Dario Ghersi

CD8 memory T cells: cross-reactivity and heterologous immunity

Abstract Virus-specific memory T cell populations demonstrate plasticity in antigen recognition and in their ability to accommodate new memory T cell populations. The degeneracy of T cell antigen recognition and the flexibility of diverse antigen-specific repertoires allow the host to respond to a multitude of pathogens while accommodating these numerous large memory pools in a finite immune system. These cross-reactive memory T cells can be employed in immune responses and mediate protective immunity, but they can also induce life-threatening immunopathology or impede transplantation tolerance and graft survival. Here we discuss examples of altered viral pathogenesis occurring as a consequence of heterologous T cell immunity and propose models for the maintenance of a dynamic pool of memory cells.

SITEHOUND-web: a server for ligand binding site identification in protein structures

SITEHOUND-web (http://sitehound.sanchezlab.org) is a binding-site identification server powered by the SITEHOUND program. Given a protein structure in PDB format SITEHOUND-web will identify regions of the protein characterized by favorable interactions with a probe molecule. These regions correspond to putative ligand binding sites. Depending on the probe used in the calculation, sites with preference for different ligands will be identified. Currently, a carbon probe for identification of binding sites for drug-like molecules, and a phosphate probe for phosphorylated ligands (ATP, phoshopeptides, etc.) have been implemented. SITEHOUND-web will display the results in HTML pages including an interactive 3D representation of the protein structure and the putative sites using the Jmol java applet. Various downloadable data files are also provided for offline data analysis.

IFN-Induced Attrition of CD8 T Cells in the Presence or Absence of Cognate Antigen during the Early Stages of Viral Infections

Profound lymphopenia has been observed during many acute viral infections, and our laboratory has previously documented a type I IFN-dependent loss of CD8 T cells immediately preceding the development of the antiviral T cell response. Most memory (CD44high) and some naive (CD44low) CD8 T cells are susceptible to IFN-induced attrition, and we show in this study that the IFN-induced attrition of CD8+CD44high T cells is associated with elevated activation of caspase-3 and caspase-8. We questioned whether TCR engagement by Ag would render CD8 T cells resistant to attrition. We tested whether a high concentration of Ag (GP33 peptide) would protect lymphocytic choriomeningitis (LCMV)-specific naive CD8 T cells (TCR transgenic P14 cells specific for the GP33 epitope of LCMV) and memory CD8 T cells (GP33-specific LCMV-immune cells) from depletion. Both naive P14 and memory GP33-specific donor CD8 T cells decreased substantially 16 h after inoculation with the Toll receptor agonist and IFN inducer, poly(I:C), regardless of whether a high concentration of GP33 peptide was administered to host mice beforehand. Moreover, donor naive P14 and LCMV-specific memory cells were depleted from day 2 LCMV-infected hosts by 16 h posttransfer. These results indicate that Ag engagement does not protect CD8 T cells from the IFN-induced T cell attrition associated with viral infections. In addition, computer models indicated that early depletion of memory T cells may allow for the generation for a more diverse T cell response to infection by reducing the immunodomination caused by cross-reactive T cells.

EasyMIFs and SiteHound

UNLABELLED SiteHound uses Molecular Interaction Fields (MIFs) produced by EasyMIFs to identify protein structure regions that show a high propensity for interaction with ligands. The type of binding site identified depends on the probe atom used in the MIF calculation. The input to EasyMIFs is a PDB file of a protein structure; the output MIF serves as input to SiteHound, which in turn produces a list of putative binding sites. Extensive testing of SiteHound for the detection of binding sites for drug-like molecules and phosphorylated ligands has been carried out. AVAILABILITY EasyMIFs and SiteHound executables for Linux, Mac OS X, and MS Windows operating systems are freely available for download from http://sitehound.sanchezlab.org/download.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Improving Accuracy and Efficiency of Blind Protein-Ligand Docking by Focusing on Predicted Binding Sites

The use of predicted binding sites (binding sites calculated from the protein structure alone) is evaluated here as a tool to focus the docking of small molecule ligands into protein structures, simulating cases where the real binding sites are unknown. The resulting approach consists of a few independent docking runs carried out on small boxes, centered on the predicted binding sites, as opposed to one larger blind docking run that covers the complete protein structure. The focused and blind approaches were compared using a set of 77 known protein‐ligand complexes and 19 ligand‐free structures. The focused approach is shown to: (1) identify the correct binding site more frequently than blind docking; (2) produce more accurate docking poses for the ligand; (3) require less computational time. Additionally, the results show that very few real binding sites are missed in spite of focusing on only three predicted binding sites per target protein. Overall the results indicate that, by improving the sampling in regions that are likely to correspond to binding sites, the focused docking approach increases accuracy and efficiency of protein ligand docking for those cases where the ligand‐binding site is unknown. This is especially relevant in applications such as reverse virtual screening and structure‐based functional annotation of proteins. Proteins 2009.

Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures

Structural genomics projects have revealed structures for a large number of proteins of unknown function. Understanding the interactions between these proteins and their ligands would provide an initial step in their functional characterization. Binding site identification methods are a fast and cost-effective way to facilitate the characterization of functionally important protein regions. In this review we describe our recently developed methods for binding site identification in the context of existing methods. The advantage of energy-based approaches is emphasized, since they provide flexibility in the identification and characterization of different types of binding sites.

Biochemical Profiling of Histone Binding Selectivity of the Yeast Bromodomain Family

Background It has been shown that molecular interactions between site-specific chemical modifications such as acetylation and methylation on DNA-packing histones and conserved structural modules present in transcriptional proteins are closely associated with chromatin structural changes and gene activation. Unlike methyl-lysine that can interact with different protein modules including chromodomains, Tudor and MBT domains, as well as PHD fingers, acetyl-lysine (Kac) is known thus far to be recognized only by bromodomains. While histone lysine acetylation plays a crucial role in regulation of chromatin-mediated gene transcription, a high degree of sequence variation of the acetyl-lysine binding site in the bromodomains has limited our understanding of histone binding selectivity of the bromodomain family. Here, we report a systematic family-wide analysis of 14 yeast bromodomains binding to 32 lysine-acetylated peptides derived from known major acetylation sites in four core histones that are conserved in eukaryotes. Methodology The histone binding selectivity of purified recombinant yeast bromodomains was assessed by using the native core histones in an overlay assay, as well as N-terminally biotinylated lysine-acetylated histone peptides spotted on streptavidin-coated nitrocellulose membrane in a dot blot assay. NMR binding analysis further validated the interactions between histones and selected bromodomain. Structural models of all yeast bromodomains were built using comparative modeling to provide insights into the molecular basis of their histone binding selectivity. Conclusions Our study reveals that while not all members of the bromodomain family are privileged to interact with acetylated-lysine, identifiable sequence features from those that bind histone emerge. These include an asparagine residue at the C-terminus of the third helix in the 4-helix bundle, negatively charged residues around the ZA loop, and preponderance of aromatic amino acid residues in the binding pocket. Further, while bromodomains exhibit selectivity for different sites in histones, individual interactions are of modest affinity. Finally, electrostatic interactions appear to be a primary determining factor that guides productive association between a bromodomain and a lysine-acetylated histone.

Disentangling function from topology to infer the network properties of disease genes

BackgroundThe topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhibit distinct network features, as compared to all genes.ResultsWe demonstrate that the functional composition of disease gene sets is an important confounding factor in these types of analyses. We consider five disease sets and show that while they indeed have distinct topological features, they are also enriched in functions that a priori exhibit distinct network properties. To address this, we develop a computational framework to assess the network properties of disease genes based on a sampling algorithm that generates control gene sets that are functionally similar to the disease set. Using our function-constrained sampling approach, we demonstrate that for most of the topological properties studied, disease genes are more similar to sets of genes with similar functional make-up than they are to randomly selected genes; this suggests that these observed differences in topological properties reflect not only the distinguishing network features of disease genes but also their functional composition. Nevertheless, we also highlight many cases where disease genes have distinct topological properties even when accounting for function.ConclusionsOur approach is an important first step in extracting the residual topological differences in disease genes when accounting for function, and leads to new insights into the network properties of disease genes.

Interaction-based discovery of functionally important genes in cancers

A major challenge in cancer genomics is uncovering genes with an active role in tumorigenesis from a potentially large pool of mutated genes across patient samples. Here we focus on the interactions that proteins make with nucleic acids, small molecules, ions and peptides, and show that residues within proteins that are involved in these interactions are more frequently affected by mutations observed in large-scale cancer genomic data than are other residues. We leverage this observation to predict genes that play a functionally important role in cancers by introducing a computational pipeline (http://canbind.princeton.edu) for mapping large-scale cancer exome data across patients onto protein structures, and automatically extracting proteins with an enriched number of mutations affecting their nucleic acid, small molecule, ion or peptide binding sites. Using this computational approach, we show that many previously known genes implicated in cancers are enriched in mutations within the binding sites of their encoded proteins. By focusing on functionally relevant portions of proteins—specifically those known to be involved in molecular interactions—our approach is particularly well suited to detect infrequent mutations that may nonetheless be important in cancer, and should aid in expanding our functional understanding of the genomic landscape of cancer.

Broad TCR repertoire and diverse structural solutions for recognition of an immunodominant CD8 + T cell epitope

A keystone of antiviral immunity is CD8+ T cell recognition of viral peptides bound to MHC-I proteins. The recognition modes of individual T cell receptors (TCRs) have been studied in some detail, but the role of TCR variation in providing a robust response to viral antigens is unclear. The influenza M1 epitope is an immunodominant target of CD8+ T cells that help to control influenza in HLA-A2+ individuals. Here we show that CD8+ T cells use many distinct TCRs to recognize HLA-A2–M1, which enables the use of different structural solutions to the problem of specifically recognizing a relatively featureless peptide antigen. The vast majority of responding TCRs target a small cleft between HLA-A2 and the bound M1 peptide. These broad repertoires lead to plasticity in antigen recognition and protection against T cell clonal loss and viral escape.