Commentary - Journal of Cancer Immunology (2021) Volume 3, Issue 1
Expanding the Cancer Neoantigen Peptide Repertoire beyond In silico Tools
Amit Jain1, Jackwee Lim2*
1National Cancer Centre Singapore, 11 Hospital Crescent, Singapore 169610
2Singapore Immunology Network, Agency for Science, Technology and Research, 8A Biomedical Grove, Immunos, Singapore
- *Corresponding Author:
- Jackwee Lim
Received date: January 08, 2021; Accepted date: February 23, 2021
Citation: Jain A, Lim. Expanding the Cancer Neoantigen Peptide Repertoire beyond In silico Tools. J Cancer Immunol. 2021; 3(1):
Copyright: © 2021 Jain A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
CD8+ cytotoxic T cells recognise and kill cancer cells that present immunogenic peptides bound to the cell surface major histocompatibility complex class I (MHC-I) molecules. The immunogenicity of these peptides derives from them being recognised as non-self after their parent proteins are intracellularly processed and presented as peptide-major histocompatibility complex class I (pMHC-I) complexes. pMHC specific T cell receptor (TCR) recognition then leads to cytotoxic T cell response. Generally, robust pMHC-I binding is needed for a chance encounter with a pMHC specific TCR bearing CD8+ T cell derived from in vivo T cell evolution and thymic selection. As such, pMHC binding and stability are the key starting points towards understanding the T cell response. With current technologies, peptide and MHC interaction may be deduced from either direct pMHC binding/ stability assays or in silico prediction tools, and each method has its advantages and disadvantages (Table 1). Nonetheless, the ideal method should not exclude peptides that are potentially immunogenic. Hence there is continued need for more accurate high-throughput methods that assess the natural physico-biochemical interaction between peptide and MHC molecules.
|In silico algorithms|
|• Fairly accurate and fast|
• Free public access
|• Lack of biophysical characterization|
• Restricted binding motifs
• Numerous candidates
• Missed hits
|• Natural processing of protein antigen and|
presentation of peptide antigens
• Limited multiplexing
|• Peptide recovery challenge|
• Complex data analysis
• Mixed results immunogenicity in humans
|In vitro assays|
e.g. ELISA, ProImmune
REVEAL and Immunitrack
|• Early biophysical characterization|
• Unrestricted peptide types
|• Multiple-step irreproducibility|
• Sample preparation challenge
• Hypothetical candidates
|EZ MHC-I assay||• Early biophysical characterization|
• Unrestricted peptide types
• Direct band visualization of MHC-I protein
• Shortest hands-on time among current in vitro assays
|• Limited to MHC-I alleles|
• Production of suitable MHC-I molecule
• Hypothetical candidates
Table 1: Advantages and disadvantages of different T cell epitope prediction resources.
Lim has recently developed the EZ MHC-I assay using the pMHC-I single chain trimer (SCT) molecule to enable a direct interrogation of the MHC ligandome predicted in silico or derived from patients’ samples. It is an assay based on empty MHC-I protein fragmentation to rapidly characterize bound peptides for affinity and stability . This will exclude predicted binders which do not stabilize the MHC-I molecule, identify missed hits, and potentially enable neoantigen discovery with better characterized peptides. Here, we describe the challenges of neoantigen selection, share missed hits identified by the EZ MHC-I assay, present the EZ MHC-I assay in greater details, and propose future SCT-based applications towards CD8+ T cell specific neoantigen discovery.
