Bond-centric modular design of protein assemblies.

Directional interactions that generate regular coordination geometries are a powerful means of guiding molecular and colloidal self-assembly, but implementing such high-level interactions with proteins remains challenging due to their complex shapes and intricate interface properties. Here we describe a modular approach to protein nanomaterial design inspired by the rich chemical diversity that can be generated from the small number of atomic valencies. We design protein building blocks using deep learning-based generative tools, incorporating regular coordination geometries and tailorable bonding interactions that enable the assembly of diverse closed and open architectures guided by simple geometric principles.

Artificial Intelligence Algorithm Predicts Response to Immune Checkpoint Inhibitors.

Purpose: Cancer treatment has been revolutionized by immune checkpoint inhibitors (ICI). However, a subset of patients do not respond and/or they experience significant adverse events. Attempts to integrate reliable biomarkers of ICI response as part of standard care have been hampered by limited generalizability.

LetA defines a structurally distinct transporter family involved in lipid trafficking.

Membrane transport proteins translocate diverse cargos, ranging from small sugars to entire proteins, across cellular membranes. A few structurally distinct protein families have been described that account for most of the known membrane transport processes. However, many membrane proteins with predicted transporter functions remain uncharacterized.

Cryo-ET reveals the in situ architecture of the polar tube invasion apparatus from microsporidian parasites.

Microsporidia are divergent fungal pathogens that employ a unique harpoon-like apparatus called the polar tube (PT) to invade host cells. The long PT is fired out of the microsporidian spore over the course of just a few hundred milliseconds. Once fired, the PT is thought to pierce the plasma membrane of a target cell and act as a conduit for the transfer of the parasite into the host cell, which initiates infection.

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features from small image patches (tiles). Leiden community detection groups tiles into histomorphological phenotype clusters (HPCs).

Self supervised artificial intelligence predicts poor outcome from primary cutaneous squamous cell carcinoma at diagnosis.

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions.

Quantitative and Morphology-Based Deep Convolutional Neural Network Approaches for Osteosarcoma Survival Prediction in the Neoadjuvant and Metastatic Settings.

Purpose: Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep-learning strategies on histology samples to predict outcome for osteosarcoma in the neoadjuvant setting.

Experimental Design: Our study relies on a training cohort from New York University (NYU; New York, NY) and an external cohort from Charles University (Prague, Czechia).

MetFinder: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models.

As efforts to study the mechanisms of melanoma metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate the effects on tumor burden resulting from specific interventions. We show that automated quantification of tumor content from whole slide images is a compelling solution to assess in vivo experiments. In order to increase the outflow of data collection from preclinical studies, we assembled a large dataset with annotations and trained a deep neural network for the quantitative analysis of melanoma tumor content on histopathological sections of murine models.

Cryo-ET reveals the architecture of the polar tube invasion apparatus from microsporidian parasites.

Microsporidia are divergent fungal pathogens that employ a harpoon-like apparatus called the polar tube (PT) to invade host cells. The PT architecture and its association with neighboring organelles remain poorly understood. Here, we use cryo-electron tomography to investigate the structural cell biology of the PT in dormant spores from the human-infecting microsporidian species, .

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles.

Self-Supervised Learning Reveals Clinically Relevant Histomorphological Patterns for Therapeutic Strategies in Colon Cancer.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to extract features from small image patches. Leiden community detection then grouped tiles into histomorphological phenotype clusters (HPCs).

Conformational states of the microtubule nucleator, the γ-tubulin ring complex.

Microtubules (MTs) perform essential functions in the cell, and it is critical that they are made at the correct cellular location and cell cycle stage. This nucleation process is catalyzed by the γ-tubulin ring complex (γ-TuRC), a cone-shaped protein complex composed of over 30 subunits. Despite recent insight into the structure of vertebrate γ-TuRC, which shows that its diameter is wider than that of a MT, and that it exhibits little of the symmetry expected for an ideal MT template, the question of how γ-TuRC achieves MT nucleation remains open.

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma.

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up.

Unlatching of the stem domains in the Staphylococcus aureus pore-forming leukocidin LukAB influences toxin oligomerization.

Staphylococcus aureus (S. aureus) is a serious global pathogen that causes a diverse range of invasive diseases. S.

Protein-protein interactions in the Mla lipid transport system probed by computational structure prediction and deep mutational scanning.

The outer membrane (OM) of Gram-negative bacteria is an asymmetric bilayer that protects the cell from external stressors, such as antibiotics. The Mla transport system is implicated in the Maintenance of OM Lipid Asymmetry by mediating retrograde phospholipid transport across the cell envelope. Mla uses a shuttle-like mechanism to move lipids between the MlaFEDB inner membrane complex and the MlaA-OmpF/C OM complex, via a periplasmic lipid-binding protein, MlaC.

Structure and mechanism of the bacterial lipid ABC transporter, MlaFEDB.

The cell envelope of Gram-negative bacteria is composed of an inner membrane, outer membane, and an intervening periplasmic space. How the outer membrane lipids are trafficked and assembled there, and how the asymmetry of the outer membrane is maintained is an area of intense research. The Mla system has been implicated in the maintenance of lipid asymmetry in the outer membrane, and is generally thought to drive the removal of mislocalized phospholipids from the outer membrane and their retrograde transport to the inner membrane.

Role of Ring6 in the Function of the E. coli MCE Protein LetB.

LetB is a tunnel-forming protein found in the cell envelope of some double-membraned bacteria, and is thought to be important for the transport of lipids between the inner and outer membranes. In Escherichia coli the LetB tunnel is formed from a stack of seven rings (Ring1 - Ring7), in which each ring is composed of a homo-hexameric assembly of MCE domains. The primary sequence of each MCE domain of the LetB protein is substantially divergent from the others, making each MCE ring unique in nature.

Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas.

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. In this study, we utilize two distinct and complementary machine-learning methods of analyzing whole-slide images for predicting mutated BRAF. In the first method, whole-slide images of melanomas from 256 patients were used to train a deep convolutional neural network to develop a fully automated model that first selects for tumor-rich areas (area under the curve = 0.

Targeting allostery in the Dynein motor domain with small molecule inhibitors.

Cytoplasmic dyneins are AAA (ATPase associated with diverse cellular activities) motor proteins responsible for microtubule minus-end-directed intracellular transport. Dynein's unusually large size, four distinct nucleotide-binding sites, and conformational dynamics pose challenges for the design of potent and selective chemical inhibitors. Here we use structural approaches to develop a model for the inhibition of a well-characterized S.

HEAL: an automated deep learning framework for cancer histopathology image analysis.

Motivation: Digital pathology supports analysis of histopathological images using deep learning methods at a large-scale. However, applications of deep learning in this area have been limited by the complexities of configuration of the computational environment and of hyperparameter optimization, which hinder deployment and reduce reproducibility.

Results: Here, we propose HEAL, a deep learning-based automated framework for easy, flexible and multi-faceted histopathological image analysis.