Allosteric Regulation of Fibronectin/α5β1 Interaction by Fibronectin-Binding MSCRAMMs

Citation: Liang, X. W., Garcia, B. L., Visai, L., Prabhakaran, S., Meenan, N. A. G., Potts, J. R., . . . Hook, M. (2016). Allosteric Regulation of Fibronectin/alpha(5)beta(1) Interaction by Fibronectin-Binding MSCRAMMs. Plos One, 11(7), 17. doi:10.1371/journal.pone.0159118Adherence ofmicrobes to host tissues is a hallmark of infectious disease and is often mediated by a class of adhesins termed MSCRAMMs (Microbial Surface Components Recognizing Adhesive Matrix Molecules). Numerous pathogens express MSCRAMMs that specifically bind the heterodimeric human glycoprotein fibronectin (Fn). In addition to roles in adhesion, Fn-binding MSCRAMMs exploit physiological Fn functions. For example, several pathogens can invade host cells by a mechanism whereby MSCRAMM-bound Fn bridges interaction with alpha(5)beta(1) integrin. Here, we investigate two Fn-binding MSCRAMMs, FnBPA (Staphylococcus aureus) and BBK32 (Borrelia burgdorferi) to probe structure-activity relationships of MSCRAMM-induced Fn/alpha(5)beta(1) integrin activation. Circular dichroism, fluorescence resonance energy transfer, and dynamic light scattering techniques uncover a conformational rearrangement of Fn involving domains distant from the MSCRAMM binding site. Surface plasmon resonance experiments demonstrate a significant enhancement of Fn/alpha(5)beta(1) integrin affinity in the presence of FnBPA or BBK32. Detailed kinetic analysis of these interactions reveal that this change in affinity can be attributed solely to an increase in the initial Fn/alpha(5)beta(1) on-rate and that this rate-enhancement is dependent on high-affinity Fn-binding by MSCRAMMs. These data implicate MSCRAMM-induced perturbation of specific intramolecular contacts within the Fn heterodimer resulting in activation by exposing previously cryptic alpha(5)beta(1) interaction motifs. By correlating structural changes in Fn to a direct measurement of increased Fn/alpha(5)beta(1) affinity, this work significantly advances our understanding of the structural basis for the modulation of integrin function by Fn-binding MSCRAMMs

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community

Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community