Daily Podcast 2026-04-29
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Deep Dive (5)
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- [2]arXiv:2604.25244v1 Announce Type: new Abstract: Protein dynamics underlie many biological functions, yet remain difficult to characterize due to the high computational cost of molecular dynamics simulations and the scarcity of dynamic structural data. This survey reviews recent advances in artificial intelligence for protein dynamics from three perspecti...
- [3]arXiv:2604.24805v1 Announce Type: cross Abstract: Modern machine learning optimizes for accuracy without explicitly accounting for internal computational cost, even though physical and biological systems operate under intrinsic energy constraints. We evaluate energy-aware learning across 2,203 experiments spanning vision, text, neuromorphic, and physiolo...
- [4]First-principles molecular simulations provide fundamental insights into the structure-property relationships of materials, but their high computational cost limits large-scale applications. With the advent of machine learning interatomic potentials (MLIPs), it has reduced computational costs by enabling large-scale dynamic simulations with accuracy compa...
- [5]Human leukocyte antigen (HLA) class I molecules are highly polymorphic, restricting peptide binding to narrow sequence subsets. Designing peptides that bind multiple HLA supertypes-termed superbinders-offers a promising strategy for broad-spectrum T cell vaccines and immunotherapies. Here, we present superHLA, a computational framework that combines Marko...
Also collected (15)
- [6]Amyloid fibrils, a form of peptide aggregates, are associated with multiple diseases and hinder the development of therapeutics. The experimental characterization of aggregating peptides is resource-intensive, and data are scarce, limiting the development of accurate models. We present a deep-learning model, PALM (Predicting Aggregation with Language Mode...
- [7]INTRODUCTION: Predicting drug-target interactions remains a significant challenge in drug development and lead optimization. Recent advances have leveraged machine learning algorithms to model drug-target interactions from molecular and sequence data. MATERIALS AND METHODS: In this work, we use Evolutionary Scale Modeling (ESM-3) to construct a transforme...
- [8]Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physics-guided graph neural network (GNN) model that predicts Zn K-edge X-ray spectroscopy (XAS) spectr...
- [9]OBJECTIVES: The neurotrophin receptor TrkA is a clinically and genetically validated target in pain signaling. Anti-nerve growth factor (NGF) monoclonal antibodies have shown clinical efficacy but with side effects such as rapidly progressing osteoarthritis limiting their use, potentially mediated via inhibition of NGF/p75NTR signaling. Therefore, we soug...
- [10]In adjuvant research, which has long relied on experience and trial and error, advancements in technologies in broad areas, including artificial intelligence (AI)/machine learning, enabling data-driven research, have supported the introduction of more evidence-based, efficient approaches. Data-driven research, including systems biology in vaccinology ('sy...
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- [13]Representation learning is an emerging paradigm for deriving phenotypes from complex measurements (e.g., imaging) for genetic discovery. However, the learning dynamics of deep neural networks, especially the evolution of representations during training, while of interest in representation learning, were insufficiently investigated in the context of geneti...
- [14]Accurate enzyme function annotation remains a major bottleneck in genome analysis despite the rapid expansion of available protein sequence and structure data. Most existing methods rely on sequence similarity or machine-learning representations, which often perform poorly for proteins with low sequence identity or convergent evolutionary histories. Becau...
- [15]Viruses are key drivers of microbial ecology and evolution, yet their study is hindered due to challenges in culturing. Traditional gene-centric methods, which focus on a few hallmark genes like for capsids, miss much of the viral genome, leaving key viral proteins and functions undiscovered. Here, we introduce two powerful annotation-free metrics, V-scor...
- [16]Chemotherapy is essential for cancer treatment but may cause adverse events requiring emergency department visits and hospitalizations, placing substantial burdens on patients and healthcare systems. Existing approaches to detect these events often rely on structured electronic health records (EHR) data, which incompletely capture patients' symptom trajec...
- [17]Both autophagy and heat shock proteins (HSPs) play dual roles in viral infections, yet their coordination in antiviral defense remains unclear. Here, we show that a cytosolic small heat shock protein (AcsHSP) and a type II J-domain protein (AcDNAJB13) from areca palm interact with the coat protein (CP) of areca palm velarivirus 1 (APV1) independently of H...
- [18]Borrelia recurrentis, the agent of louse-borne relapsing fever, causes a poverty-associated, infectious disease of high mortality. Here, we identified and characterized five Complement targeting and Host Interacting proteins, ChiA to ChiE displaying immunomodulatory functions. Almost all Chi homologs inhibit complement activation by direct binding of key ...
- [19]arXiv:2507.14245v2 Announce Type: replace-cross Abstract: Nanomaterial-protein interactions (NPI) are pivotal to realizing the therapeutic and diagnostic potential of nanomaterials. Although AI promises to accelerate mechanistic understanding and enable rational nanomaterial design, robust generalization to unseen nanomaterials or proteins remains unreso...
- [20]arXiv:2604.24877v1 Announce Type: cross Abstract: Controlling illumination in images is essential for photography and visual content creation. While closed-source models have demonstrated impressive illumination control, open-source alternatives either require heavy control inputs like depth maps or do not release their data and code. We present a fully ...