scholarly journals AI-MHC: an allele-integrated deep learning framework for improving Class I & Class II HLA-binding predictions

2018 ◽  
Author(s):  
John-William Sidhom ◽  
Drew Pardoll ◽  
Alexander Baras
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Immune System ◽  
State Of The Art ◽  
Class Ii ◽  
Class I ◽  
Binding Prediction ◽  
Translational Invariance ◽  
Mhc Molecules ◽  
Improved Performance

AbstractMotivationThe immune system has potential to present a wide variety of peptides to itself as a means of surveillance for pathogenic invaders. This means of surveillances allows the immune system to detect peptides derives from bacterial, viral, and even oncologic sources. However, given the breadth of the epitope repertoire, in order to study immune responses to these epitopes, investigators have relied on in-silico prediction algorithms to help narrow down the list of candidate epitopes, and current methods still have much in the way of improvement.ResultsWe present Allele-Integrated MHC (AI-MHC), a deep learning architecture with improved performance over the current state-of-the-art algorithms in human Class I and Class II MHC binding prediction. Our architecture utilizes a convolutional neural network that improves prediction accuracy by 1) allowing one neural network to be trained on all peptides for all alleles of a given class of MHC molecules by making the allele an input to the net and 2) introducing a global max pooling operation with an optimized kernel size that allows the architecture to achieve translational invariance in MHC-peptide binding analysis, making it suitable for sequence analytics where a frame of interest needs to be learned in a longer, variable length sequence. We assess AI-MHC against internal independent test sets and compare against all algorithms in the IEDB automated server benchmarks, demonstrating our algorithm achieves state-of-the-art for both Class I and Class II prediction.Availability and ImplementationAI-MHC can be used via web interface at baras.pathology.jhu.edu/[email protected]

Sequence analysis of peptides presented to the immune system by class I and class II MHC molecules

1992 ◽  
Vol 11 (4) ◽  
pp. 377-378
Author(s):  
Donald F. Hunt ◽  
Jeffrey Shabanowitz ◽  
Hanspeter Michel ◽  
Andrea L. Cox ◽  
Tracey Dickinson ◽  
...  
Keyword(s):  
Immune System ◽  
Sequence Analysis ◽  
Class Ii ◽  
Class I ◽  
Mhc Molecules ◽  
Class Ii Mhc

scholarly journals DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction

2020 ◽  
Author(s):  
ZHONGHAO LIU ◽  
Jing Jin ◽  
Yuxin Cui ◽  
Zheng Xiong ◽  
Alireza Nasiri ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Structure ◽  
State Of The Art ◽  
Class Ii ◽  
Hla Class Ii ◽  
Binding Prediction ◽  
Neural Network Structure ◽  
Art Performance

Abstract Background: Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant progress in deep learning, a series of neural network based models have been proposed and demonstrated with their good performances for peptide-HLA class I binding prediction. However, there still lack effective binding prediction models for HLA class II protein binding with peptides due to its inherent challenges. In this work, we present a novel sequence-based pan-specific neural network structure, DeepSeaPanII, for peptide-HLA class II binding prediction. Compared with existing pan-specific models, our model is an end-to-end neural network model without the need for pre- or post-processing on input samples. Results: The leave-one-allele-out cross validation and benchmark evaluation results show that our proposed network model achieved state-of-the-art performance in HLA-II peptide binding. Besides state-of-the-art performance in binding affinity prediction, DeepSeqPanII can also extract biological insight on the binding mechanism over the peptide and HLA sequences by its attention mechanism based binding core prediction capability. Conclusions: In this work, we present a novel neural network structure for peptide-HLA class II binding prediction. It has state-of-the-art performance and could display insightful information it learned benefiting from attention module we carefully designed. Without requiring additional data, this structure could be applied to other related sequence problems. The source code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPanII.

