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T cells respond swiftly, specifically, sensitively, and robustly to cognate antigens presented on the surface of antigen presenting cells. Existing microscopic models capture various aspects of early T-cell antigen receptor (TCR) signaling at the molecular level. However, none of these models account for the totality of the data, impeding our understanding of early T-cell activation. Here, we study early TCR signaling using Bayesian metamodeling, an approach for systematically integrating multiple partial models into a metamodel of a complex system. We inform the partial models using multiple published super-resolution microscopy datasets. Collectively, these datasets describe the spatiotemporal organization, activity, interactions, and dynamics of TCR, CD45 and Lck signaling molecules in the early-forming immune synapse, and the concurrent membrane alterations. The resulting metamodel accounts for a distinct nanoscale dynamic pattern that could not be accounted for by any of the partial models on their own: a ring of phosphorylated TCR molecules, enriched at the periphery of early T cell contacts and confined by a proximal ring of CD45 molecules. The metamodel suggests this pattern results from limited activity range for the Lck molecules, acting as signaling messengers between kinetically-segregated TCR and CD45 molecules. We assessed the potential effect of Lck activity range on TCR phosphorylation and robust T cell activation for various pMHC:TCR association strengths, in the specific setting of an initial contact. We also inspected the impact of localized Lck inhibition via Csk recruitment to pTCRs, and that of splicing isoforms of CD45 on kinetic segregation. Due to the inherent scalability and adaptability of integrating independent partial models via Bayesian metamodeling, this approach can elucidate additional aspects of cell signaling and decision making.

Nucleocytoplasmic transport is mediated by the interaction of transport factors (TFs) with disordered phenylalanine-glycine (FG) repeats that fill the central channel of the nuclear pore complex (NPC). However, the mechanism by which TFs rapidly diffuse through multiple FG repeats without compromising NPC selectivity is not yet fully understood. In this study, we build on our recent NMR investigations showing that FG repeats are highly dynamic, flexible, and rapidly exchanging among TF interaction sites. We use unbiased long timescale all-atom simulations on the Anton supercomputer, combined with extensive enhanced sampling simulations and NMR experiments, to characterize the thermodynamic and kinetic properties of FG repeats and their interaction with a model transport factor. Both the simulations and experimental data indicate that FG repeats are highly dynamic random coils, lack intrachain interactions, and exhibit significant entropically driven resistance to spatial confinement. We show that the FG motifs reversibly slide in and out of multiple TF interaction sites, transitioning rapidly between a strongly interacting state and a weakly interacting state, rather than undergoing a much slower transition between strongly interacting and completely noninteracting (unbound) states. In the weakly interacting state, FG motifs can be more easily displaced by other competing FG motifs, providing a simple mechanism for rapid exchange of TF/FG motif contacts during transport. This slide-and-exchange mechanism highlights the direct role of the disorder within FG repeats in nucleocytoplasmic transport, and resolves the apparent conflict between the selectivity and speed of transport.

We report a patient who presented with congenital hypotonia, hypoventilation, and cerebellar histopathological alterations. Exome analysis revealed a homozygous mutation in the initiation codon of the NME3 gene, which encodes an NDP kinase. The initiation-codon mutation leads to deficiency in NME3 protein expression. NME3 is a mitochondrial outer-membrane protein capable of interacting with MFN1/2, and its depletion causes dysfunction in mitochondrial dynamics. Consistently, the patient’s fibroblasts were characterized by a slow rate of mitochondrial dynamics, which was reversed by expression of wild-type or catalytic-dead NME3. Moreover, glucose starvation caused mitochondrial fragmentation and cell death in the patient’s cells. The expression of wild-type and catalytic-dead but not oligomerization-attenuated NME3 restored mitochondrial elongation. However, only wild-type NME3 sustained ATP production and viability. Thus, the separate functions of NME3 in mitochondrial fusion and NDP kinase cooperate in metabolic adaptation for cell survival in response to glucose starvation. Given the critical role of mitochondrial dynamics and energy requirements in neuronal development, the homozygous mutation in NME3 is linked to a fatal mitochondrial neurodegenerative disorder.

Proteins are active, flexible machines that perform a range of different functions. Innovative experimental approaches may now provide limited partial information about conformational changes along motion pathways of proteins. There is therefore a need for computational approaches that can efficiently incorporate prior information into motion prediction schemes. In this paper, we present PathRover, a general setup designed for the integration of prior information into the motion planning algorithm of rapidly exploring random trees (RRT). Each suggested motion pathway comprises a sequence of low-energy clash-free conformations that satisfy an arbitrary number of prior information constraints. These constraints can be derived from experimental data or from expert intuition about the motion. The incorporation of prior information is very straightforward and significantly narrows down the vast search in the typically high-dimensional conformational space, leading to dramatic reduction in running time. To allow the use of state-of-the-art energy functions and conformational sampling, we have integrated this framework into Rosetta, an accurate protocol for diverse types of structural modeling. The suggested framework can serve as an effective complementary tool for molecular dynamics, Normal Mode Analysis, and other prevalent techniques for predicting motion in proteins. We applied our framework to three different model systems. We show that a limited set of experimentally motivated constraints may effectively bias the simulations toward diverse predicates in an outright fashion, from distance constraints to enforcement of loop closure. In particular, our analysis sheds light on mechanisms of protein domain swapping and on the role of different residues in the motion.

