Infections have long been implicated as causative factors in Alzheimer's disease (AD). Multiple studies have further suggested a key role for herpesviruses, such as cytomegalovirus (CMV). Using transgenic 3xTg-AD mice, we demonstrate that systemic infection with the β-herpesvirus murine CMV (MCMV) accelerates the development of cognitive decline, tauopathy and synaptic loss in the hippocampus, all of which are key features of AD. Accelerated disease progression after infection was associated with substantial lymphocyte infiltration into the brain, dominated by MCMV-specific effector memory CD8+ T cells expressing CXCR3. T cell receptor analyses revealed that clonally diverse virus-specific CD8+ T cells were selectively recruited into the brain during the development of AD. T cell depletion or treatment with the antiviral drug valganciclovir during chronic infection reduced lymphocytic infiltrates in the brain and reversed cognitive decline. These data provide a mechanistic link between chronic viral infections and the development of AD.

Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular foundation models spanning diverse tasks, including structure prediction, generative protein design, and fixed backbone sequence design. We use AtomWorks to train RosettaFold-3 (RF3), a structure prediction network capable of predicting arbitrary biomolecular complexes with an improved treatment of chirality that narrows the performance gap between closed-source AlphaFold3 (AF3) and existing open-source implementations. We expect that AtomWorks will accelerate the next generation of open-source biomolecular machine learning models and that RF3 will be broadly useful as a structure prediction tool. To this end, we release the AtomWorks framework (https://github.com/RosettaCommons/atomworks), together with curated training data, code and model weights for RF3 (https://github.com/RosettaCommons/modelforge) under a permissive BSD license.

Protein-protein interactions (PPIs) are essential for biological function. Coevolutionary analysis and deep-learning (DL)-based protein structure prediction have enabled comprehensive PPI identification in bacteria and yeast, but these approaches have had limited success for the more complex human proteome. We overcame this challenge by enhancing the coevolutionary signals with sevenfold-deeper multiple sequence alignments harvested from 30 petabytes of unassembled genomic data and developing a new DL network trained on augmented datasets of domain-domain interactions from 200 million predicted protein structures. We systematically screened 200 million human protein pairs and predicted 17,849 interactions with an expected precision of 90%, of which 3631 interactions were not identified in previous experimental screens. Three-dimensional models of these predicted interactions provide numerous hypotheses about protein function and mechanisms of human diseases.

α/β-hydrolase domain-containing protein 11 (ABHD11) is a mitochondrial hydrolase that maintains the catalytic function of α-ketoglutarate dehydrogenase (α-KGDH), and its expression in CD4 + T-cells has been linked to remission status in rheumatoid arthritis (RA). However, the importance of ABHD11 in regulating T-cell metabolism and function is yet to be explored. Here, we show that pharmacological inhibition of ABHD11 dampens cytokine production by human and mouse T-cells. Mechanistically, the anti-inflammatory effects of ABHD11 inhibition are attributed to increased 24,25-epoxycholesterol (24,25-EC) biosynthesis and subsequent liver X receptor (LXR) activation, which arise from a compromised TCA cycle. The impaired cytokine profile established by ABHD11 inhibition is extended to two patient cohorts of autoimmunity. Importantly, using murine models of accelerated type 1 diabetes (T1D), we show that targeting ABHD11 suppresses cytokine production in antigen-specific T-cells and delays the onset of diabetes in vivo in female mice. Collectively, our work provides pre-clinical evidence that ABHD11 is an encouraging drug target in T-cell-mediated inflammation.

The majority of proteins must form higher-order assemblies to perform their biological functions. Despite the importance of protein quaternary structure, there are few machine learning models that can accurately and rapidly predict the symmetry of assemblies involving multiple copies of the same protein chain. Here, we address this gap by training several classes of protein foundation models, including ESM-MSA, ESM2, and RoseTTAFold2, to predict homo-oligomer symmetry. Our best model named Seq2Symm, which utilizes ESM2, outperforms existing template-based and deep learning methods. It achieves an average PR-AUC of 0.48 and 0.44 across homo-oligomer symmetries on two different held-out test sets compared to 0.32 and 0.23 for the template-based method. Because Seq2Symm can rapidly predict homo-oligomer symmetries using a single sequence as input (~ 80,000 proteins/hour), we have applied it to 5 entire proteomes and ~ 3.5 million unlabeled protein sequences to identify patterns in protein assembly complexity across biological kingdoms and species.