The hunt for immunogenic peptides
Immunogenic T cell antigens have been well characterized in the context of infectious diseases where the entire antigen from viral proteins is “non-self” and hence immunogenic. On the other hand, the identification of “immunogenic” cancer antigens or neoantigens is more challenging [2,3]. While in methylcholanthrene-induced mouse models, several immunogenic peptide sequences have been identified , human cancer mutations are largely patient specific and therefore bespoke, notwithstanding intratumoral heterogeneity. Broadly, cancer antigens comprise of cancer specific overexpressed proteins, viral proteins in virus associated cancers, and specific peptides derived from nonsynonymous mutations, deletions, or translocations. Several bed to bench clinical translational studies of cancer immunotherapy have unravelled that stable pMHC-T cell interactions are requisite for T cell cytotoxicity against cancer [5,6]. However, identifying stable pMHC molecules is experimentally laborious and in silico predictions remain under scrutiny [7-9]. Nonetheless, tremendous efforts to characterize neoantigen pools with bonafide T cell responses in patients responding to immune checkpoint inhibitors have led to clinical development of therapeutic cancer peptide vaccines by companies such as Gritstone Oncology and Genocea [10,11]. Presently,
potential neoantigens are identified using paired normal genomic sequencing of both DNA and RNA. This creates
a large pool of mutation specific peptides which are then
mapped to the patient specific human leukocyte antigen
(HLA). Mass spectrometry and a large literature of
experimental data have enabled the development of more
accurate in silico prediction algorithms to identify peptides that are likely to bind stably in a MHC allelic groove . Furthermore, the discovery of immune checkpoint
receptors, CTLA-4 and PD-1  that abrogate T cell
responses against cancer has led to a reinvigoration of the
study of pMHC binding to identify therapeutic targets ,
especially in the context of patients treated with immune
checkpoint inhibitors to relieve CD8+ T cell exhaustion
[15,16]. However, not all cancer mutations are similarly
immunogenic, and immunogenicity is possibly driven by
the duration of MHC peptide binding and pMHC-TCR
bond conformation to trigger a T cell response [17,18].
Hence careful characterization of stable pMHC-I molecules
can lead to the identification of neoantigens more likely to
trigger tumor-specific T cell mediated antitumor immune
response, and potentially drive the success of neoantigen
derived cancer vaccine therapy [19-22].
An in silico challenge: predicting unconventional peptide binding in MHC-I groove
Early neoantigen discovery based on early proofs of peptide antigens presented on cell surfaces propelled the development of pMHC-I/ TCR related applications [23,24]. Currently, prediction of neoantigens by bioinformatics remains a popular approach prior to experimental validation. Indeed, prediction algorithms such as NetMHCpan, MHCSeqNet, MHCflurry and NetMHCstab have become highly reliable in predicting MHC-peptide binding affinity or stability [11,12,25-27]. This initially
propelled the clinical development of neoantigen vaccines
by both academia [28-30] and companies such as Neon,
BioNTech, and subsequently also in combination with
immune checkpoint inhibitors such as in the clinical
trials run by Moderna and Merck [31,32]. However, the suboptimal nature of in silico prediction has also driven companies such as Gritstone Oncology and Genocea to develop patient data-trained deep learning tools or in vitro cellular methods, respectively to improve the early identification of potential neoantigens [10,11]. Hence
further characterization of these neoantigens for their
stability will favor more optimized therapeutics. Indeed
recent studies on experimentally validated neoantigen
have shown that both binding affinity and complex stability
are key parameters of a pMHC molecule to stimulate
patient tumor infiltrating lymphocytes and mount an
immunogenic response [18,33]. However, urea denaturing
methods to measure actual pMHC-I stability are tedious
and predicting the energetic stability of a pMHC complex
can be computationally inaccurate, a grand challenge
to consider the entropic and enthalpic factors even for
the protein folding community [34,35]. Indeed, a large
majority of predicted neoantigens do not elicit T-cell
responses as only a small fraction is capable of presentation
as cell surface pMHC and subsequent recognition by the
rare TCR. Therefore, actual measurement of pMHC-I
affinity and stability can potentially improve the reliability
of predicted peptides [7,36]. Indeed, an additional 60%
of predicted epidermal growth factor receptor (EGFR)
mutated peptide candidates was found using the EZ
MHC-I assay, which remain to be validated in future
patient studies (Figure 1).
Favoring biophysical characterization with
easier pMHC-I production
To date, the human MHC gene, also known as HLA has been strongly associated with hundreds of disease and thus play a pivotal role in disease susceptibility genetic testing . Several structural studies have also advanced our understanding of different immunogenic peptides bound within the MHC-I/II protein groove, including the development of automated modeling albeit selected MHC-I allotypes . X-ray crystallography has further revealed peptides of 10/11 amino acids, which can bind the MHC-I groove in either a zig-zag or bulging manner, while anchoring its N and C termini into the A and F pockets, respectively . Moreover, several highresolution structures have also shown non-canonical peptide lengths of up to 16-mer exiting out at the F pocket [40-43]. These suggest that longer peptide lengths can adopt unconventional binding modes within the enclosed groove of MHC-I protein, previously thought to be unique to the more opened groove of MHC-II protein. Also, longer peptide precursors of MHC-I do undergo trimming by endoplasmic reticulum aminopetidases 1 and 2 along the transporter associated with antigen processing machinery to be shorten to a more preferable 9- to 13-mer and adopt multiple bulging conformations in the MHC-I binding groove [44-46]. Taken together, the binding of peptide in the MHC-I groove is not always flat and thus unpredictable in silico.
