Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads.
Transient, regulated binding of globular protein domains to Short Linear Motifs (SLiMs) in disordered regions of other proteins drives cellular signaling. Mapping the energy landscapes of these interactions is essential for deciphering and perturbing signaling networks but is challenging due to their weak affinities. We present a powerful technology (MRBLE-pep) that simultaneously quantifies protein binding to a library of peptides directly synthesized on beads containing unique spectral codes. Using MRBLE-pep, we systematically probe binding of human calcineurin (CN), a conserved protein phosphatase essential for the immune response and target of immunosuppressants, to the PxIxIT SLiM. We discover that flanking residues and post-translational modifications critically contribute to PxIxIT-CN affinity and identify CN-binding peptides based on multiple scaffolds with a wide range of affinities. The quantitative biophysical data provided by this approach will improve computational modeling efforts, elucidate a broad range of weak protein-SLiM interactions, and revolutionize our understanding of signaling networks.
View details for DOI 10.7554/eLife.40499
View details for PubMedID 31282865
micrIO: an open-source autosampler and fraction collector for automated microfluidic input-output.
Lab on a chip
Microfluidic devices are an enabling technology for many labs, facilitating a wide range of applications spanning high-throughput encapsulation, molecular separations, and long-term cell culture. In many cases, however, their utility is limited by a 'world-to-chip' barrier that makes it difficult to serially interface samples with these devices. As a result, many researchers are forced to rely on low-throughput, manual approaches for managing device input and output (IO) of samples, reagents, and effluent. Here, we present a hardware-software platform for automated microfluidic IO (micrIO). The platform, which is uniquely compatible with positive-pressure microfluidics, comprises an 'AutoSipper' for input and a 'Fraction Collector' for output. To facilitate widespread adoption, both are open-source builds constructed from components that are readily purchased online or fabricated from included design files. The software control library, written in Python, allows the platform to be integrated with existing experimental setups and to coordinate IO with other functions such as valve actuation and assay imaging. We demonstrate these capabilities by coupling both the AutoSipper and Fraction Collector to two microfluidic devices: a simple, valved inlet manifold and a microfluidic droplet generator that produces beads with distinct spectral codes. Analysis of the collected materials in each case establishes the ability of the platform to draw from and output to specific wells of multiwell plates with negligible cross-contamination between samples.
View details for DOI 10.1039/c9lc00512a
View details for PubMedID 31701110
Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2018; 115 (16): E3702–E3711
Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.
View details for PubMedID 29588420
An Open-Source, Programmable Pneumatic Setup for Operation and Automated Control of Single- and Multi-Layer Microfluidic Devices.
2018; 3: 117–34
Microfluidic technologies have been used across diverse disciplines (e.g. high-throughput biological measurement, fluid physics, laboratory fluid manipulation) but widespread adoption has been limited in part due to the lack of openly disseminated resources that enable non-specialist labs to make and operate their own devices. Here, we report the open-source build of a pneumatic setup capable of operating both single and multilayer (Quake-style) microfluidic devices with programmable scripting automation. This setup can operate both simple and complex devices with 48 device valve control inputs and 18 sample inputs, with modular design for easy expansion, at a fraction of the cost of similar commercial solutions. We present a detailed step-by-step guide to building the pneumatic instrumentation, as well as instructions for custom device operation using our software, Geppetto, through an easy-to-use GUI for live on-chip valve actuation and a scripting system for experiment automation. We show robust valve actuation with near real-time software feedback and demonstrate use of the setup for high-throughput biochemical measurements on-chip. This open-source setup will enable specialists and novices alike to run microfluidic devices easily in their own laboratories.
View details for PubMedID 30221210