The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation method for assessing active site preorganization to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies ( k cat / K m ) up to 2.2x10 5 M −1 s −1 and crystal structures that closely match the design models (Cα RMSDs < 1 Å). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts in low-throughput screens with five different folds distinct from those of natural serine hydrolases. Our de novo approach provides insight into the geometric basis of catalysis and a roadmap for designing enzymes that catalyze multistep transformations.

Identification of bacterial protein-protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here we developed RoseTTAFold2-Lite, a rapid deep learning model that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1,923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer-membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

Enzymes that proceed through multistep reaction mechanisms often utilize complex, polar active sites positioned with sub-angstrom precision to mediate distinct chemical steps, which makes their de novo construction extremely challenging. We sought to overcome this challenge using the classic catalytic triad and oxyanion hole of serine hydrolases as a model system. We used RFdiffusion1 to generate proteins housing catalytic sites of increasing complexity and varying geometry, and a newly developed ensemble generation method called ChemNet to assess active site geometry and preorganization at each step of the reaction. Experimental characterization revealed novel serine hydrolases that catalyze ester hydrolysis with catalytic efficiencies (k cat /K m ) up to 3.8 x 103 M-1 s-1, closely match the design models (Cα RMSDs < 1 Å), and have folds distinct from natural serine hydrolases. In silico selection of designs based on active site preorganization across the reaction coordinate considerably increased success rates, enabling identification of new catalysts in screens of as few as 20 designs. Our de novo buildup approach provides insight into the geometric determinants of catalysis that complements what can be obtained from structural and mutational studies of native enzymes (in which catalytic group geometry and active site makeup cannot be so systematically varied), and provides a roadmap for the design of industrially relevant serine hydrolases and, more generally, for designing complex enzymes that catalyze multi-step transformations.

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.

Adenoviruses (Ads) have demonstrated significant success as replication-deficient (RD) viral vectored vaccines, as well as broad potential across gene therapy and cancer therapy. Ad vectors transduce human cells via direct interactions between the viral fiber knob and cell surface receptors, with secondary cellular integrin interactions. Ad receptor usage is diverse across the extensive phylogeny. Commonly studied human Ad serotype 5 (Ad5), and chimpanzee Ad-derived vector “ChAdOx1” in licensed ChAdOx1 nCoV-19 vaccine, both form primary interactions with the coxsackie and adenovirus receptor (CAR), which is expressed on human epithelial cells and erythrocytes. CAR usage is suboptimal for targeted gene delivery to cells with low/negative CAR expression, including human dendritic cells (DCs) and vascular smooth muscle cells (VSMCs). We evaluated the performance of an RD Ad5 vector pseudotyped with the fiber knob of human Ad serotype 49, termed Ad5/49K vector. Ad5/49K demonstrated superior transduction of murine and human DCs over Ad5, which translated into significantly increased T cell immunogenicity when evaluated in a mouse cancer vaccine model using 5T4 tumor-associated antigen. Additionally, Ad5/49K exhibited enhanced transduction of primary human VSMCs. These data highlight the potential of Ad5/49K vector for both vascular gene therapy applications and as a potent vaccine vector.

CD4 + T cells are central to adaptive immunity. Their role in cross -protection in viral infections such as influenza and severe acute respiratory syndrome (SARS) is well documented; however, molecular rules governing T cell receptor (TCR) engagement of peptide -human leukocyte antigen (pHLA) class II are less understood. Here, we exploit an aspect of HLA class II presentation, the peptide -flanking residues (PFRs), to "tune"CD4 + T cell responses within an in vivo model system of influenza. Using a recombinant virus containing targeted substitutions at immunodominant HLA-DR1 epitopes, we demonstrate limited weight loss and improved clinical scores after heterosubtypic re -challenge. We observe enhanced protection linked to lung -derived influenza -specific CD4 + and CD8 + T cells prior to re -infection. Structural analysis of the ternary TCR:pHLA complex identifies that flanking amino acids influence side chains in the core 9-mer peptide, increasing TCR affinity. Augmentation of CD4 + T cell immunity is achievable with a single mutation, representing a strategy to enhance adaptive immunity that is decoupled from vaccine modality.

