Summary Ligation of the CD28 receptor on T cells provides a critical second signal alongside T cell receptor (TCR) ligation for naive T cell activation. Here we discuss the expression, structure, and biochemistry of CD28 and its ligands. CD28 signals play a key role in many T cell processes including cytoskeletal remodeling, production of cytokines, survival, and differentiation. CD28 ligation leads to unique epigenetic, transcriptional, and post-translational changes in T cells that cannot be recapitulated by TCR ligation alone. We discuss the function of CD28 and its ligands in both effector and regulatory T cells. CD28 is critical for regulatory T cell survival and the maintenance of immune homeostasis. We outline the roles that CD28 and its family members play in human disease and we review the clinical efficacy of drugs that block CD28 ligands. Despite the centrality of CD28 and its family members and ligands to immune function, many aspects of CD28 biology remain unclear. Translation of a basic understanding of CD28 function into immunomodulatory therapeutics has been uneven with both successes and failures. Such real-world results may stem from multiple factors including complex receptor-ligand interactions among CD28 family members, differences between the mouse and human CD28 families, and cell-type specific roles of CD28 family members.

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SUMMARY Regulatory T cells (Treg cells) are required for immune homeostasis. Chromatin remodeling is essential for establishing diverse cellular identities, but how the epigenetic program in Treg cells is maintained throughout the dynamic activation process remains unclear. Here we have shown that CD28 co-stimulation, an extracellular cue intrinsically required for Treg cell maintenance, induced the chromatin-modifying enzyme, Ezh2. Treg-specific ablation of Ezh2 resulted in spontaneous autoimmunity with reduced Foxp3+ cells in non-lymphoid tissues and impaired resolution of experimental autoimmune encephalomyelitis. Utilizing a model designed to selectively deplete wild-type Treg cells in adult mice co-populated with Ezh2-deficient Treg cells, Ezh2-deficient cells were destabilized and failed to prevent autoimmunity. After activation, the transcriptome of Ezh2-deficient Treg cells was disrupted, with altered expression of Treg cell lineage genes in a pattern similar to Foxp3-deficient Treg cells. These studies reveal a critical role for Ezh2 in the maintenance of Treg cell identity during cellular activation.

Regulatory T cells (Treg) play a central role in counteracting inflammation and autoimmunity. A more complete understanding of cellular heterogeneity and the potential for lineage plasticity in human Treg subsets may identify markers of disease pathogenesis and facilitate the development of optimized cellular therapeutics. To better elucidate human Treg subsets, we conducted direct transcriptional profiling of CD4+FOXP3+Helios+ thymic-derived Treg (tTreg) and CD4+FOXP3+Helios− T cells, followed by comparison to CD4+FOXP3−Helios− T conventional (Tconv) cells. These analyses revealed that the coinhibitory receptor T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) was highly expressed on tTreg. TIGIT and the costimulatory factor CD226 bind the common ligand CD155. Thus, we analyzed the cellular distribution and suppressive activity of isolated subsets of CD4+CD25+CD127lo/− T cells expressing CD226 and/or TIGIT. We observed TIGIT is highly expressed and upregulated on Treg following activation and in vitro expansion and is associated with lineage stability and suppressive capacity. Conversely, the CD226+TIGIT− population was associated with reduced Treg purity and suppressive capacity following expansion, along with a marked increase in IL-10 and effector cytokine production. These studies provide additional markers to delineate functionally distinct Treg subsets that may help direct cellular therapies and provide important phenotypic markers for assessing the role of Treg in health and disease.

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound–proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12–25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 ‘high value’ predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.

We have examined the effect of eight different protein classes (channels, GPCRs, kinases, ligases, nuclear receptors, proteases, phosphatases, transporters) on the benchmarking performance of the CANDO drug discovery and repurposing platform (http://protinfo.org/cando). The first version of the CANDO platform utilizes a matrix of predicted interactions between 48278 proteins and 3733 human ingestible compounds (including FDA approved drugs and supplements) that map to 2030 indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol (> one billion predicted interactions considered). The platform uses similarity of compound-proteome interaction signatures as indicative of similar functional behavior and benchmarking accuracy is calculated across 1439 indications/diseases with more than one approved drug. The CANDO platform yields a significant correlation (0.99, p-value < 0.0001) between the number of proteins considered and benchmarking accuracy obtained indicating the importance of multitargeting for drug discovery. Average benchmarking accuracies range from 6.2 % to 7.6 % for the eight classes when the top 10 ranked compounds are considered, in contrast to a range of 5.5 % to 11.7 % obtained for the comparison/control sets consisting of 10, 100, 1000, and 10000 single best performing proteins. These results are generally two orders of magnitude better than the average accuracy of 0.2% obtained when randomly generated (fully scrambled) matrices are used. Different indications perform well when different classes are used but the best accuracies (up to 11.7% for the top 10 ranked compounds) are achieved when a combination of classes are used containing the broadest distribution of protein folds. Our results illustrate the utility of the CANDO approach and the consideration of different protein classes for devising indication specific protocols for drug repurposing as well as drug discovery.

One of the most challenging problems in protein structure prediction is improvement of homology models (structures within 1-3 Å C ␣ rmsd of the native structure), also known as the protein structure refinement problem. It has been shown that improvement could be achieved using in vacuo energy minimization with molecular mechanics and statistically derived continuously differentiable hybrid knowledge-based (KB) potential functions. Globular proteins, however, fold and function in aqueous solution in vivo and in vitro. In this work, we study the role of solvent in protein structure refinement. Molecular dynamics in explicit solvent and energy minimization in both explicit and implicit solvent were performed on a set of 75 native proteins to test the various energy potentials. A more stringent test for refinement was performed on 729 near-native decoys for each native protein. We use a powerfully convergent energy minimization method to show that implicit solvent (GBSA) provides greater improvement for some proteins than the KB potential: 24 of 75 proteins showing an average improvement of >20% in C ␣ rmsd from the native structure with GBSA, compared to just 7 proteins with KB. Molecular dynamics in explicit solvent moved the structures further away from their native conformation than the initial, unrefined decoys. Implicit solvent gives rise to a deep, smooth potential energy attractor basin that pulls toward the native structure.energy minimization ͉ implicit solvent ͉ knowledge-based ͉ molecular dynamics ͉ explicit solvent E xperimental determination of protein structures is very expensive, costing U.S. $250,000 in 2000 (1) and $66,000 today (2) and can be a notoriously difficult task, especially for membrane proteins. With the continuing exponential growth of genome sequence data, there is an increasing need for methods that accurately compute the high-resolution native structure of a protein, for use in biological applications that include virtual ligand screening (3), structure-based protein function prediction (4) and structure-based drug design (5). Homology or template based modeling has been the most successful method for protein structure prediction in the critical assessment of protein structure prediction (CASP) experiments (6, 7). The power of this technique progressively increases as ever more structures are solved by world-wide structural genomics initiatives (8, 9). Nevertheless, obtaining a model with the same accuracy as a crystal structure is still an unsolved problem: structure refinement of a rough model (within 1-3 Å rmsd) to bring it closer to the native structure remains a major challenge (6, 10). Work on structure refinement has been ongoing for many decades, starting from the first Molecular Mechanics (MM) energy minimization (11, 12) and continuing to a recent study with knowledgebased (KB) statistically derived potentials (13). During this period many different potentials and a variety of simulation methodologies such as energy minimization, molecular dynamics, and replica exchange Mon...

A new multi-label deep neural network architecture is used to combine Infrared and mass spectra, trained on single compounds to predict functional groups, and experimentally validated on complex mixtures.