ß-glucan Exposure on the Fungal Cell Wall Tightly Correlates with Competitive Fitness of Candida Species in the Mouse Gastrointestinal Tract.

Candida albicans is responsible for ~400,000 systemic fungal infections annually, with an associated mortality rate of 46-75%. The human gastrointestinal (GI) tract represents the largest natural reservoir of Candida species and is a major source of systemic fungal infections. However, the factors that control GI colonization by Candida species are not completely understood. We hypothesized that the fungal cell wall would play an important role in determining the competitive fitness of Candida species in the mammalian GI tract. To test this hypothesis, we generated a systematic collection of isogenic C. albicans cell wall mutants and measured their fitness in the mouse GI tract via quantitative competition assays. Whereas a large variation in competitive fitness was found among mutants, no correlation was observed between GI fitness and total levels of individual cell wall components. Similar results were obtained in a set of distantly-related Candida species, suggesting that total amounts of individual cell wall components do not determine the ability of fungi to colonize the GI tract. We then subjected this collection of Candida strains and species to an extensive quantitative phenotypic profiling in search for features that might be responsible for their differences in GI fitness, but found no association with the ability to grow in GI-mimicking and stressful environments or with in vitro and in vivo virulence. The most significant association with GI fitness was found to be the strength of signaling through the Dectin-1 receptor. Using a quantitative assay to measure the amount of exposed ß-glucan on the surface of fungal cells, we found this parameter, unlike total ß-glucan levels, to be strongly predictive of competitive fitness in the mouse GI tract. These data suggest that fungal cell wall architecture, more so than its crude composition, critically determines the ability of fungi to colonize the mammalian GI tract. In particular, recognition of exposed ß-glucan by Dectin-1 receptor appears to severely limit Candida GI fitness and hence represents a promising target to reduce fungal colonization in patients at risks of systemic candidiasis.

Predicting peptide binding to Major Histocompatibility Complex molecules.

The Major Histocompatibility Complex (MHC) constitutes an important part of the human immune system. During infection, pathogenic proteins are processed into peptide fragments by the antigen processing machinery. These peptides bind to MHC molecules and the MHC-peptide complex is then transported to the cell membrane where it elicits an immune response via T-cell binding. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. One of the most challenging aspects of this area of research is understanding the specificity and sensitivity of the binding process. An empirical approach to the problem is unfeasible as there are over 512 billion potential binding peptides for each MHC molecule. Computational approaches offer the promise of predicting peptide binding, thus dramatically reducing the number of peptides proceeding to experimental verification. Various bioinformatic approaches have been developed to predict whether or not a particular peptide will bind to a particular MHC allele. Currently, peptide binding prediction methods can be categorised into three major groups: motif- and scoring matrix-based methods, artificial intelligence- (AI-) based methods, and structure-based methods. The first two are sequence-based approaches and are generally based on common sequence motifs in peptides known to bind to MHC molecules. The structure-based approach concerns the structural features and the distribution of energy between the binding peptide and the MHC molecule. Although knowledge of the molecular structure of the MHC molecules is expected to lead to better predictions of peptide binding, the development of structure-based methods has been relatively slow compared to sequence-based methods. Comparisons of various methods showed that the best sequence-based methods significantly outperform structure-based methods. This may be improved by producing more structures and binding data desperately needed by many alleles, especially class II molecules. On the other hand, the large number of verification methods and indicators used by structure-based studies hinders critical evaluation of the methods. Adopting commonly used assessment procedures can demonstrate the relative performance of structure-based methods in a straightforward comparison with other methods. This review provides an overview of current methods for predicting peptide binding to the MHC, with a focus on structure-based methods, and explores the potential for future development in this area.

Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function.

The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods.