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Annalisa Pawlosky

Annalisa Pawlosky

Annalisa Pawlosky is the founder and principal investigator for the Google Accelerated Science biochemistry and molecular biology laboratory, a unique laboratory specifically designed for building custom assays that integrate with mathematical models, including machine learning, to accelerate new findings in the biological sciences. Annalisa conducted her postdoctoral work under Jan Liphardt and Michael Clarke as a Stanford Molecular Imaging Scholar postdoctoral fellow, and focused on discovering the mechanism behind the role of Usp16 in stem cell senescence. For this work, she built dimerization dependent protein sensors (ddFPs) between Usp16 and histone H2A for single molecule live cell imaging assays with TIRF. Annalisa received her PhD from MIT in the Harvard-MIT Division of Health Sciences and Technology program under the supervision of Alexander van Oudenaarden with her thesis titled Single molecule techniques to probe decision-making processes in developmental biology. She initiated mammalian work in the van Oudenaarden laboratory, and focused on studying patterning in mammalian organs during embryonic development. In particular, she studied and modeled Notch pathway regulated mechanisms behind cellular patterning for mammalian auditory outer hair cells with single molecule RNA FISH (smFISH).
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    ProtSeq: towards high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes
    Jessica Hong
    Michael Connor Gibbons
    Ali Bashir
    Diana Wu
    Shirley Shao
    Zachary Cutts
    Mariya Chavarha
    Ye Chen
    Lauren Schiff
    Mikelle Foster
    Victoria Church
    Llyke Ching
    Sara Ahadi
    Anna Hieu-Thao Le
    Alexander Tran
    Michelle Therese Dimon
    Phillip Jess
    iScience, vol. 25 (2022), pp. 32
    Preview abstract We demonstrate early progress toward constructing a high-throughput, single-molecule protein sequencing technology utilizing barcoded DNA aptamers (binders) to recognize terminal amino acids of peptides (targets) tethered on a next-generation sequencing chip. DNA binders deposit unique, amino acid identifying barcodes on the chip. The end goal is that over multiple binding cycles, a sequential chain of DNA barcodes will identify the amino acid sequence of a peptide. Toward this, we demonstrate successful target identification with two sets of target-binder pairs: DNA-DNA and Peptide-Protein. For DNA-DNA binding, we show assembly and sequencing of DNA barcodes over 6 consecutive binding cycles. Intriguingly, our computational simulation predicts that a small set of semi-selective DNA binders offers significant coverage of the human proteome. Toward this end, we introduce a binder discovery pipeline that ultimately could merge with the chip assay into a technology called ProtSeq, for future high-throughput, single-molecule protein sequencing. View details
    Machine learning guided aptamer discovery
    Ali Bashir
    Geoff Davis
    Michelle Therese Dimon
    Qin Yang
    Scott Ferguson
    Zan Armstrong
    Nature Communications (2021)
    Preview abstract Aptamers are discovered by searching a large library for sequences with desirable binding properties. These libraries, however, are physically constrained to a fraction of the theoretical sequence space and limited to sampling strategies that are easy to scale. Integrating machine learning could enable identification of high-performing aptamers across this unexplored fitness landscape. We employed particle display (PD) to partition aptamers by affinity and trained neural network models to improve physically-derived aptamers and predict affinity in silico. These predictions were used to locally improve physically derived aptamers as well as identify completely novel, high-affinity aptamers de novo. We experimentally validated the predictions, improving aptamer candidate designs at a rate 10-fold higher than random perturbation, and generating novel aptamers at a rate 448-fold higher than PD alone. We characterized the explanatory power of the models globally and locally and showed successful sequence truncation while maintaining affinity. This work combines machine learning and physical discovery, uses principles that are widely applicable to other display technologies, and provides a path forward for better diagnostic and therapeutic agents. View details
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