Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

With the advent of next-generation DNA sequencing machines, there is an increasing need for the development of computational tools that can anchor accurately and expediently the millions of generated short DNA sequences (or reads) onto the genomes of target organisms. In this work, we describe 'Q-Pick', a new and efficient method for solving this problem. Q-Pick allows the rapid identification and anchoring of such reads with possible wildcards in large genomic databases, while guaranteeing completeness of results and efficiency of operation. Q-Pick requires very spartan memory and computational resources, and is trivially amenable to SIMD implementation; it can also be easily extended to handle longer reads, e.g. 75-mers or longer. Our experiments indicate that Q-Pick can anchor millions of distinct short reads against both strands of a mammalian genome in seconds, using a single-core computer processor.

A technique is presented for comparing the performance of functional languages with different evaluation strategies running on different machines. A set of small benchmarks is used, and th execution times of these programs running in the functional language and in the implementation language of the functional system are compared. The ratio of these execution times measured how well the functional system used the resources of the underlying hardware and implementation language. Also two functional programming systems are described. One system is a graph reduction interpreter for lambda calculus. The other is a DEL-style intermediate instruction set architecture for FP. The benchmarks in FP and the performances of the two systems on these benchmarks are presented.>