Measuring Ligand-bound Protein Complexes with Proximity Labeling: A Practical Guide.

Identifying the drug-target interactome of small molecule therapeutics is essential for understanding the full pharmacological effects of a compound. These therapies often induce changes within the cellular proteome, leading to unexpected consequences such as changes in the targets complexation state or off-target interactions between the compound and additional proteins. Currently, unbiased target-ID approaches are being used to embark on this task. Here we provide an overview of the strengths and limitations of these methods, and a practical step-by-step protocol for using the BioTAC system to assist with drug target and interactome ID.

Progressive Learning of 3D Reconstruction Network From 2D GAN Data.

This paper presents a method to reconstruct high-quality textured 3D models from single images. Current methods rely on datasets with expensive annotations; multi-view images and their camera parameters. Our method relies on GAN generated multi-view image datasets which have a negligible annotation cost. However, they are not strictly multi-view consistent and sometimes GANs output distorted images. This results in degraded reconstruction qualities. In this work, to overcome these limitations of generated datasets, we have two main contributions which lead us to achieve state-of-the-art results on challenging objects: 1) A robust multi-stage learning scheme that gradually relies more on the models own predictions when calculating losses and 2) A novel adversarial learning pipeline with online pseudo-ground truth generations to achieve fine details. Our work provides a bridge from 2D supervisions of GAN models to 3D reconstruction models and removes the expensive annotation efforts. We show significant improvements over previous methods whether they were trained on GAN generated multi-view images or on real images with expensive annotations.

A biotin targeting chimera (BioTAC) system to map small molecule interactomes in situ.

Understanding how small molecules bind to specific protein complexes in living cells is critical to understanding their mechanism-of-action. Unbiased chemical biology strategies for direct readout of protein interactome remodelling by small molecules would provide advantages over target-focused approaches, including the ability to detect previously unknown ligand targets and complexes. However, there are few current methods for unbiased profiling of small molecule interactomes. To address this, we envisioned a technology that would combine the sensitivity and live-cell compatibility of proximity labelling coupled to mass spectrometry, with the specificity and unbiased nature of chemoproteomics. In this manuscript, we describe the BioTAC system, a small-molecule guided proximity labelling platform that can rapidly identify both direct and complexed small molecule binding proteins. We benchmark the system against µMap, photoaffinity labelling, affinity purification coupled to mass spectrometry and proximity labelling coupled to mass spectrometry datasets. We also apply the BioTAC system to provide interactome maps of Trametinib and analogues. The BioTAC system overcomes a limitation of current approaches and supports identification of both inhibitor bound and molecular glue bound complexes.

A Biotin Targeting Chimera (BioTAC) System to Map Small Molecule Interactomes in situ.

Unbiased chemical biology strategies for direct readout of protein interactome remodelling by small molecules provide advantages over target-focused approaches, including the ability to detect previously unknown targets, and the inclusion of chemical off-compete controls leading to high-confidence identifications. We describe the BioTAC system, a small-molecule guided proximity labelling platform, to rapidly identify both direct and complexed small molecule binding proteins. The BioTAC system overcomes a limitation of current approaches, and supports identification of both inhibitor bound and molecular glue bound complexes.

Fine Detailed Texture Learning for 3D Meshes With Generative Models.

This paper presents a method to achieve fine detailed texture learning for 3D models that are reconstructed from both multi-view and single-view images. The framework is posed as an adaptation problem and is done progressively where in the first stage, we focus on learning accurate geometry, whereas in the second stage, we focus on learning the texture with a generative adversarial network. The contributions of the paper are in the generative learning pipeline where we propose two improvements. First, since the learned textures should be spatially aligned, we propose an attention mechanism that relies on the learnable positions of pixels. Second, since discriminator receives aligned texture maps, we augment its input with a learnable embedding which improves the feedback to the generator. We achieve significant improvements on multi-view sequences from Tripod dataset as well as on single-view image datasets, Pascal 3D+ and CUB. We demonstrate that our method achieves superior 3D textured models compared to the previous works.

Partial Convolution for Padding, Inpainting, and Image Synthesis.

Partial convolution weights convolutions with binary masks and renormalizes on valid pixels. It was originally proposed for image inpainting task because a corrupted image processed by a standard convolutional often leads to artifacts. Therefore, binary masks are constructed that define the valid and corrupted pixels, so that partial convolution results are only calculated based on valid pixels. It has been also used for conditional image synthesis task, so that when a scene is generated, convolution results of an instance depend only on the feature values that belong to the same instance. One of the unexplored applications for partial convolution is padding which is a critical component of modern convolutional networks. Common padding schemes make strong assumptions about how the padded data should be extrapolated. We show that these padding schemes impair model accuracy, whereas partial convolution based padding provides consistent improvements across a range of tasks. In this article, we review partial convolution applications under one framework. We conduct a comprehensive study of the partial convolution based padding on a variety of computer vision tasks, including image classification, 3D-convolution-based action recognition, and semantic segmentation. Our results suggest that partial convolution-based padding shows promising improvements over strong baselines.

Targeted protein degradation: Emerging concepts and protein state-specific targeting principles.

Targeted protein degraders are heterobifunctional small molecules that link a target ligand or bait to an E3-ligase binder via a chemical spacer. Upon entering the cell, these ligands trigger the formation of a ternary complex between the target protein, degrader and E3-ligase, which leads to target polyubiquitination and proteasomal degradation. In recent years, TPD has expanded rapidly as a field, becoming the modality of choice in drug discovery and chemical probe development. This has been driven by the unique pharmacology of these molecules, which allows for fast and reversible knockdown of the target protein. Recent studies have demonstrated that degraders with specificity for a defined subpopulation of a protein-of-interest can be developed, giving rise to the emerging concept of protein state-specific targeting. In this article, we review advances towards developing degraders that differentiate between target protein subpopulations based on their; activation state, oligomerization state, cellular localization state, and cell type.