Modeling the structure of bound peptide ligands to major histocompatibility complex

Tong, Joo Chuan; Tan, Tin Wee; Ranganathan, Shoba

doi:10.1110/ps.04631204

Cited by 84 publications

(93 citation statements)

References 30 publications

(29 reference statements)

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“…The edges were docked as rigid bodies, and the rest of the peptide was placed using a loop-closure technique, achieving Ca RMSD \1.5 Å for 9-to 10-aminoacid-long peptides (heavy atom RMSD was not reported). 87 Well-defined anchoring points of peptides to proteins such as in MHC-peptide binding also occur in other cases: many linear motifs have a well-defined docking region for the conserved residues but more variable docking areas for the residues flanking the motif. 88 Two of the applications described below 81,83 utilize the anchoring point idea and succeed not only in redocking, but also in the much more challenging task of docking peptides to other structures and to homology models.…”

Section: Structure-based Approaches For Peptide Design and Optimizationmentioning

confidence: 99%

Peptidic modulators of protein‐protein interactions: Progress and challenges in computational design

Rubinstein

Niv

2009

Biopolymers

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With the decline in productivity of drug‐development efforts, novel approaches to rational drug design are being introduced and developed. Naturally occurring and synthetic peptides are emerging as novel promising compounds that can specifically and efficiently modulate signaling pathways in vitro and in vivo. We describe sequence‐based approaches that use peptides to mimic proteins in order to inhibit the interaction of the mimicked protein with its partners. We then discuss a structure‐based approach, in which protein‐peptide complex structures are used to rationally design and optimize peptidic inhibitors. We survey flexible peptide docking techniques and discuss current challenges and future directions in the rational design of peptidic inhibitors. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 505–513, 2009.This article was originally published online as an accepted preprint. The “Published Online”date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com

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Section: Structure-based Approaches For Peptide Design and Optimizationmentioning

confidence: 99%

Peptidic modulators of protein‐protein interactions: Progress and challenges in computational design

Rubinstein

Niv

2009

Biopolymers

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“…The next step for this project is to show how it results can be applied to a clinical setting to determine the effectiveness of a cancer vaccine prior to a host developing cancer. For this part of the study, three processed antigens from the TANTIGEN database were utilized for the MonteCarlo Simulator, which is: 1) Survivin, 2) Kita-Kyushu lung cancer antigen 1 (KKLC1), and 3) Epidermal growth factor receptor (EGFR) [24]. Figure 1A depicts a normal immune response to cancer (C=200,000).…”

Section: Resultsmentioning

confidence: 99%

“…The initial stages of development of this model confirmed prior knowledge of the innate and adaptive immune system; for example, CD8 cells are the principal cell-mediated immunity for cancer as well as dendritic cells being the principal cell during the innate immune response. Three antigens Survivin, KKLC1, and EGFR [24] were utilized from the TANTIGEN database to predict an immune response once cancer was detected within an individual following utilization of a synthetic vaccine. In conclusion, we have for the first time applied mathematical modeling as a tool to depict the relative strength of a host's immune response after it has been subjected to a lung tumor vaccine.…”

Section: Resultsmentioning

confidence: 99%

“…Synthetic survivin can also exist as fragment lengths of 9 or 11 as long and can interact with molecules of the HLA-A family. We utilized to TANTIGEN database to look for possible pairings of epitopes to specific HLA molecules; from a TANTIGEN database search, an HLA molecule that can bind with the best affinity to Survivin molecules is HLA-A11:02 mainly due to the number of patterns that can bind to the molecule [24]. This model, although useful in predicting the long-term status of a patient, cannot effectively predict which antigen epitopes and HLA combinations will produce a strong immune response due to the current nature of the model.…”

Section: Discussionmentioning

confidence: 99%

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A Mathematical Investigation on Tumor-Immune Dynamics: The Impact of Vaccines on the Immune Response

