Timothy P. Riley; Grant L. J. Keller; Angela R. Smith; Lauren M. Davancaze; Alyssa G. Arbuiso; Jason R. Devlin; Brian M. Baker · 2019 · Frontiers in Immunology
Paper
The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although neoantigens can be identified through sequencing, bioinformatics and mass spectrometry, identifying those which are immunogenic and able to promote tumor rejection remains a significant challenge. Here we examined the potential of high-resolution structural modeling followed by energetic scoring of structural features for predicting neoantigen immunogenicity. After developing a strategy to rapidly and accurately model nonameric peptides bound to the common class I MHC protein HLA-A2, we trained a neural network on structural features that influence T cell receptor (TCR) and peptide binding energies. The resulting structurally-parameterized neural network outperformed methods that do not incorporate explicit structural or energetic properties in predicting CD8+ T cell responses of HLA-A2 presented nonameric peptides, while also providing insight into the underlying structural and biophysical mechanisms governing immunogenicity. Our proof-of-concept study demonstrates the potential for structure-based immunogenicity predictions in the development of personalized peptide-based vaccines.
Analysis
This paper explores using high-resolution structural modeling and energetic scoring to predict the immunogenicity of neoantigens for personalized cancer vaccines.
Discovery
Ruby Srivastava
Karthick Vasudevan; T Dhanushkumar; Sripad Rama Hebbar; Prasanna Kumar Selvam; Majji Rambabu; K. Anbarasu; Rohini Karunakaran
Dariush Haghmorad; Majid Eslami; Niloufar Orooji; Iryna Halabitska; Iryna Kamyshna; Oleksandr Kamyshnyi; Valentyn Oksenych
Zachary Sethna; Pablo Guasp; Charlotte Reiche; Martina Milighetti; Nicholas Ceglia; Erin Patterson; Jayon Lihm; George C. Payne; Olga Lyudovyk; Luis A. Rojas; Nan Pang; Akihiro Ohmoto; Masataka Amisaki; Abderezak Zebboudj; Zagaa Odgerel; Emmanuel M. Bruno; Siqi Linsey Zhang; C. K. Cheng; Yuval Elhanati; Evelyna Derhovanessian; Luisa Manning; Felicitas Müller; Ina Rhee; Mahesh Yadav; Taha Merghoub; Jedd D. Wolchok; Olca Baştürk; Mithat Gönen; Andrew S. Epstein; Parisa Momtaz; Wungki Park; Ryan Sugarman; Anna M. Varghese; Elizabeth Won; Avni M. Desai; Alice C. Wei; Michael I. D’Angelica; T. Peter Kingham; Kevin C. Soares; William R. Jarnagin; Jeffrey A. Drebin; Eileen M. O’Reilly; Ira Mellman; Uğur Şahin; Özlem Türeci; Benjamin D. Greenbaum; Vinod P. Balachandran
David A. Braun; Giorgia Moranzoni; Vipheaviny Chea; Bradley A. McGregor; Eryn Blass; Chloe Ran Tu; Allison P. Vanasse; Cleo Forman; Juliet Forman; Alexander B. Afeyan; Nicholas Schindler; Yiwen Liu; Shuqiang Li; Jackson Southard; Steven L. Chang; Michelle S. Hirsch; Nicole R. LeBoeuf; Oriol Olive; Ambica Mehndiratta; Haley Greenslade; Keerthi Shetty; Susan Klaeger; Siranush Sarkizova; Christina B. Pedersen; Matthew Mossanen; Isabel Carulli; Anna Tarren; Joseph Duke-Cohan; Alexis A. Howard; J. Bryan Iorgulescu; Bohoon Shim; Jeremy M. Simon; Sabina Signoretti; Jon C. Aster; Liudmila Elagina; Steven A. Carr; Ignaty Leshchiner; Gad Getz; Stacey Gabriel; Nir Hacohen; Lars Rønn Olsen; Giacomo Oliveira; Donna Neuberg; Kenneth J. Livak; Sachet A. Shukla; Edward F. Fritsch; Catherine J. Wu; Derin B. Keskin; Patrick A. Ott; Toni K. Choueiri
Ting Fan; Congcong Xu; Jichuan Wu; Yihua Cai; WuQiang Cao; Haifa Shen; Mingna Zhang; Hanfei Zhu; Jingxian Yang; Zhounan Zhu; Xiaopin Ma; Jiale Ren; Lei Huang; Qianyun Li; Yuying Tang; Bo Yu; Chunxiu Chen; Mingcheng Xu; Qiuhe Wang; Zhuya Xu; Fengjia Chen; Shujing Liang; Zhixian Zhong; Anmbreen Jamroze; Dean G. Tang; Hangwen Li; Chunyan Dong
Source record