sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

Abstract

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.

HLA class I binding prediction via convolutional neural networks

“…Various other dimensional size were explored, however, 15-dimensions gave the best results on 10-fold cross-validation of HLA-A*02:01 subtype. The entire post-processed dataset by Luo et al [Luo et al, 2016]…”

“…Two commonly used evaluation metric for peptide binding prediction task are the Spearman's rank correlation coefficient (SRCC) and area under the receiver operating characteristic curve (AUC). The state-of-the-art NetMHCpan [Andreatta et al, 2015, Trolle et al, 2015, a shallow feed forward neural network, and a more recently developed bipartite network-based algorithm, sNebula [Luo et al, 2016], will be used to compared the performance of our proposed HLA-CNN prediction model.…”

“…To control for data pre-processing variabilities, we decided to use an existing post-processed training dataset so prediction algorithms could be more directly compared. The dataset used was filtered, processed, and prepared by Luo et al [Luo et al, 2016]. This dataset contained HLA class I binding data currated from four widely used, publicly available MHC datasets: IEDB [Vita et al, 2015], AntiJen [Toseland et al, 2005], MHCBN [Lata et al, 2009], and SYFPEITHI [Rammensee et al, 1999].…”

“…Kim et al derived an amino acid similarity matrix ]. Luo et al proposed both a colored and non-colored bipartite networks [Luo et al, 2016]. Shallow and high-order artificial neural networks were proposed from various labs [Hoof et al, 2009, Koch et al, 2013, Kuksa, 2015, Nielsen et al, 2003.…”

DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

“…HLAs play a critical role helping our 23 immune system recognizing pathogens by binding to peptide fragments derived from 24 pathogens and exposing them on the cell surface for recognition by appropriate T cells. 25 Study of the binding mechanism between peptides and HLAs can help improve our 26 understanding of human immune system and boost the development of protein-based 27 vaccines and drugs [12,13]. Out of all classes of HLAs, we are interested in two major 28 classes: class I and II.…”