To enable real biophysical characterization, different pMHC-I molecules have to be produced using recombinant methods [1,47-51]. Stable pMHC-I molecules are traditionally used in cytometry for probing CD8+ T cells but unsuitable reagents for peptide-exchange. Thus Bakker et al. used a photolabile peptide cleavable by UV irradiation to make empty MHC-I molecules to enable peptide exchange . Similarly, advances in peptide-exchange technologies include peptide deficient MHC-I/TAPasin binding protein related complexes and thermal exchangeable pMHC-I molecules to overcome possible photodamage using UV cleavable pMHC-I molecules [49,50]. More importantly, to encourage biophysical characterization, secreted peptide
exchangeable SCT proteins were successfully made using
the mellitin-based baculovius expression vector system
(mBEVS, mellitin leader: MKFLVNVALVFMVVYISYIYA)
. A major technical advantage of a secreted SCT is the rapid purification of functional pMHC-I protein in hours instead of several days using the traditional E. coli. system
[52,53]. Hence the time-saving mBEVS method is more likely to create more SCT fusion analogues compared to current tedious in vitro refolding methods, and favor biophysical characterization of pMHC-I molecules [47,54].
Challenges of developing the EZ MHC-I assay
However, identifying a peptide exchangeable SCT, which is suitable for EZ MHC-I assay can be challenging. A primary limitation is the wrong choice of a peptide can often result in insoluble SCT inclusion bodies, an indication of misfolded protein. Therefore, different peptides interacting weakly with the A and F pockets of the MHC-I groove were screened and evaluated for secreted soluble SCT protein. Additionally, these peptides unless cleaved, are covalently tethered to the N-terminal of the human β2-microglobulin chain to stabilize the original pMHC-I molecule . More importantly, the tethered peptides should readily dissociate from the MHC-I molecule when cleaved as previously described . For example, known HLA-A*02:01 epitopes KILGRVFFV/ KLLTKILTI and HLA-A*02:07 epitope FLPSDYFPSV were found nonexchangeable and thus unsuitable for EZ MHC-I assay. Nonetheless, suitable SCT proteins were successfully produced for EZ MHC-I assay .
To encourage actual physico-biochemical measurement of stable MHC-I peptide binding, we have also significantly reduced the time spent in pMHC-I binding assays by eliminating traditional enzyme-linked immunosorbent assay (ELISA) methodology. Here, blocking and washing steps in standard ELISA were removed. Instead the EZ MHC-I assay is a de novo approach of direct protein fragmentation . EZ MHC-I assay is developed based on a combination of unfavorable observations; First, emptied MHC-I proteins were previously known to destabilize and dissociate into α–heavy and β2-microglobulin light chains . Second, fusion protein when destabilized will partially unfold and become more susceptible to non-specific cleavage . Third, enterokinase has been reported to be a non-specific protease in some cases [57,58]. Taken together, these unfavorable observations were successfully incorporated to make a SCT fusion protein, which results in enterokinase-induced fragments in the absence of a rescue peptide.
In search for more neoantigens
Pipeline to identify neoantigen-specific T cells in blood and tumor samples still remain challenging. Presently, neogantigens due to non-synonymous mutation can be identified using next generation sequencing techniques but still require filtering of tumor DNA against germline DNA and the subsequent identification of private neoantigens unique to different patients. However, the latter requires selecting peptides that are either naturally processed or presented in tumor cells. Moreover, to date, peptide selection using in silico algorithms can still generate a high number of predicted candidates, especially for cancers with high mutational load. Nonetheless, immunogenicity validation of these numerous peptide candidates has been successful in the neoantigen discovery in melanoma and glioblastoma [29,59], but is costly. Thus reducing
the peptide pool while improving the quality of peptide
candidates are relevant to identifying more neoantigens.