Abstract Motivation Molecular dynamics (MD) simulations enable the study of complex biomolecular processes by integrating system forces over time, but their computational inefficiency limits application at relevant scales. Enhanced sampling methods often sacrifice kinetic detail and require prior knowledge of the energy landscape. Results We developed the temporally multiscale prediction (TEMPO) Integrator, significantly reducing the number of force evaluations per simulated time unit by predicting forces at progressively larger intervals, thus boosting force-call efficiency. We incorporated the TEMPO integrator in a multiscale Brownian dynamics (MSBD) simulation tool. Compared with standard Brownian dynamics using the Euler-Maruyama integrator, our benchmarks of MSBD demonstrated 27- to 32-fold efficiency improvements for intrinsically disordered protein models and a seven-fold gain for nucleocytoplasmic transport through the nuclear pore complex (NPC), a critical cellular process in health and disease. Unlike conventional enhanced sampling, MSBD preserves kinetic properties, such as reaction rates, without relying on prior system knowledge or predefined reaction coordinates. By leveraging the inherently multiscale structure of energy landscapes, MSBD facilitates rapid molecular simulations while maintaining their accuracy. TEMPO’s flexible framework is generalizable to various biomolecular systems and could complement existing enhanced sampling methods, facilitating efficient exploration of energy landscapes or complex dynamical processes. Availability and implementation https://github.com/ravehlab/tempo.

Mechanical force controls fundamental cellular processes in health and disease, and increasing evidence shows that the nucleus both experiences and senses applied forces. Such forces can lead to the nuclear translocation of proteins, but whether force controls nucleocytoplasmic transport, and how, remains unknown. Here we show that nuclear forces differentially control passive and facilitated nucleocytoplasmic transport, setting the rules for the mechanosensitivity of shuttling proteins. We demonstrate that nuclear force increases permeability across nuclear pore complexes, with a dependence on molecular weight that is stronger for passive than for facilitated diffusion. Owing to this differential effect, force leads to the translocation of cargoes into or out of the nucleus within a given range of molecular weight and affinity for nuclear transport receptors. Further, we show that the mechanosensitivity of several transcriptional regulators can be both explained by this mechanism and engineered exogenously by introducing appropriate nuclear localization signals. Our work unveils a mechanism of mechanically induced signalling, probably operating in parallel with others, with potential applicability across signalling pathways. Andreu et al. show that force regulates nucleocytoplasmic transport by weakening the permeability barrier of nuclear pore complexes, affecting passive and facilitated diffusion in different ways.

Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions.

Single-cell and spatial omics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations or phenotypes. However, cell annotations are inherently ambiguous, as discrete labels with subjective interpretations are assigned to heterogeneous cell populations on the basis of noisy, sparse and high-dimensional data. Here we developed Annotatability, a framework for identifying annotation mismatches and characterizing biological data structure by monitoring the dynamics and difficulty of training a deep neural network over such annotated data. Following this, we developed a signal-aware graph embedding method that enables downstream analysis of biological signals. This embedding captures cellular communities associated with target signals. Using Annotatability, we address key challenges in the interpretation of genomic data, demonstrated over eight single-cell RNA sequencing and spatial omics datasets, including identifying erroneous annotations and intermediate cell states, delineating developmental or disease trajectories, and capturing cellular heterogeneity. These results underscore the broad applicability of annotation-trainability analysis via Annotatability for unraveling cellular diversity and interpreting collective cell behaviors in health and disease. The Annotatability framework analyzes neural network training dynamics to interpret single-cell and spatial omics data. It identifies erroneous annotations and ambiguous cell states, infers trajectories from binary labels and enables signal-aware analysis.

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.

We present a general framework for the generation, alignment, comparison, and hybridization of motion pathways between two known protein conformations. The framework, which is rooted in probabilistic motion-planning techniques in robotics, allows for the efficient generation of collision-free motion pathways, while considering a wide range of degrees of freedom involved in the motion. Within the framework, we provide the means to hybridize pathways, thus producing, the motion pathway of the lowest energy barrier out of the many pathways proposed by our algorithm. This method for comparing and hybridizing pathways is modular, and may be used within the context of molecular dynamics and Monte Carlo simulations. The framework was implemented within the Rosetta software suite, where the protein is represented in atomic detail. The K-channels switch between open and closed conformations, and we used the overall framework to investigate this transition. Our analysis suggests that channel-opening may follow a three-phase pathway. First, the channel unlocks itself from the closed state; second, it opens; and third, it locks itself in the open conformation. A movie that depicts the proposed pathway is available in the Supplementary Material (Movie S1) and at http://www.cs.tau.ac.il/(angela/SuppKcsA.html.