Cytoskeletal actin dynamics is essential for T cell activation. Here, we show evidence that the binding kinetics of the antigen engaging the T cell receptor influences the nanoscale actin organization and mechanics of the immune synapse. Using an engineered T cell system expressing a specific T cell receptor and stimulated by a range of antigens, we found that the peak force experienced by the T cell receptor during activation was independent of the unbinding kinetics of the stimulating antigen. Conversely, quantification of the actin retrograde flow velocity at the synapse revealed a striking dependence on the antigen unbinding kinetics. These findings suggest that the dynamics of the actin cytoskeleton actively adjusted to normalize the force experienced by the T cell receptor in an antigen-specific manner. Consequently, tuning actin dynamics in response to antigen kinetics may thus be a mechanism that allows T cells to adjust the lengthscale and timescale of T cell receptor signaling.

Cells tightly regulate their contents. Still, nonspecific Coulombic interactions between cationic molecules and anionic membrane components can lead to adventitious endocytosis. Here, we characterize this process in a natural system. To do so, we create variants of human pancreatic ribonuclease (RNase 1) that differ in net molecular charge. By conjugating a small-molecule latent fluorophore to these variants and using flow cytometry, we are able to determine the kinetic mechanism for RNase 1 internalization into live human cells. We find that internalization increases with solution concentration and is not saturable. Internalization also increases with time to a steady-state level, which varies linearly with molecular charge. In contrast, the rate constant for internalization (t1/2 = 2 h) is independent of charge. We conclude that internalization involves an extracellular equilibrium complex between the cationic proteins and abundant anionic cell-surface molecules, followed by rate-limiting internalization. The enhanced internalization of more cationic variants of RNase 1 is, however, countered by their increased affinity for the cytosolic ribonuclease inhibitor protein, which is anionic. Thus, Coulombic forces mediate extracellular and intracellular equilibria in a dichotomous manner that both endangers cells and defends them from the potentially lethal enzymatic activity of ribonucleases.

DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo's unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.

Ultrafast diffraction at X-ray free-electron lasers (XFELs) has the potential to yield new insights into important biological systems that produce radiation-sensitive crystals. An unavoidable feature of the `diffraction before destruction' nature of these experiments is that images are obtained from many distinct crystals and/or different regions of the same crystal. Combined with other sources of XFEL shot-to-shot variation, this introduces significant heterogeneity into the diffraction data, complicating processing and interpretation. To enable researchers to get the most from their collected data, a toolkit is presented that provides insights into the quality of, and the variation present in, serial crystallography data sets. These tools operate on the unmerged, partial intensity integration results from many individual crystals, and can be used on two levels: firstly to guide the experimental strategy during data collection, and secondly to help users make informed choices during data processing.

Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. It is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis.

This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Correcting Output Coding (ECOC), which uses a code matrix to decompose a multi-class problem into multiple binary problems. ECOC for multi-class classification hinges on the design of the code matrix. We propose to explore the distribution of data classes and optimize both the composition and the number of base learners to design an effective and compact code matrix. Two real world applications are studied: (1) the holistic recognition (i.e., recognition without segmentation) of touching handwritten numeral pairs and (2) the classification of cancer tissue types based on microarray gene expression data. The results show that the proposed DECOC is able to deliver competitive accuracy compared with other ECOC methods, using parsimonious base learners than the pairwise coupling (one-vs-one) decomposition scheme. With a rejection scheme defined by a simple robustness measure, high reliabilities of around 98% are achieved in both applications.

The ability of naturally occurring proteins to change conformation in response to environmental changes is critical to biological function. Although there have been advances in the de novo design of stable proteins with a single, deep free-energy minimum, the design of conformational switches remains challenging. We present a general strategy to design pH-responsive protein conformational changes by precisely preorganizing histidine residues in buried hydrogen-bond networks. We design homotrimers and heterodimers that are stable above pH 6.5 but undergo cooperative, large-scale conformational changes when the pH is lowered and electrostatic and steric repulsion builds up as the network histidine residues become protonated. The transition pH and cooperativity can be controlled through the number of histidine-containing networks and the strength of the surrounding hydrophobic interactions. Upon disassembly, the designed proteins disrupt lipid membranes both in vitro and after being endocytosed in mammalian cells. Our results demonstrate that environmentally triggered conformational changes can now be programmed by de novo protein design.

Sculpting a flat patch of membrane into an endocytic vesicle requires curvature generation on the cell surface, which is the primary function of the endocytosis machinery. Using super-resolved live cell fluorescence imaging, we demonstrate that curvature generation by individual clathrin-coated pits can be detected in real time within cultured cells and tissues of developing organisms. Our analyses demonstrate that the footprint of clathrin coats increases monotonically during the formation of pits at different levels of plasma membrane tension. These findings are only compatible with models that predict curvature generation at the early stages of endocytic clathrin pit formation. We also found that CALM adaptors associated with clathrin plaques form clusters, whereas AP2 distribution is more homogenous. Considering the curvature sensing and driving roles of CALM, we propose that CALM clusters may increase the strain on clathrin lattices locally, eventually giving rise to rupture and subsequent pit completion at the edges of plaques.

Understanding molecular mechanisms of cellular pathways requires knowledge of the identities of participating proteins, their cellular localization and their 3D structures. Contemporary workflows typically require multiple techniques to identify target proteins, track their localization using fluorescence microscopy, followed by in vitro structure determination. To identify mammal-specific sperm proteins and understand their functions, we developed a visual proteomics workflow to directly address these challenges. Our in situ cryo-electron tomography and subtomogram averaging provided 6.0 Å resolution reconstructions of axonemal microtubules and their associated proteins. The well-resolved secondary and tertiary structures allowed us to computationally match, in an unbiased manner, novel densities in our 3D reconstruction maps with 21,615 AlphaFold2-predicted protein models of the mouse proteome. We identified Tektin 5, CCDC105 and SPACA9 as novel microtubule inner proteins that form an extensive network crosslinking the lumen of microtubule and existing proteins. Additional biochemical and mass spectrometry analyses helped validate potential candidates. The novel axonemal sperm structures identified by this approach form an extensive interaction network within the lumen of microtubules, suggesting they have a role in the mechanical and elastic properties of the microtubule filaments required for the vigorous beating motions of flagella.