However, biophysical characterization of a large number
of peptides can be technically laborious and also require
the MHC-I molecule. In this commentary, the EZ MHC-I
assay uses the pMHC-I SCT molecules and offers a fast
and hassle-free approach to screen large peptide libraries
which form stable pMHC-I molecules prior to expensive
patient sample screening. Besides the EZ MHC-I assay, the
rapid mBEVS pMHC-I SCT protein production can also
attract more users and promote SCT-based applications
for detecting antigenic CD8+ T cells. The C-terminal end,
away from the peptide binding groove of the SCT molecule
still remains highly modifiable. Possible modifications
include the additional of a BirA recognition sequence for
biotinylation for streptavidin tagging , coiled-coil motif
for multimerization  and the incorporation of clickable
chemical groups for bioconjugation to oligonucleotides
 or as fusion protein [50,63]. Hence the feasible
manipulation of the C-terminal end in the pMHC-I SCT
molecule would undoubtedly create many tools for cellular
cytometry, cellular assays and imaging studies to identify
undiscovered tumor neoantigens (Figure 1).
Moving forward, the success of neoantigen discovery and cancer vaccine largely requires a pool of predicted MHC peptides with qualities of good affinity and stability. This commentary sheds light on possible missed hits, which remain unaccounted for in silico due to non-conventional environmental factors and opens doors for the EZ MHC-I assay or similar experimental binding/stability assays. Additionally, the use of mBEVS for rapid pMHC-I SCT protein production bearing different C-terminal modifications may create new technologies to unveil antitumor CD8+ cytotoxic T cells.
Authors acknowledge financial support by the Singapore Immunology Network (SIgN) core grant from A*STAR. Authors also acknowledge the National Cancer Centre, Singapore for financial support by the NCCRF Grant NCCSPG-YR2016-JAN-18.
- Lim J. Destabilizing single chain major
histocompatibility complex class I protein for repurposed
enterokinase proteolysis. Scientific Reports. 2020 Sep
- Zhang H, Zhou X, Liu D, Zhu Y, Ma Q, Zhang Y. Progress
and challenges of personalized neoantigens in the clinical
treatment of tumors. Medicine in Drug Discovery. 2020
- Garcia-Garijo A, Fajardo CA, Gros A. Determinants for
neoantigen identification. Frontiers in Immunology. 2019
- Kono K, Petersson M, Ciupitu AM, Wen T, Klein G,
Kiessling R. Methylcholanthrene-induced mouse sarcomas
express individually distinct major histocompatibility
complex class I-associated peptides recognized by
specific CD8+ T-cell lines. Cancer Research. 1995 Dec
- Riquelme E, Carreño LJ, González PA, Kalergis AM.
The duration of TCR/pMHC interactions regulates CTL
effector function and tumor-killing capacity. European
Journal of Immunology. 2009 Aug;39(8):2259-69.
- Harao M, Hirata S, Irie A, Senju S, Nakatsura T, Komori
H, et al. HLA-A2-restricted CTL epitopes of a novel lung
cancer-associated cancer testis antigen, cell division cycle
associated 1, can induce tumor-reactive CTL. International
Journal of Cancer. 2008 Dec 1;123(11):2616-25.
- Rasmussen M, Fenoy E, Harndahl M, Kristensen AB,
Nielsen IK, Nielsen M, et al. Pan-specific prediction of
peptide–MHC class I complex stability, a correlate of T cell
immunogenicity. The Journal of Immunology. 2016 Aug
- Abella JR, Antunes DA, Clementi C, Kavraki LE.
Large-scale structure-based prediction of stable peptide
binding to class i hlas using random forests. Frontiers in
Immunology. 2020 Jul 22;11:1583.
- Serçinoglu O, Ozbek P. Sequence-structure-function
relationships in class I MHC: A local frustration
perspective. PloS One. 2020 May 18;15(5):e0232849.
- Lam H, McNeil LK, Starobinets H, DeVault VL, Cohen
RB, Twardowski P, et al. An Empirical Antigen Selection
Method Identifies Neoantigens That Either Elicit Broad
Antitumor T-cell Responses or Drive Tumor Growth.
Cancer Discovery. 2021 Jan 27.
- Bulik-Sullivan B, Busby J, Palmer CD, Davis MJ,
Murphy T, Clark A, et al. Deep learning using tumor HLA
peptide mass spectrometry datasets improves neoantigen
identification. Nature Biotechnology. 2019 Jan;37(1):55-
- Jørgensen KW, Rasmussen M, Buus S, Nielsen M.
