Explore the vast range of titles available.

Thymic central tolerance eliminates most immature T cells with autoreactive T cell receptors (TCR) that recognize self MHC/peptide complexes. Regardless, an unknown number of autoreactive CD4 + Foxp3 − T cells escape negative selection and in the periphery require continuous suppression by CD4 + Foxp3 + regulatory cells (Tregs). Here, we compare immune repertoires of Treg-deficient and Treg-sufficient mice to find Tregs continuously constraining one-third of mature CD4 + Foxp3 − cells from converting to pathogenic effectors in healthy mice. These dormant pathogenic clones frequently express TCRs activatable by ubiquitous autoantigens presented by class II MHCs on conventional dendritic cells, including self-peptides that select them in the thymus. Our data thus suggest that identification of most potentially autoreactive CD4 + T cells in the peripheral repertoire is critical to harness or redirect these cells for therapeutic advantage. Autoreactive T cells are normally eliminated during their maturation in the thymus, but an unknown number of autoreactive CD4 +  T cells escape to the periphery. Here the authors show, by comparing the T cell receptors of mice sufficient or deficient in CD4 + Foxp3 +  regulatory T (Treg) cells, that autoreactive and potentially pathogenic clones account for approximately one-third of the peripheral repertoire of CD4 + Foxp3 – cells.

In the gut, various subsets of intraepithelial T cells (IELs) respond to self or non-self-antigens derived from the body, diet, commensal and pathogenic microbiota. Dominant subset of IELs in the small intestine are TCRαβCD8αα + cells, which are derived from immature thymocytes that express self-reactive TCRs. Although most of TCRαβCD8αα + IELs are thymus-derived, their repertoire adapts to microbial flora. Here, using high throughput TCR sequencing we examined how clonal diversity of TCRαβCD8αα + IELs changes upon exposure to commensal-derived antigens. We found that fraction of CD8αα + IELs and CD4 + T cells express identical αβTCRs and this overlap raised parallel to a surge in the diversity of microbial flora. We also found that an opportunistic pathogen ( Staphylococcus aureus ) isolated from mouse small intestine specifically activated CD8αα + IELs and CD4 + derived T cell hybridomas suggesting that some of TCRαβCD8αα + clones with microbial specificities have extrathymic origin. We also report that CD8ααCD4 + IELs and Foxp3CD4 + T cells from the small intestine shared many αβTCRs, regardless whether the later subset was isolated from Foxp3 CNS1 sufficient or Foxp3 CNS1 deficient mice that lacks peripherally-derived Tregs. Overall, our results imply that repertoire of TCRαβCD8αα + in small intestine expends in situ in response to changes in microbial flora.

Background One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold. Results Our results show that DeepFrag-k yields 92.98 % accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition. Conclusions There is a set of fragments that can serve as structural “keywords” distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition.

Although the protein sequence-structure gap continues to enlarge due to the development of high-throughput sequencing tools, the protein structure universe tends to be complete without proteins with novel structural folds deposited in the protein data bank (PDB) recently. In this work, we identify a protein structural dictionary (Frag-K) composed of a set of backbone fragments ranging from 4 to 20 residues as the structural \"keywords\" that can effectively distinguish between major protein folds. We firstly apply randomized spectral clustering and random forest algorithms to construct representative and sensitive protein fragment libraries from a large scale of high-quality, non-homologous protein structures available in PDB. We analyze the impacts of clustering cut-offs on the performance of the fragment libraries. Then, the Frag-K fragments are employed as structural features to classify protein structures in major protein folds defined by SCOP (Structural Classification of Proteins). Our results show that a structural dictionary with ～4004- to 20-residue Frag-K fragments is capable of classifying major SCOP folds with high accuracy.

Proteins play a crucial role in living organisms as they perform many vital tasks in every living cell. Knowledge of protein folding has a deep impact on understanding the heterogeneity and molecular functions of proteins. Such information leads to crucial advances in drug design and disease understanding. Fold recognition is a key step in the protein structure discovery process, especially when traditional computational methods fail to yield convincing structural homologies. In this work, we present a new protein fold recognition approach using machine learning and data mining methodologies. First, we identify a protein structural fragment library (Frag-K) composed of a set of backbone fragments ranging from 4 to 20 residues as the structural “keywords” that can effectively distinguish between major protein folds. We firstly apply randomized spectral clustering and random forest algorithms to construct representative and sensitive protein fragment libraries from a large-scale of high-quality, non-homologous protein structures available in PDB. We analyze the impacts of clustering cut-offs on the performance of the fragment libraries. Then, the Frag-K fragments are employed as structural features to classify protein structures in major protein folds defined by SCOP (Structural Classification of Proteins). Our results show that a structural dictionary with ~400 4- to 20-residue Frag-K fragments is capable of classifying major SCOP folds with high accuracy. Then, based on Frag-k, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multimodal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolution neural network (CNN) to classify the fragment vectors into the corresponding folds. Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition.

Although the protein sequence-structure gap continues to enlarge due to the development of high-throughput sequencing tools, the protein structure universe tends to be complete without proteins with novel structural folds deposited in the protein data bank (PDB) recently. In this work, we identify a protein structural dictionary (Frag-K) composed of a set of backbone fragments ranging from 4 to 20 residues as the structural “keywords” that can effectively distinguish between major protein folds. We firstly apply randomized spectral clustering and random forest algorithms to construct representative and sensitive protein fragment libraries from a large scale of high-quality, non-homologous protein structures available in PDB. We analyze the impacts of clustering cut-offs on the performance of the fragment libraries. Then, the Frag-K fragments are employed as structural features to classify protein structures in major protein folds defined by SCOP (Structural Classification of Proteins). Our results show that a structural dictionary with ~400 4- to 20-residue Frag-K fragments is capable of classifying major SCOP folds with high accuracy.