Cell systems最新文献

Classical phylogenetics assumes site independence, potentially overlooking epistasis. Protein language models capture dependencies in conserved structural and functional domains across the protein universe. Here, we ask whether MSA Transformer, which takes a multiple sequence alignment (MSA) as input, captures evolutionary distance and to what extent its representations reflect epistasis in protein sequence evolution, neither of which are explicitly available during training. Systematic shuffling of natural and simulated MSAs demonstrates that the model exploits column-wise conservation to distinguish phylogenetic relationships. Using internal embeddings, we reconstruct trees that are markedly consistent with those generated by maximum likelihood inference. Applying this approach to both the RNA-dependent RNA polymerase of RNA viruses and the nucleo-cytoplasmic large DNA virus domain, we recover both established and novel evolutionary relationships. We conclude that MSA Transformer complements, rather than replaces, classical inference for more accurate histories of protein families.

The maintenance of cellular homeostasis requires tight regulation of proteome concentration and composition. To achieve this, protein production and elimination must be robustly coordinated. However, the mechanistic basis of this coordination remains unclear. Here, we address this question using quantitative live-cell imaging, computational modeling, transcriptomics, and proteomics approaches. We found that protein decay rates systematically adapt to global alterations of protein synthesis rates. This adaptation is driven by a core passive mechanism supplemented by facultative changes in mechanistic/mammalian target of rapamycin (mTOR) signaling. Passive adaptation hinges on changes in the production rate of the machinery governing protein decay and allows for partial maintenance of the cellular proteome. Sustained changes in mTOR signaling provide an additional layer of adaptation unique to naive pluripotent stem cells, allowing for near-perfect maintenance of proteome composition. Our work unravels the mechanisms protecting the integrity of mammalian proteomes upon variations in protein synthesis rates. A record of this paper's transparent peer review process is included in the supplemental information.

Intrinsically disordered proteins or regions (IDPs or IDRs) adopt diverse binding modes with different partners, ranging from coupled folding and binding to fuzzy binding and fully disordered binding. Characterizing IDR interfaces is challenging both experimentally and computationally. State-of-the-art tools such as AlphaFold multimer and AlphaFold3 can be used to predict IDR binding sites, although they are less accurate at their benchmarked confidence cutoffs. Here, we developed Disobind, a deep-learning method that predicts inter-protein contact maps and interface residues for an IDR and its partner, given their sequences. It uses sequence embeddings from the ProtT5 protein language model. Disobind outperforms state-of-the-art interface predictors for IDRs. It also outperforms AlphaFold multimer and AlphaFold3 at multiple confidence cutoffs. Combining Disobind and AlphaFold-multimer predictions further improves performance. In contrast to current methods, Disobind considers the context of the binding partner and does not depend on structures and multiple sequence alignments. Its predictions can be used to localize IDRs in large assemblies and characterize IDR-mediated interactions.

The gut microbiome plays a critical role in human health, spurring extensive research using multi-omic technologies. Although these tools offer valuable insights, they often fall short in capturing the complexity of microbial interactions that associate with disease onset, progression, and treatment. Thus, integration of multi-omics datasets with metabolic models is needed to predict associations between microbial activity and disease. Here, we automated the reconstruction of 495 metabolic and gene expression models (ME-models), overcoming the main limitation preventing the wide use of this approach. We integrated them with multi-omics data from patients with inflammatory bowel disease (IBD), identifying taxa associated with variations in amino acids, short-chain fatty acids, and pH in the gut of IBD patients. In general, this approach provides testable hypotheses of the metabolic activity of the gut microbiota, and the automated pipeline opens the opportunity to study microbial interactions in other biologically relevant settings using ME-models.

Two recent studies in Cell Systems show why protein dynamics matter for prediction. By moving beyond static structures and embracing the dynamic "jigglings and wigglings" that Richard Feynman famously described, these approaches improve accuracy in binding site predictions for flexible systems despite challenges such as sparse training data. Together, they signal a shift toward models that try to capture the full energy landscape, paving the way for deeper insights into protein function.