Sequence Analysis of Peptides Presented to the Immune System by Class I and Class II MHC Molecules

1993 ◽  
pp. 127-133 ◽  
Author(s):  
Donald F. Hunt ◽  
Jeffrey Shabanowitz ◽  
Hanspeter Michel ◽  
Andrea L. Cox ◽  
Tracey Dickinson ◽  
...  
Keyword(s):  
Immune System ◽  
Sequence Analysis ◽  
Class Ii ◽  
Class I ◽  
Mhc Molecules ◽  
Class Ii Mhc

scholarly journals DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction

2019 ◽  
Author(s):  
Zhonghao Liu ◽  
Jing Jin ◽  
Yuxin Cui ◽  
Zheng Xiong ◽  
Alireza Nasiri ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Models ◽  
State Of The Art ◽  
Human Leukocyte ◽  
Class Ii ◽  
Hla Class Ii ◽  
Attention Mechanism ◽  
Binding Prediction

AbstractHuman leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant progress in deep learning, a series of neural network based models have been proposed and demonstrated with their good performances for peptide-HLA class I binding prediction. However, there still lack effective binding prediction models for HLA class II protein binding with peptides due to its inherent challenges. In this work, we present a novel sequence-based pan-specific neural network structure, DeepSeaPanII, for peptide-HLA class II binding prediction. Compared with existing pan-specific models, our model is an end-to-end neural network model without the need for pre- or post-processing on input samples. Besides state-of-the-art peformance in binding affinity prediction, DeepSeqPanII can also extract biological insight on the binding mechanism over the peptide and HLA sequences by its attention mechanism based binding core prediction capability. The leave-one-allele-out cross validation and benchmark evaluation results show that our proposed network model achieved state-of-the-art performance in HLA-II peptide binding. The source code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPanII.

scholarly journals Ranking-Based Convolutional Neural Network Models for Peptide-MHC Class I Binding Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Ziqi Chen ◽  
Martin Renqiang Min ◽  
Xia Ning
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Mhc Class I ◽  
State Of The Art ◽  
The State ◽  
Class I ◽  
Specific Method ◽  
Binding Affinities ◽  
Binding Prediction ◽  
Allele Specific

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network-based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope Database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles.

Electron microscopy analysis of degenerating pancreatic ß cells in transgenic mice expressing the MHC Class I antigen Dd

1990 ◽  
Vol 48 (3) ◽  
pp. 548-549
Author(s):  
T. A. Stewart ◽  
D. Liggitt ◽  
S. Pitts ◽  
L. Martin ◽  
M. Siegel ◽  
...  
Keyword(s):  
Type I Diabetes ◽  
Disease Process ◽  
Class Ii ◽  
Class I ◽  
Type I ◽  
Aberrant Expression ◽  
Mhc Molecules ◽  
Endogenous Insulin ◽  
Class Ii Mhc ◽  
Ifn Γ

Insulin-dependant (Type I) diabetes mellitus (IDDM) is a metabolic disorder resulting from the lack of endogenous insulin secretion. The disease is thought to result from the autoimmune mediated destruction of the insulin producing ß cells within the islets of Langerhans. The disease process is probably triggered by environmental agents, e.g. virus or chemical toxins on a background of genetic susceptibility associated with particular alleles within the major histocompatiblity complex (MHC). The relation between IDDM and the MHC locus has been reinforced by the demonstration of both class I and class II MHC proteins on the surface of ß cells from newly diagnosed patients as well as mounting evidence that IDDM has an autoimmune pathogenesis. In 1984, a series of observations were used to advance a hypothesis, in which it was suggested that aberrant expression of class II MHC molecules, perhaps induced by gamma-interferon (IFN γ) could present self antigens and initiate an autoimmune disease. We have tested some aspects of this model and demonstrated that expression of IFN γ by pancreatic ß cells can initiate an inflammatory destruction of both the islets and pancreas and does lead to IDDM.

MHC Sınıf I ve MHC Sınıf II Gen Düzenlenmesi

2020 ◽  
Vol 8 (3) ◽  
pp. 144-156
Author(s):  
Şule KARATAŞ ◽  
Fatma SAVRAN OĞUZ
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Gene Regulation ◽  
Mhc Class I ◽  
Mhc Class Ii ◽  
Class Ii ◽  
Class I ◽  
Mhc Molecules ◽  
Class Ii Mhc ◽  
Regulation Mechanisms

