Deconvolution can be used to obtain sharp images or volumes from blurry or encoded measurements in imaging systems. Given knowledge of the system’s point spread function (PSF) over the field of view, a reconstruction algorithm can be used to recover a clear image or volume. Most deconvolution algorithms assume shift-invariance; however, in realistic systems, the PSF varies laterally and axially across the field of view due to aberrations or design. Shift-varying models can be used, but are often slow and computationally intensive. In this work, we propose a deep-learning-based approach that leverages knowledge about the system’s spatially varying PSFs for fast 2D and 3D reconstructions. Our approach, termed MultiWienerNet, uses multiple differentiable Wiener filters paired with a convolutional neural network to incorporate spatial variance. Trained using simulated data and tested on experimental data, our approach offers a 625 − 1600 × increase in speed compared to iterative methods with a spatially varying model, and outperforms existing deep-learning-based methods that assume shift invariance.
Successful development of monoclonal antibodies (mAbs) for therapeutic applications is hindered by developability issues such as low solubility, low thermal stability, high aggregation, and high immunogenicity. The discovery of more developable mAb candidates relies on high-quality antibody libraries for isolating candidates with desirable properties. We present Immunoglobulin Language Model (IgLM), a deep generative language model for generating synthetic libraries by re-designing variable-length spans of antibody sequences. IgLM formulates antibody design as an autoregressive sequence generation task based on text-infilling in natural language. We trained IgLM on approximately 558M antibody heavy- and light-chain variable sequences, conditioning on each sequence's chain type and species-of-origin. We demonstrate that IgLM can be applied to generate synthetic libraries that may accelerate the discovery of therapeutic antibody candidates
Generative artificial intelligence (AI) has the potential to greatly increase the speed, quality and controllability of antibody design. Traditional de novo antibody discovery requires time and resource intensive screening of large immune or synthetic libraries. These methods also offer little control over the output sequences, which can result in lead candidates with sub-optimal binding and poor developability attributes. Several groups have introduced models for generative antibody design with promising in silico evidence, however, no such method has demonstrated de novo antibody design with experimental validation. Here we use generative deep learning models to de novo design antibodies against three distinct targets, in a zero-shot fashion, where all designs are the result of a single round of model generations with no follow-up optimization. In particular, we screen over 400,000 antibody variants designed for binding to human epidermal growth factor receptor 2 (HER2) using our high-throughput wet lab capabilities. From these screens, we further characterize 421 binders using surface plasmon resonance (SPR), finding three that bind tighter than the therapeutic antibody trastuzumab. The binders are highly diverse, have low sequence identity to known antibodies, and adopt variable structural conformations. Additionally, these binders score highly on our previously introduced Naturalness metric, indicating they are likely to possess desirable developability profiles and low immunogenicity. We open source the HER2 binders and report the measured binding affinities. These results unlock a path to accelerated drug creation for novel therapeutic targets using generative AI combined with high-throughput experimentation.
Neutrophils are rapidly recruited from the peripheral blood to the inflammatory site to initiate inflammatory response against pathogenic infections. The process to recruit neutrophils must be properly regulated since the abnormal accumulation of neutrophils can cause organ damage and dysfunction. The acute respiratory distress syndrome (ARDS)/acute lung injury (ALI) is a common cause of respiratory failure that is characterized by the infiltration of neutrophils and epithelial integrity disruption. Indeed, recent studies suggest a pathogenic role of neutrophils in the clinic severity of the coronavirus disease 2019 (COVID-19) ARDS. The chemokine CXCL1, which is rapidly induced by inflammatory stimuli, plays a key role in neutrophil influx during lung inflammation. The molecular basis of Cxcl1 induction is not fully understood. Here we report that TET1, a member of the ten eleven translocation (TET) methylcytosine dioxygenase protein family, displays a striking specificity in the regulation of gene expression in macrophages. RNA sequencing (RNA-seq) analysis showed that Tet1 disruption significantly altered the expression of only 48 genes that include Cxcl1 and several other genes known to be important for cell migration and trafficking in bone marrow derived macrophages (BMDMs) in response to LPS stimulation. TET1 regulates the induction of Cxcl1 by facilitating the DNA demethylation of the Cxcl1 promoter. In Tet1-/- mice, the induction of Cxcl1 was suppressed, resulting in defective neutrophil recruitment to the lung during LPS-induced acute lung injury. Our results identify a novel epigenetic mechanism that selectively controls Cxcl1 induction and neutrophil recruitment during acute lung injury.
We present a deep-learning method based on Wiener filters and U-Nets that performs image reconstruction in systems with spatially-varying aberrations. We train on simulated microscopy measurements and test on experimental data, demonstrating high resolution reconstructions.
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