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HIBIT

From Sequence-to-Expression: Understanding How Proteins Are Made

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Roadmaps
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Methods
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Benchmarks
GROQ-seq HIBIT Growth Curves

Overview

The Align Protein Expression project aims to establish a high-throughput experimental system built to unravel how DNA sequence governs protein production. Our goal is to generate the data needed for the creation of a predictive model of protein expression that replaces trial-and-error with precision design. This would enable breakthroughs in synthetic biology, biotechnology, and gene regulation.

Proposal
Read the Proposal
Peer Review Document
Read the Review
Technical Report
coming soon

Strategy

This platform systematically measures how host organisms and standardized experimental conditions affect protein expression levels. We tested our first arrayed assay format, the HiBiT luminescence assay, in E. coli and Pichia pastoris to quantify soluble expression of diverse proteins. This straightforward assay captures both transcriptional and translational effects in a single composite readout.

platform capabilities

Key features:

  • High-resolution expression measurements using HiBiT and other scalable reporter assays
  • Standardized, protocols across widely used hosts including E. coli
  • Machine learning-ready datasets linking sequence context to protein abundance
    Extensible experimental framework, adaptable to additional hosts, sequences, and conditions
  • Extensible experimental framework, adaptable to additional hosts, sequences, and conditions

As the dataset grows, so does its power—enabling increasingly accurate models that can predict expression outcomes from sequence alone. These models will accelerate gene circuit design, guide selection of optimal expression systems, and support fine-tuned control over biological processes.

Timeline

2023

Held Protein Sequence-to-Expression Workshop

2024

Methods Development

2025

Pooled Assay Diligence

Coming Soon Technical Report Published

Technical Program Manager

Kasia Baranowski
LinkedIn

Kasia attended graduate school at Harvard where she studied the mycobacterial cell wall. Since then, she’s worked in synthetic biology as a yeast strain engineer, a CRISPR platform biologist and a scientific project manager. At Align, Kasia leads the Protein Sequences to Expression dataset effort, and also supports the GROQ-seq platform.

Interested in getting involved?

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