Net MHC stab–predicting stability of peptide–MHC-I
complexes; impacts for cytotoxic T lymphocyte epitope
discovery. Immunology. 2014 Jan;141(1):18-26.
- Gubin MM, Zhang X, Schuster H, Caron E, Ward
JP, Noguchi T, et al. Checkpoint blockade cancer
immunotherapy targets tumour-specific mutant antigens.
Nature. 2014 Nov;515(7528):577-81.
- Høydahl LS, Frick R, Sandlie I, Løset GÅ. Targeting
the MHC ligandome by use of TCR-like antibodies.
Antibodies. 2019 Jun;8(2):32.
- Pfannenstiel LW, Diaz-Montero CM, Tian YF,
Scharpf J, Ko JS, Gastman BR. Immune-checkpoint
blockade opposes CD8+ T-cell suppression in human and
murine Cancer. Cancer Immunology Research. 2019 Mar
- Grywalska E, Pasiarski M, Gózdz S, Rolinski J.
Immune-checkpoint inhibitors for combating T-cell
dysfunction in cancer. OncoTargets and Therapy.
- Sasmal DK, Feng W, Roy S, Leung P, He Y, Cai C, et
al. TCR–pMHC bond conformation controls TCR ligand
discrimination. Cellular & Molecular Immunology. 2020
- Wells DK, van Buuren MM, Dang KK, Hubbard-Lucey
VM, Sheehan KC, Campbell KM, et al. Key parameters
of tumor epitope immunogenicity revealed through a
consortium approach improve neoantigen prediction. Cell.
2020 Oct 29;183(3):818-34.
- De Plaen E, Lurquin C, Van Pel A, Mariamé B, Szikora
JP, Wölfel T, et al. Immunogenic (tum-) variants of mouse
tumor P815: cloning of the gene of tum-antigen P91A and
identification of the tum-mutation. Proceedings of the
National Academy of Sciences. 1988 Apr 1;85(7):2274-8.
- Robbins PF, El-Gamil M, Li YF, Kawakami Y,
Loftus D, Appella E, et al. A mutated beta-catenin gene
encodes a melanoma-specific antigen recognized by
tumor infiltrating lymphocytes. Journal of Experimental
Medicine. 1996 Mar 1;183(3):1185-92.
- Brändle D, Brasseur F, Weynants P, Boon T, Van
den Eynde B. A mutated HLA-A2 molecule recognized by
autologous cytotoxic T lymphocytes on a human renal cell carcinoma. The Journal of Experimental Medicine. 1996
- Peng M, Mo Y, Wang Y, Wu P, Zhang Y, Xiong F, et al.
Neoantigen vaccine: an emerging tumor immunotherapy.
Molecular Cancer. 2019 Dec;18(1):1-4.
- DeWeerdt S. Calling cancer’s bluff with neoantigen
vaccines. Nature. 2017 Dec 21;552(7685).
- Jiang T, Shi T, Zhang H, Hu J, Song Y, Wei J, et
al. Tumor neoantigens: from basic research to clinical
applications. Journal of Hematology & Oncology. 2019
- O’Donnell TJ, Rubinsteyn A, Laserson U. MHCflurry
2.0: Improved pan-allele prediction of MHC class
I-presented peptides by incorporating antigen processing.
Cell Systems. 2020 Jul 22;11(1):42-8.
- Phloyphisut P, Pornputtapong N, Sriswasdi S,
Chuangsuwanich E. MHCSeqNet: a deep neural network
model for universal MHC binding prediction. BMC
Bioinformatics. 2019 Dec;20(1):1-0.
- Reynisson B, Alvarez B, Paul S, Peters B, Nielsen
M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved
predictions of MHC antigen presentation by concurrent
motif deconvolution and integration of MS MHC
eluted ligand data. Nucleic Acids Research. 2020 Jul
- Ott PA, Hu Z, Keskin DB, Shukla SA, Sun J, Bozym
DJ, et al. An immunogenic personal neoantigen vaccine for
patients with melanoma. Nature. 2017 Jul;547(7662):217-
- Carreno BM, Magrini V, Becker-Hapak M,
Kaabinejadian S, Hundal J, Petti AA, Ly A, Lie WR,
Hildebrand WH, Mardis ER, Linette GP. A dendritic
cell vaccine increases the breadth and diversity of
melanoma neoantigen-specific T cells. Science. 2015 May
- Sahin U, Derhovanessian E, Miller M, Kloke BP,
Simon P, Löwer M, et al. Personalized RNA mutanome
vaccines mobilize poly-specific therapeutic immunity
against cancer. Nature. 2017 Jul;547(7662):222-6.