ABSTRACT There are no licensed vaccines for human cytomegalovirus (HCMV), and current antiviral drugs that target viral proteins are toxic and prone to resistance. Targeting host pathways essential for virus replication provides an alternate strategy that may reduce opportunities for drug resistance to occur. Oxidative stress is triggered by numerous viruses including HCMV. Peroxynitrite is a reactive nitrogen species that is formed during oxidative stress. Herein, we identified that HCMV rapidly induces the generation of intracellular peroxynitrite upon infection in a manner partially dependent upon xanthine oxidase generation. Peroxynitrite promoted HCMV infection in both cell-free and cell-associated infection systems in multiple cell types. Inhibiting peroxynitrite within the first 24 hours of infection prevented HCMV replication and peroxynitrite promoted cell entry and pp65 translocation into the host cell nuclei. Furthermore, using the murine cytomegalovirus model, we demonstrated that antagonizing peroxynitrite significantly reduces cytomegalovirus replication and pathogenesis in vivo . Overall, our study highlights a proviral role for peroxynitrite in CMV infection and implies that RNS and/or the mechanisms that induce their production could be targeted as a novel strategy to inhibit HCMV infection. IMPORTANCE Human cytomegalovirus (HCMV) causes significant disease in individuals with impaired or immature immune systems, such as transplant patients and after congenital infection. Antiviral drugs that target the virus directly are toxic and are susceptible to antiviral drug resistance due to virus mutations. An alternate strategy is to target processes within host cells that are required by the virus for replication. Herein, we show that HCMV infection triggers a highly reactive molecule, peroxynitrite, during the initial stages of infection. Peroxynitrite was required for the initial entry of the virus into the cell and promotes virus replication in multiple cell types, suggesting a broad pro-viral function. Importantly, targeting peroxynitrite dramatically inhibited cytomegalovirus replication in cells in the laboratory and in mice, suggesting that therapeutic targeting of this molecule and/or the cellular functions it regulates could represent a novel strategy to inhibit HCMV infection.

Protein-protein interactions (PPI) are essential for biological function. Recent advances in coevolutionary analysis and Deep Learning (DL) based protein structure prediction have enabled comprehensive PPI identification in bacterial and yeast proteomes, but these approaches have limited success to date for the more complex human proteome. Here, we overcome this challenge by 1) enhancing the coevolutionary signals with 7-fold deeper multiple sequence alignments harvested from 30 petabytes of unassembled genomic data, and 2) developing a new DL network trained on augmented datasets of domain-domain interactions from 200 million predicted protein structures. These advancements allow us to systematically screen through 200 million human protein pairs and predict 18,316 PPIs with an expected precision of 90%, among which 5,578 are novel predictions. 3D models of these predicted PPIs nearly triple the number of human PPIs with accurate structural information, providing numerous insights into protein function and mechanisms of human diseases. ### Competing Interest Statement The authors have declared no competing interest.

Mucosal surfaces that line the respiratory, gastrointestinal and genitourinary tracts are the major interfaces between the immune system and the environment. Their unique immunological landscape is characterized by the necessity of balancing tolerance to commensal microorganisms and other innocuous exposures against protection from pathogenic threats such as viruses. Numerous pathogenic viruses, including herpesviruses and retroviruses, exploit this environment to establish chronic infection. Effector and regulatory T-cell populations, including effector and resident memory T cells, play instrumental roles in mediating the transition from acute to chronic infection, where a degree of viral replication is tolerated to minimize immunopathology. Persistent antigen exposure during chronic viral infection leads to the evolution and divergence of these responses. In this review, we discuss advances in the understanding of mucosal T-cell immunity during chronic viral infections and how features of T-cell responses develop in different chronic viral infections of the mucosa. We consider how insights into T-cell immunity at mucosal surfaces could inform vaccine strategies: not only to protect hosts from chronic viral infections but also to exploit viruses that can persist within mucosal surfaces as vaccine vectors.

