Biology subjects

Choe, C. A.

Publications and source records attributed to Choe, C. A..

6 recordsLinked to original sources

bioRxiv2025

ADAPT-M: A workflow for rapid, quantitative in vitro measurements of enriched protein libraries

Protein-protein interactions underpin most cellular interactions, and engineered binders present powerful tools for probing biology and developing novel therapeutics. One bottleneck in binder generation is the scalable, quantitative characterization of these interactions. We present ADAPT-M (Affinity Determination by Adaptation of ProTein binders for Microfluidics), a streamlined workflow that connects yeast surface display (YSD) with in vitro affinity and kinetic measurements using the high-throughput STAMMPPING microfluidic platform. ADAPT-M quantifies Kds and dissociation kinetic parameters for hundreds of enriched protein variants in under one week without requiring hands-on protein purification. We applied ADAPT-M to a computationally designed library targeting the SARS-CoV-2 Omicron BA.1 receptor binding domain, successfully recovering and measuring Kds for most highly enriched YSD variants. Measurements correlate strongly with biolayer interferometry and yeast titration assays. ADAPT-M further enabled selection of lead candidates for structural and mutational analysis, which revealed designed paratopes were preserved despite binding to off-target epitopes. By bridging YSD screening and in vitro validation, ADAPT-M accelerates protein binder discovery and supports data-driven protein engineering.

biophysics↗

bioRxiv2025

RNAGym: Large-scale Benchmarks for RNA Fitness and Structure Prediction

Understanding RNA structure and predicting the functional consequences of mutations are fundamental challenges in computational biology with broad implications for therapeutic development and synthetic biology. Current evaluation of machine learning-based RNA models suffers from disparate experimental datasets and inconsistent performance assessments across different RNA families. To address these challenges, we introduce RNAGym, a large-scale benchmarking framework specifically designed for three core tasks-RNA fitness, secondary structure, and tertiary structure prediction. The framework integrates extensive datasets, including 70 standardized deep mutational scanning assays covering over a million mutations across diverse RNA types; 901k chemical-mapping reactivity profiles for secondary structure; and 215 diverse tertiary structures curated from the PDB. RNAGym is designed to facilitate a systematic comparison of RNA models, offering an essential resource to enhance the understanding and development of these models.

synthetic biology↗

bioRxiv2024

Compact RNA sensors for increasingly complex functions of multiple inputs

Designing single molecules that compute general functions of input molecular partners represents a major unsolved challenge in molecular design. Here, we demonstrate that high-throughput, iterative experimental testing of diverse RNA designs crowdsourced from Eterna yields sensors of increasingly complex functions of input oligonucleotide concentrations. After designing single-input RNA sensors with activation ratios beyond our detection limits, we created logic gates, including challenging XOR and XNOR gates, and sensors that respond to the ratio of two inputs. Finally, we describe the OpenTB challenge, which elicited 85-nucleotide sensors that compute a score for diagnosing active tuberculosis, based on the ratio of products of three gene segments. Building on OpenTB design strategies, we created an algorithm Nucleologic that produces similarly compact sensors for the three-gene score based on RNA and DNA. These results open new avenues for diverse applications of compact, single molecule sensors previously limited by design complexity.

synthetic biology↗

bioRxiv2022

Deep Generative Design of Epitope-Specific Binding Proteins by Latent Conformation Optimization

AO_SCPLOWBSTRACTC_SCPLOWDesigning de novo binding proteins against arbitrary epitopes using a single scaffold, as seen with natural antibodies, remains an unsolved challenge in protein design. Current design methods are unable to capture the structural dynamics of flexible loops nor search loop conformational space in a principled way. Here we present Sculptor, a deep generative design algorithm that creates epitope-specific protein binders. The Sculptor algorithm constitutes a joint search over the positions, interactions, and generated conformations of a fold, and crafts a backbone to complement a user-specified epitope. Sequences are designed onto generated backbones using a combination of a residue-wise interaction database, a convolutional sequence design module, and Rosetta. Instead of relying on static structures, we capture the local conformational landscape of a single fold using molecular dynamics, and demonstrate that a model trained on such dense conformational data can generate backbones tailor-fit to an epitope. We use Sculptor to design binders against a conserved epitope on venom toxins implicated in neuromuscular paralysis, and obtain a multi-toxin binder from a small naive library - a promising step towards creating broadly neutralizing binders. This study constitutes a novel application of deep generative modeling for epitope-targeted design, leveraging conformational dynamics to achieve function.

biochemistry↗

bioRxiv2020

Theoretical basis for stabilizing messenger RNA through secondary structure design

RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery, and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Here, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall hydrolysis rate. To characterize the stabilization achievable through structure design, we compare AUP optimization by conventional mRNA design methods to results from more computationally sophisticated algorithms and crowdsourcing through the OpenVaccine challenge on the Eterna platform. These computational tests were carried out on both model mRNAs and COVID-19 mRNA vaccine candidates. We find that rational design on Eterna and the more sophisticated algorithms lead to constructs with low AUP, which we term superfolder mRNAs. These designs exhibit wide diversity of sequence and structure features that may be desirable for translation, biophysical size, and immunogenicity, and their folding is robust to temperature, choice of flanking untranslated regions, and changes in target protein sequence, as illustrated by rapid redesign of superfolder mRNAs for B.1.351, P.1, and B.1.1.7 variants of the prefusion-stabilized SARS-CoV-2 spike protein. Increases in in vitro mRNA half-life by at least two-fold appear immediately achievable. Significance statementMessenger RNA (mRNA) medicines that encode and promote translation of a target protein have shown promising use as vaccines in the current SARS-CoV-2 pandemic as well as infectious diseases due to their speed of design and manufacturing. However, these molecules are intrinsically prone to hydrolysis, leading to poor stability in aqueous buffer and major challenges in distribution. Here, we present a principled biophysical model for predicting RNA degradation, and demonstrate that the stability of any mRNA can be increased at least two-fold over conventional design techniques. Furthermore, the predicted stabilization is robust to post-design modifications. This conceptual framework and accompanying algorithm can be immediately deployed to guide re-design of mRNA vaccines and therapeutics to increase in vitro stability.

biophysics↗

bioRxiv2020

Ig-VAE: Generative Modeling of Immunoglobulin Proteins by Direct 3D Coordinate Generation

AO_SCPLOWBSTRACTC_SCPLOWWhile deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation--an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the models generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

bioinformatics↗