- Burris III HA, Patel MR, Cho DC, Clarke JM, Gutierrez
M, Zaks TZ, et al. A phase 1, open-label, multicenter study
to assess the safety, tolerability, and immunogenicity of
mRNA-4157 alone in subjects with resected solid tumors
and in combination with pembrolizumab in subjects with
unresectable solid tumors (Keynote-603). J. Glob. Oncol.
- An Efficacy Study of Adjuvant Treatment With the Personalized Cancer Vaccine mRNA-4157 and Pembrolizumab in Patients With High-Risk Melanoma (KEYNOTE-942). Case Med. Res. (2019) https:/doi.
- Harndahl M, Rasmussen M, Roder G, Dalgaard
Pedersen I, Sørensen M, Nielsen M, et al. Peptide-MHC
class I stability is a better predictor than peptide affinity of
CTL immunogenicity. European Journal of Immunology.
- Caro JA, Harpole KW, Kasinath V, Lim J, Granja J,
Valentine KG, et al. Entropy in molecular recognition by
proteins. Proceedings of the National Academy of Sciences.
2017 Jun 20;114(25):6563-8.
- Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre
L, Green T, et al. Improved protein structure prediction
using potentials from deep learning. Nature. 2020
- Schmidt J, Guillaume P, Dojcinovic D, Karbach J,
Coukos G, Luescher I. In silico and cell-based analyses
reveal strong divergence between prediction and
observation of T-cell–recognized tumor antigen T-cell
epitopes. Journal of Biological Chemistry. 2017 Jul
- Gao J, Zhu C, Zhu Z, Tang L, Liu L, Wen L, et al.
The human leukocyte antigen and genetic susceptibility
in human diseases. Journal of Bio-X Research. 2019 Sep
- Rigo MM, Antunes DA, De Freitas MV, de Almeida
Mendes MF, Meira L, Sinigaglia M, et al. DockTope: a Webbased
tool for automated pMHC-I modelling. Scientific
Reports. 2015 Dec 17;5(1):1-3.
- Guo HC, Jardetzky TS, Garrettt TP, Lane WS,
Strominger JL, Wiley DC. Different length peptides bind
to HLA-Aw68 similarly at their ends but bulge out in the
middle. Nature. 1992 Nov;360(6402):364-6.
- McMurtrey C, Trolle T, Sansom T, Remesh SG, Kaever
T, Bardet W, et al. Toxoplasma gondii peptide ligands open
the gate of the HLA class I binding groove. Elife. 2016 Jan
- Collins EJ, Garboczi DN, Wiley DC. Three-dimensional
structure of a peptide extending from one end of a class I
MHC binding site. Nature. 1994 Oct;371(6498):626-9.
- Guillaume P, Picaud S, Baumgaertner P, Montandon
N, Schmidt J, Speiser DE, et al. The C-terminal extension
landscape of naturally presented HLA-I ligands.
Proceedings of the National Academy of Sciences. 2018
- Josephs TM, Grant EJ, Gras S. Molecular challenges imposed by MHC-I restricted long epitopes on T cell immunity. Biological Chemistry. 2017 Aug 28;398(9):1027-36.
- Ayres CM, Corcelli SA, Baker BM. Peptide and peptidedependent
motions in MHC proteins: immunological
implications and biophysical underpinnings. Frontiers in
Immunology. 2017 Aug 7;8:935.
- Howarth M, Williams A, Tolstrup AB, Elliott T.
Tapasin enhances MHC class I peptide presentation
according to peptide half-life. Proceedings of the National
Academy of Sciences. 2004 Aug 10;101(32):11737-42.
- Ekeruche-Makinde J, Miles JJ, Van Den Berg
HA, Skowera A, Cole DK, Dolton G, et al. Peptide
length determines the outcome of TCR/peptide-MHCI
engagement. Blood. 2013 Feb 14;121(7):1112-23.