Identification of bacterial protein-protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep learning-based pipeline that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

Patescibacteria, also known as the candidate phyla radiation (CPR), are a diverse group of bacteria that constitute a disproportionately large fraction of microbial dark matter. Its few cultivated members, belonging mostly to Saccharibacteria, grow as epibionts on host Actinobacteria. Due to a lack of suitable tools, the genetic basis of this lifestyle and other unique features of Patescibacteira remain unexplored. Here, we show that Saccharibacteria exhibit natural competence, and we exploit this property for their genetic manipulation. Imaging of fluorescent protein-labeled Saccharibacteria provides high spatiotemporal resolution of phenomena accompanying epibiotic growth, and a transposon-insertion sequencing (Tn-seq) genome-wide screen reveals the contribution of enigmatic Saccharibacterial genes to growth on their hosts. Finally, we leverage metagenomic data to provide cutting-edge protein structure-based bioinformatic resources that support the strain Southlakia epibionticum and its corresponding host, Actinomyces israelii, as a model system for unlocking the molecular underpinnings of the epibiotic lifestyle.

Regulatory T (T-reg) cells contribute to immune homeostasis but suppress immune responses to cancer. Strategies to disrupt T-reg cell-mediated cancer immunosuppression have been met with limited clinical success, but the underlying mechanisms for treatment failure are poorly understood. By modeling T-reg cell-targeted immunotherapy in mice, we find that CD4(+) Foxp3(-) conventional T (T-conv) cells acquire suppressive function upon depletion of Foxp3(+) T-reg cells, limiting therapeutic efficacy. Foxp3(-) T-conv cells within tumors adopt a T-reg cell-like transcriptional profile upon ablation of T-reg cells and acquire the ability to suppress T cell activation and proliferation ex vivo. Suppressive activity is enriched among CD4(+) T-conv cells marked by expression of C-C motif receptor 8 (CCR8), which are found in mouse and human tumors. Upon T-reg cell depletion, CCR8(+) T-conv cells undergo systemic and intratumoral activation and expansion, and mediate IL-10-dependent suppression of antitumor immunity. Consequently, conditional deletion of Il10 within T cells augments antitumor immunity upon T-reg cell depletion in mice, and antibody blockade of IL-10 signaling synergizes with T-reg cell depletion to overcome treatment resistance. These findings reveal a secondary layer of immunosuppression by T-conv cells released upon therapeutic T-reg cell depletion and suggest that broader consideration of suppressive function within the T cell lineage is required for development of effective T-reg cell-targeted therapies.

Extensive research in well-studied animal models underscores the importance of commensal gastrointestinal (gut) microbes to animal physiology. Gut microbes have been shown to impact dietary digestion, mediate infection, and even modify behavior and cognition. Given the large physiological and pathophysiological contribution microbes provide their host, it is reasonable to assume that the vertebrate gut microbiome may also impact the fitness, health and ecology of wildlife. In accordance with this expectation, an increasing number of investigations have considered the role of the gut microbiome in wildlife ecology, health, and conservation. To help promote the development of this nascent field, we need to dissolve the technical barriers prohibitive to performing wildlife microbiome research. The present review discusses the 16S rRNA gene microbiome research landscape, clarifying best practices in microbiome data generation and analysis, with particular emphasis on unique situations that arise during wildlife investigations. Special consideration is given to topics relevant for microbiome wildlife research from sample collection to molecular techniques for data generation, to data analysis strategies. Our hope is that this article not only calls for greater integration of microbiome analyses into wildlife ecology and health studies but provides researchers with the technical framework needed to successfully conduct such investigations.

Endoscopic endonasal surgery is a medical procedure that utilizes an endoscopic video camera to view and manipulate a surgical site accessed through the nose. Despite these surgeries being video recorded, these videos are seldom reviewed or even saved in patient files due to the size and length of the video file. Editing to a manageable size may necessitate viewing 3 h or more of surgical video and manually splicing together the desired segments. We suggest a novel multi-stage video summarization procedure utilizing deep semantic features, tool detections, and video frame temporal correspondences to create a representative summarization. Summarization by our method resulted in a 98.2% reduction in overall video length while preserving 84% of key medical scenes. Furthermore, resulting summaries contained only 1% of scenes with irrelevant detail such as endoscope lens cleaning, blurry frames, or frames external to the patient. This outperformed leading commercial and open source summarization tools not designed for surgery, which only preserved 57% and 46% of key medical scenes in similar length summaries, and included 36% and 59% of scenes containing irrelevant detail. Experts agreed that on average (Likert Scale = 4) that the overall quality of the video was adequate to share with peers in its current state.