Structure-guided stabilization of pathogen-derived peptide-HLA-E complexes using non-natural amino acids conserves native TCR recognition.
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ABSTRACT:
SUBMITTER: Barber C
PROVIDER: S-EPMC9306587 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:An amino acid sequence, in the context of the solvent environment, contains all of the thermodynamic information necessary to encode a three-dimensional protein structure. To investigate the relationship between an amino acid sequence and its corresponding protein fold, a database of thermodynamic stability information was assembled that spanned 2951 residues from 44 nonhomologous proteins. This information was obtained using the COREX algorithm, which computes an ensemble-based description of the native state of a protein. It was observed that amino acid types partitioned unequally into high, medium, and low thermodynamic stability environments. Furthermore, these distributions were reproducible and were significantly different than those expected from random partitioning. To assess the structural importance of the distributions, simple fold-recognition experiments were performed based on a 3D-1D scoring matrix containing only COREX residue stability information. This procedure was able to recover amino acid sequences corresponding to correct target structures more effectively than scoring matrices derived from randomized data. High-scoring sequences were often aligned correctly with their corresponding target profiles, suggesting that calculated thermodynamic stability profiles have the potential to encode sequence information. As a control, identical fold-recognition experiments were performed on the same database of proteins using DSSP secondary structure information in the scoring matrix, instead of COREX residue stability information. The comparable performance of both approaches suggested that COREX residue stability information and secondary structure information could be of equivalent utility in more sophisticated fold-recognition techniques. The results of this work are a consequence of the idea that amino acid sequences fold not into single, rigidly stable structures but rather into thermodynamic ensembles best represented by a time-averaged structure.
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Project description:G-quadruplexes (G4s) are noncanonical DNA secondary structures formed through the self-association of guanines. They are distributed genome-widely and participate in multiple biological processes including gene transcription, and quadruplex-targeted ligands serve as potential therapeutic agents for DNA-targeted therapies. However, the roles of G-quadruplexes in transcriptional regulation remains elusive. Here, we establish a sensitive G4-CUT&Tag method for genome-wide profiling of native G-quadruplexes with high resolution and specificity. We find that native G-quadruplex signals are cell-type specific and are associated with transcriptional regulatory elements with active epigenetic modifications. Promoter-proximal RNA polymerase II pausing promotes native G-quadruplex formation, oppositely, G-quadruplex stabilization by quadruplex-targeted ligands globally reduces RNA polymerase II occupancy at gene promoters as well as nascent RNA synthesis. Moreover, G-quadruplex stabilization modulates chromatin states and impedes transcription initiation via inhibiting the loading of general transcription factors to promoters.Together, these studies reveal a reciprocal regulation between native G-quadruplex dynamics and gene transcription in the genome, which will deepen our knowledge of G-quadruplex biology towards considering therapeutically targeting G-quadruplexes in human diseases.
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Project description:Genetic diversity in human leukocyte antigen (HLA) molecules is thought to have arisen from the co-evolution between host and pathogen and maintained by balancing selection. Heterozygote advantage is a common proposed scenario for maintaining high levels of diversity in HLA genes, and extending from this, the divergent allele advantage (DAA) model suggests that individuals with more divergent HLA alleles bind and recognize a wider array of antigens. While the DAA model seems biologically suitable for driving HLA diversity, there is likely an upper threshold to the amount of sequence divergence. We used peptide-binding and pathogen-recognition capacity of DRB1 alleles as a model to further explore the DAA model; within the DRB1 locus, we examined binding predictions based on two distinct phylogenetic groups (denoted group A and B) previously identified based on non-peptide-binding region (PBR) nucleotide sequences. Predictions in this study support that group A allele and group B allele lineages have contrasting binding/recognition capacity, with only the latter supporting the DAA model. Furthermore, computer simulations revealed an inconsistency in the DAA model alone with observed extent of polymorphisms, supporting that the DAA model could only work effectively in combination with other mechanisms. Overall, we support that the mechanisms driving HLA diversity are non-exclusive. By investigating the relationships among HLA alleles, and pathogens recognized, we can provide further insights into the mechanisms on how humans have adapted to infectious diseases over time.
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