Biology subjects

Eguchi, R. R.

Publications and source records attributed to Eguchi, R. R..

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↗

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

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↗

bioRxiv2020

ProGen: Language Modeling for Protein Generation

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervised sequence generation problem in order to leverage the exponentially growing set of proteins that lack costly, structural annotations. We train a 1.2B-parameter language model, ProGen, on [~]280M protein sequences conditioned on taxonomic and keyword tags such as molecular function and cellular component. This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy.

synthetic biology↗

bioRxiv2020

Protein sequence design with a learned potential

AO_SCPLOWBSTRACTC_SCPLOWThe task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. We investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with the in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

bioinformatics↗

bioRxiv2018

Multi-Scale Structural Analysis of Proteins by Deep Semantic Segmentation

Recent advancements in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds, and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation -- a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structural quality assessment. We represent protein structures as 2D -carbon distance matrices (\"contact maps\"), and train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model performs exceptionally well, achieving a per-residue accuracy of 90.8% on the test set (95.0% average accuracy over all classes; 87.8% average within-structure accuracy). The unique aspect of our classifier is that it encodes sequence agnostic residue environments from the PDB and can assess structural quality as quantitative probabilities. We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design.\n\nSignificanceRecent computational advances have allowed researchers to predict the structure of many proteins from their amino acid sequences, as well as designing new sequences that fold into predefined structures. However, these tasks are often challenging because they require selection of a small subset of promising structural models from a large pool of stochastically generated ones. Here, we describe a novel approach to protein model selection that uses 2D image classification techniques to evaluate 3D protein models. Our method can be used to select structures based on the fold that they adopt, and can also be used to identify regions of low structural quality. These capabilities yield a powerful tool for both protein design and structure prediction.

bioinformatics↗