Quinonez¹,

Dasu²,

Qureshi³

2017

J Cancer Sci Ther

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Mathematical models analyzing tumor-immune interactions provide a framework by which to address specific scenarios in regard to tumor-immune dynamics. Important aspects of tumor-immune surveillance to consider is the elimination of tumor cells from a host's cell-mediated immunity as well as the implications of vaccines derived from synthetic antigen. In present studies, our mathematical model examined the role of synthetic antigen to the strength of the immune system. The constructed model takes into account accepted knowledge of immune function as well as prior work done by de Pillis et al. All equations describing tumor-immune growth, antigen presentation, immune response, and interaction rates were numerically simulated with MATLAB. Here, our work shows that a robust immune response can be generated if the immune system recognizes epitopes that are between 8 to 11 amino acids long. We show through mathematical modeling of how synthetic tumor vaccines can be utilized to mitigate a developing cancer.utilized mice that were inoculated with chemically-induced cancer cells; such cells lacked the capability to metastasize within a host. Over time, this led to the development of cancer-specific immunity in the recipient mice. This discovery provided the evidence needed for Ehrlich's hypothesis. Such experiments demonstrated that it is essential to have the presence of an antigen to elicit an immune response in the host, because if no distinctive structures exist, then no recognition would be established [9]. F. Macfarlane Burnet and Lewis Thomas, well-known immunologists during the 20 th century, hypothesized that for immunosurveillance to exist, lymphocytes would need to act aggressively akin to sentinels to recognize and eliminate a cancer threat. The cancer immunosurveillance theory revolves around three transitions states, denoted as "E's" [9]:• Elimination-The establishment of a strong cancer surveillance network by both the innate and adaptive immune system that seeks to eliminate cancer populations.• Equilibrium-The long-term process of combat between a cancer population and a host's immunosurveillance network.• Escape-The overpowering of cancer surveillance network by strong tumor variants, which results in host death.

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“…Two major categories of methods have been developed to predict domain-ligand interactions. The first category is based on structure information as exemplified by the work on predicting MHC-specific epitopes with protein docking methods (5,6). This type of method needs intensive computation and the prior knowledge of the three-dimensional structures of the bait proteins.…”

mentioning

confidence: 99%

An Integrated Machine Learning System to Computationally Screen Protein Databases for Protein Binding Peptide Ligands

Zhang

Shao

Zheng

et al. 2006

Molecular & Cellular Proteomics

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A fairly large set of protein interactions is mediated by families of peptide binding domains, such as Src homology 2 (SH2), SH3, PDZ, major histocompatibility complex, etc. To identify their ligands by experimental screening is not only labor-intensive but almost futile in screening low abundance species due to the suppression by high abundance species. An ideal way of studying protein-protein interactions is to use high throughput computational approaches to screen protein sequence databases to direct the validating experiments toward the most promising peptides. Predictors with only good cross-validation were not good enough to screen protein databases. In the current study we built integrated machine learning systems using three novel coding methods and screened the Swiss-Prot and GenBank TM protein databases for potential ligands of 10 SH3 and three PDZ domains. A large fraction of predictions has already been experimentally confirmed by other independent research groups, indicating a satisfying generalization capability for future applications in identifying protein interactions. Experimental screening for protein binding peptides is not only labor-intensive but almost futile in screening for low abundance binding species due to the suppression by high abundance species. A more plausible way of studying protein-protein interactions is by using high throughput computational predictions rather than experimental approaches to screen for interactions from protein sequence databases to direct the validating experiments toward the most promising peptides. A prediction can only be called successful when the overall efficiency and cost of computational prediction plus biological validation are much better than those of experimental screening. A fairly large set of protein interactions are mediated by families of peptide binding domains, such as SH2, 1 SH3, PDZ, MHC, etc. that act as receptors to accommodate, in their binding pockets, short peptides in an extended conformation (1, 2). We studied two common domainligand interactions mediated by SH3 domain and PDZ domain. SH3 domains selectively bind peptides of 8 -11 amino acids on their ligand proteins; PDZ domains bind 4 -8 amino acids in the C termini of their partners. Ligands of both PDZ and SH3 domains are of high diversity. Two good reviews provide detailed information on SH3 domain (3) and PDZ domain (4). Two major categories of methods have been developed to predict domain-ligand interactions. The first category is based on structure information as exemplified by the work on predicting MHC-specific epitopes with protein docking methods (5, 6). This type of method needs intensive computation and the prior knowledge of the three-dimensional structures of the bait proteins. Instead of using exact protein structures, Brannetti et al. (7) and Altuvia and Margalit (8) extracted ligandcontacting residues from the known domain-ligand complex structures to approximately represent domain interface structures of SH3 and MHC, respectively. The interactions of amino aci...

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Modeling the structure of bound peptide ligands to major histocompatibility complex

Cited by 84 publications

References 30 publications

Peptidic modulators of protein‐protein interactions: Progress and challenges in computational design

Peptidic modulators of protein‐protein interactions: Progress and challenges in computational design

A Mathematical Investigation on Tumor-Immune Dynamics: The Impact of Vaccines on the Immune Response

An Integrated Machine Learning System to Computationally Screen Protein Databases for Protein Binding Peptide Ligands

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