Deep Mutational Scanning of an Engineered High-affinity Ligand of the poly(A) Binding Protein MLLE Domain
Bibliographic record
Abstract
• PAM2 are peptide motifs that bind polyA binding protein and a ubiquitin ligase UBR5. • PAM2-containing proteins are involved in mRNA metabolism and RNP assembly. • An engineered, chimeric PAM2 motif binds with 10-fold improved affinity. • Deep scanning mutagenesis identifies essential and dispensable PAM2 residues. The MLLE domain is a peptide-binding domain found in the poly(A) binding protein (PABP) and the ubiquitin protein E3 ligase N-recognin 5 (UBR5) that recognizes a conserved motif, named PABP-interacting motif 2 (PAM2). The majority of PAM2 sequences bind to MLLE domains with low-micromolar affinity. Here, we designed a chimeric PAM2 peptide termed super PAM2 (sPAM2) by combining classical and trinucleotide repeat-containing 6 (TNRC6)-like binding modes to create a superior binder for the MLLE domain. The crystal structure of the PABPC1 MLLE-sPAM2 complex shows a crucial role of conserved sPAM2 leucine, phenylalanine and tryptophan residues in the interaction. We used deep mutational scanning (DMS) coupled with isothermal titration calorimetry (ITC) to characterize the specificity profiles for PABPC1 and UBR5 MLLE. The best sPAM2 sequence binds to PABPC1 MLLE with low-nanomolar affinity and nearly 20-fold more tightly than the best natural PAM2 sequence. This suggests that the affinities of natural PAM2 sequences are tuned to control their binding to PABPC1 and UBR5. Our study will aid in the discovery of new PAM2-containing proteins (PACs) and facilitate in vivo studies of PAM2-mediated cellular pathways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".