| Home page | https://solve.lanl.gov/terwilliger/ |
| Phenix web site | https://phenix-online.org/ |
Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, challenges persist in capturing sidechain packing, condition-dependent conformational dynamics, and biomolecular interactions, primarily due to scarcity of high-quality training data. Emerging techniques, including cryo-electron tomography (cryo-ET) and high-throughput crystallography, promise vast new sources of structural data, but translating raw experimental observations into mechanistically interpretable atomic models remains a key bottleneck. Here, we aim to address these challenges by improving the efficiency of structural analysis through combining experimental measurements with a landmark protein structure prediction method -- AlphaFold2. We present an augmentation of AlphaFold2, ROCKET, that refines its predictions using cryo-EM, cryo-ET, and X-ray crystallography data, and demonstrate that this approach captures biologically important structural variation that AlphaFold2 does not. By performing structure optimization in the space of coevolutionary embeddings, rather than Cartesian coordinates, ROCKET automates difficult modeling tasks, such as flips of functional loops and domain rearrangements, that are beyond the scope of current state-ofthe-art methods and, in some instances, even manual human modeling. The ability to efficiently sample these barrier-crossing rearrangements unlocks a new horizon for scalable and automated model building. Crucially, ROCKET does not require retraining of AlphaFold2 and is readily adaptable to multimers, ligand-cofolding, and other data modalities. Conversely, our differentiable crystallographic and cryo-EM target functions are capable of augmenting other structure prediction methods. ROCKET thus provides an extensible framework for the integration of experimental observables with biomolecular machine learning. ### Competing Interest Statement MA is a member of the scientific advisory boards of Cyrus Biotechnology, Deep Forest Sciences, Nabla Bio, Oracle Therapeutics, and Achira.
Once again feeling pretty damn good about having a place in the macromolecular crystallography community after attending the SLAC User Meeting and getting another round of "what would it take to recruit you to ___ in a few years?" and "don't worry, nobody's going to forget you [while you're in Europe]".
I'm finally beginning to believe I give decent talks when it feels like I botched it (every time) but I get feedback that they're great. Part of this is definitely that I put effort into making sure they're digestible and relevant, not just showing off the coolest stuff, but once I'm actually speaking I think I'm underwhelming and boring the audience. I've never managed to recalibrate how it feels while giving the talk, so I know there's room for improvement on the presentation itself.
After all that plus the first full night of sleep in a week, very ready to tackle some deep cleaning and minor repairs around the apartment in preparation for move-out.
A judge has forced Elon Musk to disclose who helped him buy Twitter.
And to no one's surprise: Two rich Russians from Putin's inner circle were part of the deal. Their names are Petr Aven and Vadim Moshkovich, and allegedly they are related to companies on the list.
And clearly, EU parliamentarian Guy Verhofstadt is not having it.
https://fortune.com/2024/08/22/elon-musk-x-twitter-owner-list/
Obviously, indirect Russian relations in itself is not proof of Russian influence.
I will do some more digging.
'“I’m not an artist; I can’t draw anything else,” she said in a recent interview with Quanta. It was “a lot of draw and erase, draw and erase, draw and erase.” After a year of trial and error, she homed in on elegant sheets and looping ribbons to represent atomic structures. These drawings, which first appeared in the journal Advances in Protein Chemistry in 1981, became known as ribbon diagrams.'
Of top-notch algorithms and zoned-out humans
On June 1 2009, Air France Flight 447 vanished on a routine transatlantic flight. The circumstances were mysterious until the black box flight recorder was recovered nearly two years later, and the awful truth became apparent: three highly trained pilots had crashed a fully functional aircraft into the ocean, killing all 288 people on board
https://timharford.com/2024/02/of-top-notch-algorithms-and-zoned-out-humans/
Iris Young (he/they/she) (PhD)
Randahl Fink
Thiago Carvalho
Heidi Li Feldman
Science Scholar
Tim Harford 
