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Groves, B.

Publications and source records attributed to Groves, B..

2 recordsLinked to original sources

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

synthetic biology↗

Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the translational efficiency of the 5 untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5 UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on translation of Kozak sequence composition, upstream open reading frames (uORFs) and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the translational efficiency of both a held-out set of the random 5 UTRs as well as native S. cerevisiae 5 UTRs. The model additionally was used to computationally evolve highly translating 5 UTRs. We confirmed experimentally that the great majority of the evolved sequences lead to higher translation rates than the starting sequences, demonstrating the predictive power of this model.

synthetic biology↗