Structure-based prediction of MHC–peptide association: Algorithm comparison and application to cancer vaccine design

Schiewe, Alexandra J.; Haworth, Ian S.

doi:10.1016/j.jmgm.2007.03.017

Cited by 18 publications

(6 citation statements)

References 40 publications

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“…20) computed from low-or high-resolution models of peptide-MHC complexes. Notably, such high-resolution features are often integrated with experimental binding data and binding predictions are made using specifically trained machine-learning algorithms (15,18,20), hinting at the challenges faced by high-resolution modeling.Here we report the direct use of atomically detailed molecular modeling to map peptide binding landscapes for a diverse collection of HLA-A and HLA-B proteins. For our purposes, a complete description of a peptide binding landscape would consist of the binding affinities of the target protein for all possible peptides, and three-dimensional cocomplex structures for all highaffinity binding peptides (peptide structure, as well as sequence, being critical for processes such as recognition by T cells).…”

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confidence: 99%

“…15, 18, and 19; and intermolecular contacts, ref. 20) computed from low-or high-resolution models of peptide-MHC complexes. Notably, such high-resolution features are often integrated with experimental binding data and binding predictions are made using specifically trained machine-learning algorithms (15,18,20), hinting at the challenges faced by high-resolution modeling.…”

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confidence: 99%

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Large-scale characterization of peptide-MHC binding landscapes with structural simulations

Yanover

Bradley

2011

Proc. Natl. Acad. Sci. U.S.A.

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Class I major histocompatibility complex proteins play a critical role in the adaptive immune system by binding to peptides derived from cytosolic proteins and presenting them on the cell surface for surveillance by T cells. The varied peptide binding specificity of these highly polymorphic molecules has important consequences for vaccine design, transplantation, autoimmunity, and cancer development. Here, we describe a molecular modeling study of MHC-peptide interactions that integrates sampling techniques from protein-protein docking, loop modeling, de novo structure prediction, and protein design in order to construct atomically detailed peptide binding landscapes for a diverse set of MHC proteins. Specificity profiles derived from these landscapes recover key features of experimental binding profiles and can be used to predict peptide binding with reasonable accuracy. Family wide comparison of the predicted binding landscapes recapitulates previously reported patterns of specificity divergence and peptiderepertoire diversity while providing a structural basis for observed specificity patterns. The size and sequence diversity of these structure-based binding landscapes enable us to identify subtle patterns of covariation between peptide sequence positions; analysis of the associated structural models suggests physical interactions that may mediate these sequence correlations. C lass I MHC proteins selectively bind short (typically, 8-10 amino acids) peptides derived from proteasomal degradation of cytosolic proteins and present these peptides on the cell surface for surveillance by CD8+ T lymphocytes. By this mechanism, non-self-peptides derived from intracellular pathogens can be detected by the immune system, and infected cells can be targeted for destruction. The MHC proteins are highly polymorphic (the class I MHC gene HLA-B is estimated to be the most polymorphic gene in the human genome; ref. 1), with different proteins recognizing specific and often quite divergent peptide repertoires. MHC polymorphism has been reported to play a role in a wide range of phenomena including susceptibility to infectious and inflammatory diseases, drug toxicity, autoimmunity, cancer, and transplantation outcome (2-5). The specific role of peptide-repertoire variation in each context is not well understood, in part because comprehensive characterization of the peptide binding preferences of individual MHC proteins is experimentally challenging. As a result, there has been great interest in computational models of MHC-peptide recognition. This interest has led to the development of machine-learning algorithms that are able to predict-in many cases, with high accuracy-the binding of a novel peptide to a target MHC molecule by training on experimental binding data for that protein or related MHC family members (6-10).The X-ray crystallographic structure determination of the first MHC protein (11) and, later, of several hundred peptide-MHC complexes, has shed light on the physicochemical attributes facilitating the formation o...

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confidence: 99%

Large-scale characterization of peptide-MHC binding landscapes with structural simulations

Yanover

Bradley

2011

Proc. Natl. Acad. Sci. U.S.A.

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“…Recently, a large amount of data for crystal structures of HLA–peptide complexes has been accumulated, allowing bioinformaticians to predict molecular binding simulations. ( 20,21 ) However, despite biophysical and biochemical analysis of HLA complexes, accurate prediction of antigen peptide binding to HLA molecule is still difficult.…”

Section: Discussionmentioning

confidence: 99%

Analysis of HLA‐A24‐restricted peptides of carcinoembryonic antigen using a novel structure‐based peptide‐HLA docking algorithm

Nakamura¹,

Tai²,

Oshita³

et al. 2011

Cancer Science

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Carcinoembryonic antigen (CEA) is a very common tumor marker because many types of solid cancer usually produce a variety of CEA and a highly sensitive measuring kit has been developed. However, immunological responses associated with CEA have not been fully characterized, and specifically a weak immunogenicity of CEA protein as a tumor antigen is reported in human leukocyte antigen (HLA)-A24-restricted CEA peptide-based cancer immunotherapy. These observations demonstrated that immunogenic and potent HLA-A24-restricted CTL epitope peptides derived from CEA protein are seemingly difficult to predict using a conventional bioinformatics approach based on primary amino acid sequence. In the present study, we developed an in silico docking simulation assay system of binding affinity between HLA-A24 protein and A24-restricted peptides using two software packages, AutoDock and MODELLER, and a crystal structure of HLA-A24 protein obtained from the Protein Data Bank. We compared the current assay system with HLA-peptide binding predictions of the bioinformatics and molecular analysis section (BIMAS) in terms of the prediction capability using MHC stabilization and peptide-stimulated CTL induction assays for CEA and other HLA-A24 peptides. The MHC stabilization score was inversely correlated with the affinity calculated in the docking simulation alone (r = )0.589, P = 0.015), not with BIMAS score or the IFN-c production index. On the other hand, BIMAS was not significantly correlated with any other parameters. These results suggested that our in silico assay system has potential advantages in efficiency of epitope prediction over BIMAS and ease of use for bioinformaticians. (Cancer Sci 2011; 102: 690-696)

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“…Peptides that can bind to MHC on the tumour cell surface have potential to initiate a host immune response against the tumour. Schiewe and Haworth 101 developed an algorithm P e SSI (peptide–MHC prediction of structure through solvated interfaces) for flexible structure prediction of peptide binding to the MHC molecule. They used CT antigens (Cancer Testis), KU‐CT‐1, that have the potential to bind HLA‐A2.…”

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confidence: 99%

Immunoinformatics: an integrated scenario

Tomar

2010

Immunology

129

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SummaryGenome sequencing of humans and other organisms has led to the accumulation of huge amounts of data, which include immunologically relevant data. A large volume of clinical data has been deposited in several immunological databases and as a result immunoinformatics has emerged as an important field which acts as an intersection between experimental immunology and computational approaches. It not only helps in dealing with the huge amount of data but also plays a role in defining new hypotheses related to immune responses. This article reviews classical immunology, different databases and prediction tools. It also describes applications of immunoinformatics in designing in silico vaccination and immune system modelling. All these efforts save time and reduce cost.

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Structure-based prediction of MHC–peptide association: Algorithm comparison and application to cancer vaccine design

Cited by 18 publications

References 40 publications

Large-scale characterization of peptide-MHC binding landscapes with structural simulations

Large-scale characterization of peptide-MHC binding landscapes with structural simulations

Analysis of HLA‐A24‐restricted peptides of carcinoembryonic antigen using a novel structure‐based peptide‐HLA docking algorithm

Immunoinformatics: an integrated scenario

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