Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes.

ABSTRACT: Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide-MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide-MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide-MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.

SUBMITTER: Abella JR

PROVIDER: S-EPMC7720115 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Independent Markov decomposition: Toward modeling kinetics of biomolecular complexes.

Project description:To advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) simulations and Markov state models (MSMs) has enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes, the number of independent or weakly coupled subsystems increases, and the number of global system states increases exponentially, making the sampling of all distinct global states unfeasible. In this work, we present a technique called independent Markov decomposition (IMD) that leverages weak coupling between subsystems to compute a global kinetic model without requiring the sampling of all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology, we demonstrate that IMD models can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.

| S-EPMC8346863 | biostudies-literature

Accurate modeling of peptide-MHC structures with AlphaFold.

Project description:Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T-cell surveillance. Reliable in silico prediction of which peptides would be presented and which T-cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling precision and class II peptide register prediction. We explore applications of this method towards improving peptide-MHC binding prediction.

| S-EPMC10028922 | biostudies-literature

Enhanced modeling via network theory: Adaptive sampling of Markov state models.

Project description:Computer simulations can complement experiments by providing insight into molecular kinetics with atomic resolution. Unfortunately, even the most powerful supercomputers can only simulate small systems for short timescales, leaving modeling of most biologically relevant systems and timescales intractable. In this work, however, we show that molecular simulations driven by adaptive sampling of networks called Markov State Models (MSMs) can yield tremendous time and resource savings, allowing previously intractable calculations to be performed on a routine basis on existing hardware. We also introduce a distance metric (based on the relative entropy) for comparing MSMs. We primarily employ this metric to judge the convergence of various sampling schemes but it could also be employed to assess the effects of perturbations to a system (e.g. determining how changing the temperature or making a mutation changes a system's dynamics).

| S-EPMC3637129 | biostudies-literature

Stage-dependent reactivity of thymocytes to self-peptide--MHC complexes.

Project description:In mice that express a transgene for the 2C T cell antigen-receptor (TCR) and lack a recombinase-activating gene (2C(+)RAG(-/-) mice) most of the peripheral T cells are CD8(+), a few are CD4(+), and a significant fraction are CD4(-)CD8(-) [double negative (DN)]. The DN 2C cells, like DN T cells that are abundant in various other alphabeta TCR-transgenic mice, appear to be derived directly from DN thymocytes that prematurely express the TCR transgene. The DN 2C cells are virtually absent in mice deficient in major histocompatibility complex class II (MHC-II) but more abundant in mice deficient in MHC-I, suggesting that the DN 2C thymocytes are positively selected by self-peptide-MHC-II (pMHC-II) complexes and negatively selected by self-pMHC-I complexes. The pMHC-I complexes, however, positively select CD8(+) 2C T cells in the same mice. The different effects of thymic pMHC-I on DN and CD8(+) thymocytes are consistent with the finding that DN 2C thymocytes are more sensitive than more mature CD4(+)CD8(+) [double positive (DP)] thymocytes to a weak pMHC-I agonist for the 2C TCR. Together with previous evidence that DP thymocytes respond more sensitively than T cells in the periphery to weak pMHC agonists, the findings suggest progressive decreases in responsiveness to self-pMHC-I complexes as thymocytes develop from DN to DP thymocytes and then to mature naïve T cells in the periphery.

| S-EPMC1829260 | biostudies-literature

TCR recognition of peptide/MHC class II complexes and superantigens.

Project description:Major histocompatibility complex (MHC) class II molecules display peptides to the T cell receptor (TCR). The ability of the TCR to discriminate foreign from self-peptides presented by MHC molecules is a requirement of an effective adaptive immune response. Dysregulation of this molecular recognition event often leads to a disease state. Recently, a number of structural studies have provided significant insight into several such dysregulated interactions between peptide/MHC complexes and TCR molecules. These include TCR recognition of self-peptides, which results in autoimmune reactions, and of mutant self-peptides, common in the immunosurveillance of tumors, as well as the engagement of TCRs by superantigens, a family of bacterial toxins responsible for toxic shock syndrome.

| S-EPMC2949352 | biostudies-literature

Positive selection of self-MHC-reactive T cells by individual peptide-MHC class II complexes.

