A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Published in final edited form as: Nat Biotechnol. 2020 February ; 38(2): 199–209. doi:10.1038/s41587-019-0322-9.Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.P01 CA229092 - NCI NIH HHS; P50 CA101942 - NCI NIH HHS; T32 HG002295 - NHGRI NIH HHS; T32 CA009172 - NCI NIH HHS; U24 CA224331 - NCI NIH HHS; R21 CA216772 - NCI NIH HHS; R01 CA155010 - NCI NIH HHS; U01 CA214125 - NCI NIH HHS; T32 CA207021 - NCI NIH HHS; R01 HL103532 - NHLBI NIH HHS; U24 CA210986 - NCI NIH HHSAccepted manuscrip

Outlook for inverse design in nanophotonics

Recent advancements in computational inverse design have begun to reshape the landscape of structures and techniques available to nanophotonics. Here, we outline a cross section of key developments at the intersection of these two fields: moving from a recap of foundational results to motivation of emerging applications in nonlinear, topological, near-field and on-chip optics.Comment: 13 pages, 6 figure

Ensemble preconditioning for Markov chain Monte Carlo simulation

We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas. The use of collective dynamics eliminates multiplicative noise and stabilizes the dynamics thus providing a practical approach to difficult anisotropic sampling problems in high dimensions. Numerical experiments with model problems demonstrate that dramatic potential speedups, compared to various alternative schemes, are attainable

Shape Selection in Chiral Self-Assembly

Many biological and synthetic materials self-assemble into helical or twisted aggregates. The shape is determined by a complex interplay between elastic forces and the orientation and chirality of the constituent molecules. We study this interplay through Monte Carlo simulations, with an accelerated algorithm motivated by the growth of an aggregate out of solution. The simulations show that the curvature changes smoothly from cylindrical to saddle-like as the elastic moduli are varied. Remarkably, aggregates of either handedness form from molecules of a single handedness, depending on the molecular orientation.Comment: 4 pages, including 4 postscript figures, uses REVTeX