Samuel Sledzieski

@samsl.io

771 Followers 254 Following

Research Fellow @flatironinstitute.org @simonsfoundation.org Formerly @csail.mit.edu @msftresearch.bsky.social @uconn.bsky.social Computational systems x structure biology | he/him | https://samsl.io | 👨🏼‍💻

Three-panel comparison of RNA 2D structure prediction. Top (orange): RNA sequence alone fed to ViennaRNA yields a branched 2D structure with F1 = 0.404. Middle (gray): RNA sequence plus experimental chemical reactivity data fed to ViennaRNA yields the reference structure. Bottom (blue): RNA sequence plus MIMIC-predicted reactivity fed to ViennaRNA yields a structure with F1 = 0.987, closely matching the experimental reference.
Left: three strip plots showing TM-score vs. WT (median 0.89), MaSIF surface similarity vs. WT (median 0.91), and AF3 cofolding iPTM (median 0.81) for 37 MIMIC-designed PD-L1 sequences. Right: PyMOL-style structural visualization overlaying a MIMIC-designed protein (blue) on the PD-L1 binding partner (gray), with the interface region highlighted in red.
Left panel: schematic of the MIMIC RNA design pipeline, showing a mutated RNA sequence conditioned on wild-type splice pattern and wild-type phylogenetic conservation scores to produce a designed sequence. Right panel: four stacked line plots showing PhyloP scores, MIMIC phyloP VEP scores (C>T splice-altering vs. C>A non-splice-altering), and SpliceAI acceptor and donor probabilities (unconditioned vs. PhyloP-conditioned) across positions relative to the HBB IVS-II-654 C>T pathogenic mutation, illustrating that conditioning on conservation suppresses the cryptic splice site.
Left: four horizontal bar charts showing AUPR for gene-level and transcript-level splice site prediction (coding and non-coding) comparing MIMIC, AlphaGenome, SpliceAI, and NT3. MIMIC (dark blue) leads all comparisons; a light blue bar shows MIMIC with TSS+TES conditioning. Right: line plot of splice site probability versus transcript position for SPRY1, comparing ground truth, unconditioned MIMIC prediction, and conditioned MIMIC prediction, with donor and acceptor site calls marked. After TSS+TES conditioning, false positive predictions decrease.
Two dot-plot benchmark comparisons. Left (PFMBench): MIMIC (dark blue) versus ~13 protein language model baselines (gray) across 11 tasks spanning function, structure, interaction, and developability. MIMIC leads or is competitive on most tasks. Bottom bar chart shows win rates of each baseline against MIMIC; all are below 50%. Right (mRNABench): Same format for 7 RNA/multimodal tasks. MIMIC leads on most; Evo2 and Orthrus show the highest win rates but remain below 50%.
Diagram titled "LORE: Multi-modal and multi-source data alignment." Left: a table mapping transcript IDs to UniProt IDs with checkmarks and X marks indicating data availability across modalities (PhyloP, RNA chemical probing, splice pattern, backbone structure). Right: expanded view of a single entry (ENST000012345 / P04637) showing linked RNA and protein data including sequence, PhyloP conservation track, splice pattern diagram, and AlphaFold structure.
Schematic of MIMIC's split-track input encoding and encoder-decoder architecture. Left panel shows two input tracks: nucleic acid (DNA sequence + conservation scores summed per token) and protein (amino acid residues + backbone structure summed per token), plus cellular context and gene taxonomy tokens. Right panel shows selected tokens fed into an encoder, producing a multimodal embedding passed to a decoder that outputs predictions.
Video highlighting the capabilities of MIMIC to model DNA, RNA, and protein.
(a) Workflow showing use of RocketSHP to predict correlations between residue motion, network construction and clustering, and finally those clusters on the structure of a protein (b) Scatter plot showing betweenness centrality of each residue in KRAS, colored by variance in centrality when that residue is measured as part of an in silico deep mutational scan. (c) Corresponding scatter plot showing the true folding DDG of KRAS in a deep mutational scan, colored by variance of each residue. The peaks in this plot correspond strongly with the peaks in the previous plot.
(a) Scatter plot showing RocketSHP predicted RMSF and hetNOE, with a correlation of -0.67 and a confidence interval of [-0.68, -0.66]. (b) Line plot showing per-residue hetNOE or ReLU(1 - RMSF predicted) for the protein ACRIIA4, showing agreement between the two. (c) Two structures of ACRIIA4, colored by hetNOE or ReLU(1 - RMSF predicted), showing agreement between the two.
A cartoon shows how MINT can be used to predict whether mutations lead to a loss or conservation of PPI, compared with oncogenic PPIs discovered in Cheng et al. The bottom panel shows several example mutations, where MINT correctly predicts for all except one whether the mutation will conserve or disrupt binding of this complex.
Cartoons showing MINT applied for antibody interaction prediction and TR-epitope prediction. Box plots show that MINT outperforms other baseline methods on all data sets.
Diagram showing MINT used to predict binary binding, binding affinity, or variant effect. On the SKEMPI and gold-standard (Bernett) PPI data sets, box plots show that MINT achieves the highest PCC and AUPRC respectively.
Three panel figure showing our approach (entitled MINT), the implementation as a modification of ESM with cross-chain attention, and several applications including general complex prediction, antibody binding, TCR-epitope-MHC interactions, mutational effect on cancer PPI, and cross-neutralization of SARS-CoV-2 variant effects
Diagram showing traditional approaches for applying protein language models to multiple sequences, either by concatenating output embeddings or input sequences
Percentage of proteins predicted to adopt different homo-oligomer symmetries across several different kingdoms. Icosahedral symmetry is overrepresented in viruses, and C4/C5 is overrepresented in invertebrates.
Poster for "Learning the language of protein-protein interactions with ESM-Multimer"