3rd International Meeting on Protein and RNA Structure Prediction

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I just returned from the 3rd International Meeting on Protein and RNA Structure Prediction held in Montego Bay, Jamaica. It was an excellent meeting held in a beautiful location.

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I presented our work on work on determining structures from sparse NMR data, in collaboration with the groups of Guy Montelione and Chris Jaroniec. There was a lot of interest and I had several interesting discussions.

There were a lot of interesting talks on topics ranging from better ways to do SAXS experiments to new structure prediction algorithms.

There was also some free time to explore. We went to a luminous lagoon filled with bioluminescent plankton that emit light in response to shear stress. It was an amazing experience.

Overall, it was a fantastic conference that I look forward to returning to in 2 years time.

Lab awarded CIHR grant

I'm extremely proud to announce that the MacCallum lab was awarded a five year grant from CIHR for our project aimed at developing new molecular diagnostics for crystal arthropathies. This is an exciting project with great results to date, and we're grateful for funding to push it further.

Overall success rates in this year's CIHR Project Grant were only 16 percent, which is completely unhealthy and reflects funding that has been flat for 15 years. Success rates outside of BC and Ontario were half the national average. Such regional disparities will not lead to the thriving research environment that Canadians need.

While I'm fortunate to have obtained a grant in the in the current climate, I hope that the Minister of Science recognizes the need for improved funding for science in Canada.

MOLSSI Workshop at Stanford

I recently attended the Molecular Sciences Software Institute (MOLSSI) workshop hosted at Stanford. MOLSSI is an NSF-funded consortium that aims to advance molecular simulation software, infrastructure, education, standards, and best practices. The workshop focused on the topics of conformational sampling, molecular simulation workflows, and emerging applications of machine learning in molecular science. The discussion was great and it was a wonderful opportunity to engage with people I hadn't met before, as well as old friends.

Paper featured on F1000

Our recent paper was featured on F1000:

Recent results in the field of protein folding have showed that atomistic molecular dynamics (MD) simulations can correctly fold small proteins, providing insights on their structures as well as on their folding pathways. Those simulations, however, remain computationally expensive. MD simulations are deterministic: once the initial positions and velocities have been set, the trajectory for the protein under study is derived by numerically solving Newton's second law with tiny time steps, hence the long simulation times. If additional information (such as "forming a hydrophobic core") could be added to steer the simulations, it is expected that they could be sped up significantly. However, proper inclusion of such additional information requires caution as it needs to satisfy the thermodynamic principles of Boltzmann's law. The authors have developed a proper statistical mechanics framework for this purpose, they have applied this framework to successfully fold twenty small proteins, including ubiquitin, a millisecond folder. Inclusion of this framework into folding simulations using MD is shown to yield up to five orders of magnitude faster computational folding times than traditional MD.