- Garboczi DN, Hung DT, Wiley DC. HLA-A2-peptide
complexes: refolding and crystallization of molecules
expressed in Escherichia coli and complexed with single
antigenic peptides. Proceedings of the National Academy
of Sciences. 1992 Apr 15;89(8):3429-33.
- Yik YL, Netuschil N, Lybarger L, Connolly JM,
Hansen TH. Cutting edge: single-chain trimers of MHC
class I molecules form stable structures that potently
stimulate antigen-specific T cells and B cells. The Journal
of Immunology. 2002 Apr 1;168(7):3145-9.
- Luimstra JJ, Franken KL, Garstka MA, Drijfhout JW,
Neefjes J, Ovaa H. Production and thermal exchange of
conditional peptide-MHC I multimers. Current Protocols
in Immunology. 2019 Sep;126(1):e85.
- Overall SA, Toor JS, Hao S, Yarmarkovich M,
O’Rourke SM, Morozov GI, et al. High throughput pMHC-I
tetramer library production using chaperone-mediated
peptide exchange. Nature Communications. 2020 Apr
- Bakker AH, Hoppes R, Linnemann C, Toebes M,
Rodenko B, Berkers CR, et al. Conditional MHC class
I ligands and peptide exchange technology for the
human MHC gene products HLA-A1,-A3,-A11, and-B7.
Proceedings of the National Academy of Sciences. 2008
- Kim S, Zuiani A, Carrero JA, Hansen TH. Single chain
MHC I trimer-based DNA vaccines for protection against
Listeria monocytogenes infection. Vaccine. 2012 Mar
- Hansen TH, Lybarger L. Exciting applications of single
chain trimers of MHC-I molecules. Cancer Immunology,
Immunotherapy. 2006 Feb 1;55(2):235.
- Schmittnaegel M, Hoffmann E, Imhof-Jung S, Fischer
C, Drabner G, Georges G, et al. A new class of bifunctional
major histocompatibility class I antibody fusion molecules
to redirect CD8 T cells. Molecular Cancer Therapeutics.
2016 Sep 1;15(9):2130-42.
- Parker KC, DiBrino M, Hull L, Coligan JE. The beta
2-microglobulin dissociation rate is an accurate measure
of the stability of MHC class I heterotrimers and depends
on which peptide is bound. The Journal of Immunology.
1992 Sep 15;149(6):1896-904.
- Kühnel B, Alcantara J, Boothe J, van Rooijen G,
Moloney M. Precise and efficient cleavage of recombinant
fusion proteins using mammalian aspartic proteases.
Protein Engineering. 2003 Oct 1;16(10):777-83.
- Shahravan SH, Qu X, Chan IS, Shin JA. Enhancing the
specificity of the enterokinase cleavage reaction to promote
efficient cleavage of a fusion tag. Protein Expression and
Purification. 2008 Jun 1;59(2):314-9.
- Liu Q, Lin J, Liu M, Tao X, Wei D, Ma X, et al. Largescale
preparation of recombinant human parathyroid
hormone 1–84 from Escherichia coli. Protein Expression
and Purification. 2007 Aug 1;54(2):212-9.
- Johanns TM, Dunn GP. Applied cancer
immunogenomics: leveraging neoantigen discovery in
glioblastoma. Cancer Journal (Sudbury, Mass.). 2017
- Fairhead M, Howarth M. Site-specific biotinylation
of purified proteins using BirA. InSite-Specific Protein
Labeling 2015 (pp. 171-184). Humana Press, New York,
- Burkhard P, Stetefeld J, Strelkov SV. Coiled coils:
a highly versatile protein folding motif. Trends in Cell
Biology. 2001 Feb 1;11(2):82-8.
- Hara S, Nojima T, Seio K, Yoshida M, Hisabori T.
DNA-maleimide: an improved maleimide compound
for electrophoresis-based titration of reactive thiols in
a specific protein. Biochimica et Biophysica Acta (BBA)-
General Subjects. 2013 Apr 1;1830(4):3077-81.
- Dahotre SN, Chang YM, Romanov AM, Kwong
GA. DNA-barcoded pMHC tetramers for detection of
single antigen-specific T cells by digital PCR. Analytical
Chemistry. 2019 Jan 18;91(4):2695-700.