might write a more detailed summary, but I am quite happy with this article, and you can read it here.
PS provisional article, pre-proof, so some typos/minor corrections are being made.
One of the clustering methods we use is quality threshold clustering. Originally developed for gene clustering, Heyer et al Genome Res 1999 9, 1105-115, we use it as part of analysis of molecular dynamics simulations.
We decided to put up our python implementation of it at figshare.
Ryan Melvin just had his second article, "All-Atom MD Reveals Mechanism of Zinc Complexation with Therapeutic F10" accepted for publication in the Journal of Physical Chemistry B.
It's part of a collaboration with Bill Gmeiner in Cancer Biology focusing on understanding and hopefully one day improving on F10-based therapies. Also part of an interest that our group has had in zinc off and on since Dr. Salsbury was a post-doc.
Dr. Lu, a former postdoc, has moved on and has taken a position at Xidian University as an assistant professor. Congrats!