Project description:Mutation effects prediction is a fundamental challenge in biotechnology and biomedicine. State-of-the-art computational methods have demonstrated the benefits of including semantically rich representations learned from protein sequences, but leave structural constraints out of reach. Here we developed Protein Mutational Effect Predictor (ProMEP), a general and multimodal deep representation learning method that simultaneously learns sequence context and structural constraints from proteins at the scale of evolution. ProMEP markedly outperforms current leading methods and enables accurate zero-shot mutational effects prediction across a variety of deep mutational scanning experiments. The application of ProMEP in the transposon-associated TnpB enzyme engineering task further demonstrates its ability for high-throughput protein space exploration. Without prior knowledge of TnpB, ProMEP accurately identifies multiple mutations that significantly improve the editing efficiency from millions of variants.
2024-03-13 | GSE261254 | GEO
Project description:Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning
Project description:MicroRNA (miRNA) maturation is critically dependent on structural features of primary transcripts (pri-miRNAs). However, the scarcity of determined pri-miRNA structures has limited our understanding of miRNA maturation. Here we employed SHAPE-MaP, a high-throughput RNA structure probing method, to unravel the secondary structures of 476 high-confidence human pri-miRNAs. Our SHAPE-based structures diverge substantially from those inferred solely from computation, particularly in the apical loop and basal segments, underlining the need for experimental data in RNA structure prediction. By comparing the structures with high-throughput processing data, we determined the optimal structural features of pri-miRNAs. The sequence determinants are influenced substantially by their structural contexts. Moreover, we identified an element termed the bulged GWG motif (bGWG) with a 3′ bulge in the lower stem, which promotes processing. Our structure-function mapping better annotates the determinants of pri-miRNA processing and offers practical implications for designing small hairpin RNAs and predicting the impacts of miRNA mutations.
Project description:Despite the importance of Aβ aggregation in Alzheimer’s disease etiology, our understanding of the sequence determinants of aggregation is sparse and largely derived from in vitro studies. For example, in vitro proline and alanine scanning mutagenesis of Aβ40 proposed core regions important for aggregation. However, we lack even this limited mutagenesis data for the more disease-relevant Aβ42. Thus, to better understand the molecular determinants of Aβ42 aggregation in a cell-based system, we combined a yeast DHFR aggregation assay with deep mutational scanning. We measured the effect of 791 of the 798 possible single amino acid substitutions on the aggregation propensity of Aβ42. We found that ~75% of substitutions, largely to hydrophobic residues, maintained or increased aggregation. We identified 11 positions at which substitutions, particularly to hydrophilic and charged amino acids, disrupted Aβ aggregation. These critical positions were similar but not identical to critical positions identified in previous Aβ mutagenesis studies. Finally, we analyzed our large-scale mutagenesis data in the context of different Aβ aggregate structural models, finding that the mutagenesis data agreed best with models derived from fibrils seeded using brain-derived Aβ aggregates.