Fractionation of magnesium isotopes in the lower mantle: insights from density functional theory

<p>Poster presented at the Geological Socitey of London meeting "Deep Earth Processes: windows on the working of a planet", 15 September 2014.</p> <p> </p

The structure and polymorphisms of chicken MHC Class I alleles BF2*15∶01 and BF2*19∶01.

<p>A) The structure of the lumenal domain of a chicken MHC Class I molecule. A space filling representation of the heavy chain is shown, formed of α₁– α₂ peptide binding domain and the membrane proximal α₃ domain, creating a complex with a non-covalently bound β₂m light chain shown as a ribbon representation. B) The peptide is shown as a stick representation in grey, non-covalently bound into the groove formed between the α₁ and α₂ helices. The sites of the polymorphic residues between BF2*15∶01 and BF2*19∶01 indicated in green, with the location of residue 22 indicated in the peptide binding domain below the α₁ helix.</p

The global dynamics of MHC I identified by Principal Component Analysis.

<p>For each 420ns molecular dynamics simulation of BF2*15∶01 and BF2*19∶01 PCA was performed using a common peptide free backbone structure. A) Contributions of the first 50 PCs to the total variance of the backbone atomic motions. B) Porcupine plots indicate the magnitude and direction of motion for each backbone atom along PC1 and 2 in both the peptide bound and peptide free states. The magnitude between extremes is indicated by the colour bar. C) Gibbs free energy landscapes are generated from the principal coordinates of PC1 and PC2 and transformed by treatment as a Boltzmann ensemble. Individual probability densities for PC1 and PC2 are plotted on the outside adjacent axes.</p

Identification of a protein sector in chicken MHC I.

<p>Statistical coupling analysis (SCA) was carried out on a multiple sequence alignment (MSA) of 141 sequences obtained from a similarity search querying the BF2*15∶01 heavy chain as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089657#pone.0089657-Smock1" target="_blank">[30]</a>. A) The degree of conservation of each heavy chain residue i in the MSA is computed as the Kullback-Leibler relative entropy D_i. Bigger bars indicate greater conservation. The 85 protein sector residues are in red, 6 polymorphic residues between BF2*15∶01 and BF2*19∶01 are in green and the 2 residues that are both polymorphic and part of the protein sector are in blue. All other residues are in grey. B) Protein sector residues are mapped as spheres onto a ribbon representation of the BF2*15∶01 structure. Colours as (A), with the peptide as yellow sticks. C) and D) Space filling representations of the MHC I heavy chain, coloured as (B). The contiguous network of residues forming a protein sector comprises of 31% of heavy chain residues.</p

Quantification of the flexibility of MHC I by conformational φ angle standard deviation.

<p>A) and C) The standard deviation of the internal angle of rotation φ measuring the rotation around N-Cα bond of each residue of BF2*15∶01 and BF2*19∶01 from 420ns of molecular dynamics simulation in the peptide bound and peptide free states. Peptide bound measurements are shown as black bars and peptide free as red bars. B) and D) Ribbon representations of BF2*15∶01 and BF2*19∶01 with the peptide free simulations φ angle standard deviations mapped as increasing from blue to white to red, with annotations on the BF2*15∶01 heavy chain. Glycine residues are coloured black.</p

image_5_A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules.tif

<p>Major histocompatibility complex-I (MHC-I) molecules play a central role in the immune response to viruses and cancers. They present peptides on the surface of affected cells, for recognition by cytotoxic T cells. Determining which peptides are presented, and in what proportion, has profound implications for developing effective, medical treatments. However, our ability to predict peptide presentation levels is currently limited. Existing prediction algorithms focus primarily on the binding affinity of peptides to MHC-I, and do not predict the relative abundance of individual peptides on the surface of antigen-presenting cells in situ which is a critical parameter for determining the strength and specificity of the ensuing immune response. Here, we develop and experimentally verify a mechanistic model for predicting cell-surface presentation of competing peptides. Our approach explicitly models key steps in the processing of intracellular peptides, incorporating both peptide binding affinity and intracellular peptide abundance. We use the resulting model to predict how the peptide repertoire is modified by interferon-γ, an immune modulator well known to enhance expression of antigen processing and presentation proteins.</p