Project description:If T cells require specific interactions with MHC-bound peptides during positive selection, then the specificities of T cells selected by one peptide should be distinct from those selected by another. We have examined positive selection of CD4 T cells in four strains of mice, each overexpressing a different peptide-1-A(b)(A(b)) complex. We show that a subset of CD4 T cells is selected by the overexpressed peptide and that the specificities of the CD4 T cells, as measured by reactivity to wild-type antigen-presenting cells, vary greatly depending on which peptide is overexpressed. These differences in specificity are mediated through positive selection not negative selection. Each of the four peptide-A(b) complexes appears to adopt a different conformation, and these differences correlate with the differences in reactivity. Our results suggest that individual peptide-MHC complexes positively select different subsets of self-MHC-reactive T cells and that the conformation of the peptide-MHC complex may contribute to this process.

| S-EPMC124507 | biostudies-literature

Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin.

Project description:Calmodulin (CaM) is a calcium sensor which binds and regulates a wide range of target-proteins. This implicitly enables the concentration of calcium to influence many downstream physiological responses, including muscle contraction, learning and depression. The antipsychotic drug trifluoperazine (TFP) is a known CaM inhibitor. By binding to various sites, TFP prevents CaM from associating to target-proteins. However, the molecular and state-dependent mechanisms behind CaM inhibition by drugs such as TFP are largely unknown. Here, we build a Markov state model (MSM) from adaptively sampled molecular dynamics simulations and reveal the structural and dynamical features behind the inhibitory mechanism of TFP-binding to the C-terminal domain of CaM. We specifically identify three major TFP binding-modes from the MSM macrostates, and distinguish their effect on CaM conformation by using a systematic analysis protocol based on biophysical descriptors and tools from machine learning. The results show that depending on the binding orientation, TFP effectively stabilizes features of the calcium-unbound CaM, either affecting the CaM hydrophobic binding pocket, the calcium binding sites or the secondary structure content in the bound domain. The conclusions drawn from this work may in the future serve to formulate a complete model of pharmacological modulation of CaM, which furthers our understanding of how these drugs affect signaling pathways as well as associated diseases.

| S-EPMC9581412 | biostudies-literature

Thymoproteasome subunit-?5T generates peptide-MHC complexes specialized for positive selection.

Project description:Cortical thymic epithelial cells (cTECs) express a unique thymoproteasome subunit-?5T that plays an essential role in the development of CD8 T cells. In contrast, the immunoproteasome subunit-?5i is expressed in other thymic antigen-presenting cells (APCs). The thymoproteasome may generate peptides that are specialized for positive selection, or it may simply serve to generate peptides that are distinct from other APCs that cause negative selection, thereby promoting an overall larger number of surviving clones to mature and function in the immune system. To distinguish these models, we genetically engineered mice to express distinct peptide repertoires in cTECs vs. other APCs without expressing ?5T, by generating ?5t(5i) knockin mice, in which ?5i replaced ?5T in cTECs. When such animals were crossed to ?5i(-/-) mice, ?5i was exclusively expressed in cTECs, whereas ?5 was expressed in other cells. However, this mouse did not support normal positive selection, suggesting that ?5T generates peptides that are intrinsically better for positive selection (i.e., ?5i could not replace ?5T) and not merely because these peptides are distinct from peptides presented by other APCs. Finally, using an Nur77(GFP) reporter, we show that the T cells generated in the absence of ?5T have higher reactivity to self, generating predominantly CD44(hi) memory phenotype peripheral CD8(+) T cells. Altogether, our results suggest that the thymoproteasome supports positive selection by generating peptides that are optimized for the selection of weakly self-reactive, naïve T-cell clones.

| S-EPMC3637736 | biostudies-literature

Recognition of self-peptide-MHC complexes by autoimmune T-cell receptors.

Project description:T cell receptors (TCR) recognize antigenic peptides displayed by MHC molecules. Whereas T-cell recognition of foreign peptides is essential for immune defense against microbial pathogens, recognition of self-peptides can cause autoimmune disease. Structural studies of anti-foreign TCR showed remarkable similarities in the topology of TCR binding to peptide-MHC, which maximize interactions with the ligand. However, recent structures involving autoimmune and tumor-specific TCR have revealed that they engage self-peptide-MHC with different topologies, which are suboptimal for TCR binding. These differences might reflect the distinct selection pressures exerted on anti-microbial versus autoreactive T cells. The structures also provide new insights into TCR cross-reactivity, which can contribute to autoimmunity by increasing the likelihood of self-peptide-MHC recognition.

| S-EPMC2739108 | biostudies-literature

PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes.

Project description:Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges.

| S-EPMC9127323 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data