Introduction: Peptides obtained by processing intracellular and extracellular antigens are presented to T cells to stimulate the immune response. This presentation is made by peptide receptors called major histocompatibility complex (MHC) molecules. The regulation mechanisms of MHC molecules, which have similar roles in the immune response, especially at the gene level, have significant differences according to their class. Objective: Class I and class II MHC molecules encoded by MHC genes on the short arm of the sixth chromosome are peptide receptors that stimulate T cell response. These peptides, which will enable the recognition of the antigen from which they originate, are loaded into MHC molecules and presented to T cells. Although the principles of loading and delivering peptides are similar for both molecules, the peptide sources and peptide loading mechanisms are different. In addition, class I molecules are expressed in all nucleated cells while class II molecules are expressed only in Antigen Presentation Cells (APC). These differences; It shows that MHC class I is not expressed by exactly the same transcriptional mechanisms as MHC class II. In our article, we aimed to compare the gene expressions of both classes and reveal their similarities and differences. Discussion and Conclusion: A better understanding of the transcriptional mechanisms of MHC molecules will reveal the role of these molecules in diseases more clearly. In our review, we discussed MHC gene regulation mechanisms with presence of existing informations, which is specific to the MHC class, for contribute to future research. Keywords: MHC class I, MHC class II, MHC gene regulation, promoter, SXY module, transcription

scholarly journals Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 572
Author(s):  
Alan M. Luu ◽  
Jacob R. Leistico ◽  
Tim Miller ◽  
Somang Kim ◽  
Jun S. Song
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Cytotoxic T Cells ◽  
Metric Learning ◽  
Binding Specificity ◽  
Class I ◽  
Multimodal Learning ◽  
Binding Prediction ◽  
Deep Metric Learning ◽  
Epitope Binding

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

Phosphorylation of class I but not class II MHC molecules by membrane-localized protein kinase C

1989 ◽  
Vol 26 (12) ◽  
pp. 1095-1104 ◽  
Author(s):  
Teresa Burke ◽  
Karen Pollok ◽  
William Cushley ◽  
E. Charles Snow
Keyword(s):  
Protein Kinase C ◽  
Protein Kinase ◽  
Class Ii ◽  
Class I ◽  
Mhc Molecules ◽  
Kinase C ◽  
Class Ii Mhc

SHEDR: An End-to-End Deep Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media

2021 ◽  
Author(s):  
Yuheng Hu ◽  
Yili Hong
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Event Detection ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
Neural Network Models ◽  
Neural Event ◽  
End To End

Residents often rely on newspapers and television to gather hyperlocal news for community awareness and engagement. More recently, social media have emerged as an increasingly important source of hyperlocal news. Thus far, the literature on using social media to create desirable societal benefits, such as civic awareness and engagement, is still in its infancy. One key challenge in this research stream is to timely and accurately distill information from noisy social media data streams to community members. In this work, we develop SHEDR (social media–based hyperlocal event detection and recommendation), an end-to-end neural event detection and recommendation framework with a particular use case for Twitter to facilitate residents’ information seeking of hyperlocal events. The key model innovation in SHEDR lies in the design of the hyperlocal event detector and the event recommender. First, we harness the power of two popular deep neural network models, the convolutional neural network (CNN) and long short-term memory (LSTM), in a novel joint CNN-LSTM model to characterize spatiotemporal dependencies for capturing unusualness in a region of interest, which is classified as a hyperlocal event. Next, we develop a neural pairwise ranking algorithm for recommending detected hyperlocal events to residents based on their interests. To alleviate the sparsity issue and improve personalization, our algorithm incorporates several types of contextual information covering topic, social, and geographical proximities. We perform comprehensive evaluations based on two large-scale data sets comprising geotagged tweets covering Seattle and Chicago. We demonstrate the effectiveness of our framework in comparison with several state-of-the-art approaches. We show that our hyperlocal event detection and recommendation models consistently and significantly outperform other approaches in terms of precision, recall, and F-1 scores. Summary of Contribution: In this paper, we focus on a novel and important, yet largely underexplored application of computing—how to improve civic engagement in local neighborhoods via local news sharing and consumption based on social media feeds. To address this question, we propose two new computational and data-driven methods: (1) a deep learning–based hyperlocal event detection algorithm that scans spatially and temporally to detect hyperlocal events from geotagged Twitter feeds; and (2) A personalized deep learning–based hyperlocal event recommender system that systematically integrates several contextual cues such as topical, geographical, and social proximity to recommend the detected hyperlocal events to potential users. We conduct a series of experiments to examine our proposed models. The outcomes demonstrate that our algorithms are significantly better than the state-of-the-art models and can provide users with more relevant information about the local neighborhoods that they live in, which in turn may boost their community engagement.

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