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

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.

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Predicting TCR-epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

10.1101/2021.03.19.436191 ◽

2021 ◽

Author(s):

Alan 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 CDR3B 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 the 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 insight into the learned predictive features of TCR-epitope binding specificity and advances associated classification tasks.

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

10.1101/318881 ◽

2018 ◽

Cited By ~ 5

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]

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Feature Learning Approach for Facial Recognition Using Deep Metric Learning

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9031 ◽

2020 ◽

Vol 17 (9) ◽

pp. 4125-4130

Author(s):

Gaurav Karkal ◽

K. Dhanush Reddy ◽

Kaushik Singh ◽

Nikith Hosangadi ◽

Annapurna P. Patil

Keyword(s):

Neural Network ◽

Facial Recognition ◽

Metric Learning ◽

Feature Learning ◽

Human Interaction ◽

Single Image ◽

Whole Process ◽

Knn Classifier ◽

Deep Metric Learning ◽

Trained Neural Network

Standard deep learning in the context of facial recognition involves inputting a single image and outputting a label for that image. Deep metric learning distinguishes itself by outputting a real valued feature vector instead of a single label. The usage of deep metric learning has revolutionised facial recognition, making it very accurate and reliable. This paper exhibits the accuracy and reliability of the facial recognition model using deep metric learning in the application of an automated attendance system. The paper presents a non-intrusive attendance system which uses the described neural network to recognize faces and record attendance. The system uses the pre-trained neural network to generate embeddings for faces, using a method known as the triple training step, which is described in the paper. These embeddings are generated from a collection of photos per person. After the embeddings are generated, the system is ready to perform facial recognition on sample photos. CNN is used for facial detection in the sample group photos. Once the faces are detected, a KNN classifier is used for recognizing faces. Finally after the faces are recognized, the attendance for each recognized student is marked in the database. Thus, the whole process of attendance was automated without the requirement of human interaction.

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Quantitative Prediction of Class I MHC/Epitope Binding Affinity Using QSAR Modeling Derived from Amino Acid Structural Information

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207318666150121125746 ◽

2015 ◽

Vol 18 (1) ◽

pp. 75-82 ◽

Cited By ~ 9

Author(s):

Yuanqiang Wang ◽

Pengpeng Zhou ◽

Yong Lin ◽

Mao Shu ◽

Yong Hu ◽

...

Keyword(s):

Amino Acid ◽

Binding Affinity ◽

Structural Information ◽

Class I ◽

Quantitative Prediction ◽

Qsar Modeling ◽

Class I Mhc ◽

Epitope Binding

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Skin Disease Classification Technique Based on Convolutional Neural Network Using Deep Metric Learning

Korean Institute of Smart Media ◽

10.30693/smj.2021.10.4.45 ◽

2021 ◽

Vol 10 (4) ◽

pp. 45-53

Author(s):

Kang Min Kim ◽

Pan-Koo Kim ◽

Chanjun Chun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Skin Disease ◽

Metric Learning ◽

Disease Classification ◽

Classification Technique ◽

Deep Metric Learning

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Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach

Applied Sciences ◽

10.3390/app11156959 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6959

Author(s):

Zaky Dzulfikri ◽

Pin-Wei Su ◽

Chih-Yung Huang

Keyword(s):

Neural Network ◽

Metric Learning ◽

Training Data ◽

Probability Method ◽

Learning Approach ◽

Test Results ◽

Complex Signals ◽

Intelligent Monitoring ◽

Deep Metric Learning

Stamping processes remain crucial in manufacturing processes; therefore, diagnosing the condition of stamping tools is critical. One of the challenges in diagnosing stamping tool conditions is that traditionally, the tools need to be visually checked, and the production processes thus need to be halted. With the development of Industry 4.0, intelligent monitoring systems have been developed by using accelerometers and algorithms to diagnose the wear classification of stamping tools. Although several deep learning models such as the convolutional neural network (CNN), auto encoder (AE), and recurrent neural network (RNN) models have demonstrated promising results for classifying complex signals including accelerometer signals, the practicality of those methods are restricted due to the flexibility of adding new classes and low accuracy when faced to low numbers of samples per class. In this study, we applied deep metric learning (DML) methods to overcome these problems. DML involves extracting meaningful features using feature extraction modules to map inputs into embedding features. We compared the probability method, the contrastive method, and a triplet network to determine which method was most suitable for our case. The experimental results revealed that, compared with other models, a triplet network can be more effectively trained with limited training data. The triplet network demonstrated the best test results of the compared methods in the noised test data. Finally, when tested using unseen class, the triplet network and the probability method demonstrated similar results.

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An effective crack position diagnosis method for the hollow shaft rotor system based on the convolutional neural network and deep metric learning

Chinese Journal of Aeronautics ◽

10.1016/j.cja.2021.09.010 ◽

2021 ◽

Author(s):

Yuhong Jin ◽

Lei Hou ◽

Yushu Chen ◽

Zhenyong Lu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Metric Learning ◽

Rotor System ◽

Hollow Shaft ◽

Diagnosis Method ◽

Deep Metric Learning ◽

Crack Position

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An Enhanced Neural Network Based on Deep Metric Learning for Skin Lesion Segmentation

2019 Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2019.8832646 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xinhua Liu ◽

Gaoqiang Hu ◽

Xiaolin Ma ◽

Hailan Kuang

Keyword(s):

Neural Network ◽

Skin Lesion ◽

Metric Learning ◽

Lesion Segmentation ◽

Deep Metric Learning

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Rare bioparticle detection via deep metric learning

RSC Advances ◽

10.1039/d1ra02869c ◽

2021 ◽

Vol 11 (29) ◽

pp. 17603-17610

Author(s):

Shaobo Luo ◽

Yuzhi Shi ◽

Lip Ket Chin ◽

Yi Zhang ◽

Bihan Wen ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Networks ◽

Metric Learning ◽

Practical Applications ◽

Deep Metric Learning

Conventional deep neural networks use simple classifiers to obtain highly accurate results. However, they have limitations in practical applications. This study demonstrates a robust deep metric neural network model for rare bioparticle detection.

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