12 | | |
| 12 | * Wynton upgrades |
| 13 | * UCSD got money for GPUs but other UCs can't use them |
| 14 | * they're hosted at SDSC which has painful security requirements |
| 15 | * Wynton is getting two new GPU nodes that the RBVI will get access to |
| 16 | * 8 x L40 48GB GPUs to be added to preexisting A40s |
| 17 | * Officially NRNB nodes but RBVI is a member of NRNB |
| 18 | * UCSD and Toronto people may also use them |
| 19 | * Plato is also getting a memory upgrade (2 nodes go up to 1TB) |
| 20 | * 1.8 release |
| 21 | * Nearly ready, Eric needs to track down tricky GC bug |
| 22 | * Tom gave LookSee demo with his wife, was successful |
| 23 | * New intern, in the middle of finals, may be here next Thursday |
| 24 | * Elaine presentation |
| 25 | * Embedding Residue Interaction Networks |
| 26 | * No training required, uses autoencoder |
| 27 | * Handles many time points efficiently |
| 28 | * Motivation |
| 29 | * Hard to get meaning from MD trajectories, many variables |
| 30 | * Typically need expert knowledge to know which variables to pay attention to |
| 31 | * RINs capture structural changes on many scales |
| 32 | * Prior analyses used averages or only a few snapshots, lost temporal information |
| 33 | * Implementation |
| 34 | * Edge drawn b/t non-sequence-adjacent residues with any atoms within 6 angstroms |
| 35 | * residue closeness centrality --> N-dimensional feature vector for each time point |
| 36 | * Small Protein Example: Trp-Cage (20 residues) |
| 37 | * Super long simulation (208 microseconds, >1M frames) |
| 38 | * Supp data: Trp-Cage PCA and UMAP |
| 39 | * Author's note: compared to EncoderMap, PCA more global, UMAP more local |
| 40 | * Multidomain Example: FAT10 (165 residues) |
| 41 | * FAT10 has two ubiquitin-like domains wiht a flexible linker and tails |
| 42 | * 50 simulations, 50ns |
| 43 | * Authors hypothesize that different closed conformations offer different interaction sites for FAT10's many binding partners |
| 44 | * Map resolves functionally different conformations of a complex system over time, yet retains an interpretable global organization |
| 45 | * Alternative Featurizations |
| 46 | * Adjacency matrix colored by density or R_g |
| 47 | * Degree centrality colored by density or R_g |
| 48 | * Backbone dihedral angles colored by density or R_g |
| 49 | * Discussion |
| 50 | * Protein graph can be defined at different resolutions/with different criteria |
| 51 | * RINs cannot distinguish fine local changes, best for systems that undergo strong changes |