#subpageNavigation { display:none;}

SCB Talk Monday Jan 29th: David Ornelles and Translation

A couple days ago, the SCB Discussion group, organize by Dr. Salsbury, had the pleasure of hosting David Ornelles from WFSM Department of Microbiology and Immunology. A beautiful biophysical and biochemical study of translation. Specifically, study adenovirus and polyribosome formation on various message RNAs. 

From a quantitive point of view, one of the most interesting aspects was the use of Weibull distributions to describe the distribution of polyribosome, and the changes across mRNAs and across various mutant viruses.

Interestingly, I often put Weibull and Gumbel distribution problems on stat mech exams as examples of other physically useful distributions. Exponential, Gaussian, Poisson and uniform distributions get boring.


We're in a Protein Science Special Issue

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."

William Thompson, former undergrad researcher, won an NSF graduate fellowship

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.



New article: MutSa, allostery in response to ligand binding, and machine learning.

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.