image_11_A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules.tif

<p>Major histocompatibility complex-I (MHC-I) molecules play a central role in the immune response to viruses and cancers. They present peptides on the surface of affected cells, for recognition by cytotoxic T cells. Determining which peptides are presented, and in what proportion, has profound implications for developing effective, medical treatments. However, our ability to predict peptide presentation levels is currently limited. Existing prediction algorithms focus primarily on the binding affinity of peptides to MHC-I, and do not predict the relative abundance of individual peptides on the surface of antigen-presenting cells in situ which is a critical parameter for determining the strength and specificity of the ensuing immune response. Here, we develop and experimentally verify a mechanistic model for predicting cell-surface presentation of competing peptides. Our approach explicitly models key steps in the processing of intracellular peptides, incorporating both peptide binding affinity and intracellular peptide abundance. We use the resulting model to predict how the peptide repertoire is modified by interferon-γ, an immune modulator well known to enhance expression of antigen processing and presentation proteins.</p

image_9_A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules.tif

<p>Major histocompatibility complex-I (MHC-I) molecules play a central role in the immune response to viruses and cancers. They present peptides on the surface of affected cells, for recognition by cytotoxic T cells. Determining which peptides are presented, and in what proportion, has profound implications for developing effective, medical treatments. However, our ability to predict peptide presentation levels is currently limited. Existing prediction algorithms focus primarily on the binding affinity of peptides to MHC-I, and do not predict the relative abundance of individual peptides on the surface of antigen-presenting cells in situ which is a critical parameter for determining the strength and specificity of the ensuing immune response. Here, we develop and experimentally verify a mechanistic model for predicting cell-surface presentation of competing peptides. Our approach explicitly models key steps in the processing of intracellular peptides, incorporating both peptide binding affinity and intracellular peptide abundance. We use the resulting model to predict how the peptide repertoire is modified by interferon-γ, an immune modulator well known to enhance expression of antigen processing and presentation proteins.</p

image_1_A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules.tif

<p>Major histocompatibility complex-I (MHC-I) molecules play a central role in the immune response to viruses and cancers. They present peptides on the surface of affected cells, for recognition by cytotoxic T cells. Determining which peptides are presented, and in what proportion, has profound implications for developing effective, medical treatments. However, our ability to predict peptide presentation levels is currently limited. Existing prediction algorithms focus primarily on the binding affinity of peptides to MHC-I, and do not predict the relative abundance of individual peptides on the surface of antigen-presenting cells in situ which is a critical parameter for determining the strength and specificity of the ensuing immune response. Here, we develop and experimentally verify a mechanistic model for predicting cell-surface presentation of competing peptides. Our approach explicitly models key steps in the processing of intracellular peptides, incorporating both peptide binding affinity and intracellular peptide abundance. We use the resulting model to predict how the peptide repertoire is modified by interferon-γ, an immune modulator well known to enhance expression of antigen processing and presentation proteins.</p

image_6_A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules.tif

<p>Major histocompatibility complex-I (MHC-I) molecules play a central role in the immune response to viruses and cancers. They present peptides on the surface of affected cells, for recognition by cytotoxic T cells. Determining which peptides are presented, and in what proportion, has profound implications for developing effective, medical treatments. However, our ability to predict peptide presentation levels is currently limited. Existing prediction algorithms focus primarily on the binding affinity of peptides to MHC-I, and do not predict the relative abundance of individual peptides on the surface of antigen-presenting cells in situ which is a critical parameter for determining the strength and specificity of the ensuing immune response. Here, we develop and experimentally verify a mechanistic model for predicting cell-surface presentation of competing peptides. Our approach explicitly models key steps in the processing of intracellular peptides, incorporating both peptide binding affinity and intracellular peptide abundance. We use the resulting model to predict how the peptide repertoire is modified by interferon-γ, an immune modulator well known to enhance expression of antigen processing and presentation proteins.</p