Protein-protein interactions are fundamental to cellular processes, yet current deep learning approaches for binding site prediction rely on static structures, limiting their accuracy for disordered or flexible regions. We introduce dynamic geometric transformer (DynamicGT), a dynamic-aware model that integrates conformational dynamics into a cooperative graph neural network (Co-GNN) with a GT. Our model encodes dynamic features at both node (atom) and edge (interaction) levels, considering bound and unbound states to improve generalization. Dynamic regulation of messages passing between core and surface residues enhances detection of critical interactions for efficient information flow. Trained on a 1-ms molecular dynamics simulation dataset and augmented with AlphaFlow-generated conformations, the model was benchmarked extensively. Evaluation on diverse datasets containing disordered, transient, and unbound structures demonstrates that incorporating dynamics within a cooperative architecture significantly improves prediction accuracy where flexibility is key while requiring substantially less data than leading static approaches.

Engineering biological systems with specified functions requires navigating an extensive design space, which is challenging to achieve with wet-lab experiments alone. To expedite the design process, mathematical modeling is typically employed to predict circuit function in silico ahead of implementation, which, when coupled with computational optimization, can be used to automatically identify promising designs. However, circuit models are inherently inaccurate, which can result in suboptimal or non-functional in vivo performance. To mitigate this, we propose combining Bayesian inference, Thompson sampling, and risk management to find optimal circuit designs. Our approach employs data from non-functional designs to estimate the distribution of model parameters and then employs risk-averse optimization to select design parameters that are expected to perform well, given parameter uncertainty and biomolecular noise. We illustrate the approach by designing adaptation circuits and genetic oscillators using real and simulated data, with models of varied complexity. A record of this paper's transparent peer review process is included in the supplemental information.

Direct reprogramming of immune cells holds promise for immunotherapy but is constrained by limited knowledge of transcription factor (TF) networks. Here, we developed REPROcode, a combinatorial single-cell screening platform to identify TF combinations for immune cell reprogramming. We first validated REPROcode by inducing type-1 conventional dendritic cells (cDC1s) with multiplexed sets of 9, 22, and 42 factors. With cDC1-enriched TFs, REPROcode enabled identification of optimal TF stoichiometry, fidelity enhancers, and regulators of cDC1 states. We then constructed an arrayed lentiviral library of 408 barcoded immune TFs to explore broader reprogramming capacity. Screening 48 TFs enriched in dendritic cell subsets yielded myeloid and lymphoid phenotypes and enabled the construction of a TF hierarchy map to guide immune reprogramming. Finally, we validated REPROcode's discovery power by inducing natural killer (NK)-like cells. This study deepens our understanding of immune transcriptional control and provides a versatile toolbox for engineering immune cells to advance immunotherapy.

Understanding antibody recognition and adaptation to viral evolution is central to vaccine and therapeutic development. Over 1,100 SARS-CoV-2 antibody structures have been resolved, marking the largest structural biology effort for a single pathogen. We present a comprehensive analysis of this landmark dataset to investigate the principles of antibody recognition and immune escape. Human immunoglobulins and camelid single-chain antibodies dominate, collectively mapping 99% of the receptor-binding domain. Despite remarkable sequence and conformational diversity, antibodies exhibit convergence in their paratope structures, revealing evolutionary constraints in epitope selection. Analyses reveal near-universal immune escape of antibodies, including all clinical monoclonals, by advanced variants such as KP3.1.1. On average, over one-third of antibody epitope residues are mutated. These findings support pervasive immune escape, underscoring the need to effectively leverage multi-epitope-targeting strategies to achieve durable immunity. To support community accessibility, we developed an interactive web server for visualization and analysis of antibody-antigen complexes and mutational data.

RNA regulation is central to tuning gene expression and is controlled by thousands of RNA-binding proteins (RBPs). While many RBPs require their full sequence to function, some act through modular domains that recruit larger regulatory complexes. Mapping these RNA-regulatory effector domains is important for understanding RBP function and designing compact RNA regulators. We developed a high-throughput recruitment assay (HT-RNA-Recruit) to identify RNA-downregulatory effector domains within human RBPs. By recruiting over 30,000 protein tiles from 367 RBPs to a reporter mRNA, we discovered over 100 RNA-downregulatory effector domains in 86 RBPs. Certain domains-for instance, KRABs-suppress gene expression upon recruitment to both DNA and RNA. We engineered inducible synthetic RNA regulators based on NANOS that can downregulate endogenous RNAs or maintain reporter expression at defined intermediate levels, as predicted by mathematical modeling. This work serves as a resource for understanding RNA regulators and expands the repertoire of RNA control tools. A record of this paper's transparent peer review process is included in the supplemental information.