scholarly journals Connecting MHC-I-binding motifs with HLA alleles via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ko-Han Lee ◽  
Yu-Chuan Chang ◽  
Ting-Fu Chen ◽  
Hsueh-Fen Juan ◽  
Huai-Kuang Tsai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mhc I ◽  
Binding Motifs ◽  
Hla Alleles ◽  
Antigen Discovery ◽  
Mhc Molecules ◽  
Binding Preference ◽  
Allele Specificity ◽  
Binding Peptides ◽  
Selection Of

AbstractThe selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned.

scholarly journals Connecting MHC-I-binding motifs with HLA alleles via deep learning

2021 ◽  
Author(s):  
Ko-Han Lee ◽  
Yu-Chuan Chang ◽  
Ting-Fu Chen ◽  
Hsueh-Fen Juan ◽  
Huai-Kuang Tsai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mhc I ◽  
Binding Motifs ◽  
Hla Alleles ◽  
Antigen Discovery ◽  
Mhc Molecules ◽  
Binding Preference ◽  
Allele Specificity ◽  
Binding Peptides ◽  
Selection Of

The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned.

scholarly journals Positional SHAP for Interpretation of Deep Learning Models Trained from Biological Sequences

2021 ◽  
Author(s):  
Quinn Dickinson ◽  
Jesse G. Meyer
Keyword(s):  
Deep Learning ◽  
Rhesus Macaque ◽  
Short Term Memory ◽  
Peptide Binding ◽  
Disease Diagnosis ◽  
Biological Sequences ◽  
Mhc I ◽  
Binding Motifs ◽  
Model Interpretation ◽  
Biological Phenomena

AbstractMachine learning with artificial neural networks, also known as “deep learning”, accurately predicts biological phenomena such as disease diagnosis and protein structure. Despite the ability of deep learning to make accurate biological predictions, a challenge is model interpretation, which is especially challenging for recurrent neural network architectures due to the sequential input data. Here we train multi-output long short-term memory (LSTM) regression models to predict peptide binding affinity to five rhesus macaque major histocompatibility complex (MHC) I alleles. We adapt SHapely Additive exPlanations (SHAP) to generate positional model interpretations of which amino acids are important for peptide binding. These positional SHAP values reproduced known rhesus macaque MHC class I (Mamu-A1*001) peptide binding motifs and provided insights into inter-positional dependencies of peptide-MHC interactions. Positional SHAP should find widespread utility for interpreting a variety of models trained from biological sequences.

scholarly journals Classical MHC expression by DP thymocytes impairs the selection of non-classical MHC restricted innate-like T cells

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hristo Georgiev ◽  
Changwei Peng ◽  
Matthew A. Huggins ◽  
Stephen C. Jameson ◽  
Kristin A. Hogquist
Keyword(s):  
T Cells ◽  
Epithelial Cells ◽  
Cell Types ◽  
Inkt Cells ◽  
Mhc I ◽  
Mait Cells ◽  
Mhc Molecules ◽  
Mhc Ii ◽  
Different Cell Types ◽  
Selection Of

AbstractConventional T cells are selected by peptide-MHC expressed by cortical epithelial cells in the thymus, and not by cortical thymocytes themselves that do not express MHC I or MHC II. Instead, cortical thymocytes express non-peptide presenting MHC molecules like CD1d and MR1, and promote the selection of PLZF+ iNKT and MAIT cells, respectively. Here, we report an inducible class-I transactivator mouse that enables the expression of peptide presenting MHC I molecules in different cell types. We show that MHC I expression in DP thymocytes leads to expansion of peptide specific PLZF+ innate-like (PIL) T cells. Akin to iNKT cells, PIL T cells differentiate into three functional effector subsets in the thymus, and are dependent on SAP signaling. We demonstrate that PIL and NKT cells compete for a narrow niche, suggesting that the absence of peptide-MHC on DP thymocytes facilitates selection of non-peptide specific lymphocytes.

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

Physical elution in phage display selection of inorganic-binding peptides

2009 ◽  
Vol 29 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Senem Donatan ◽  
Hilal Yazici ◽  
Hakan Bermek ◽  
Mehmet Sarikaya ◽  
Candan Tamerler ◽  
...  
Keyword(s):  
Phage Display ◽  
Binding Peptides ◽  
Selection Of

scholarly journals Effects of Mutations on Replicative Fitness and Major Histocompatibility Complex Class I Binding Affinity Are Among the Determinants Underlying Cytotoxic-T-Lymphocyte Escape of HIV-1 Gag Epitopes

mBio ◽  
2017 ◽  
Vol 8 (6) ◽  
Author(s):  
Yushen Du ◽  
Tian-Hao Zhang ◽  
Lei Dai ◽  
Xiaojuan Zheng ◽  
Aleksandr M. Gorin ◽  
...  
Keyword(s):  
Binding Affinity ◽  
High Throughput ◽  
Peptide Binding ◽  
Virus Evolution ◽  
Mhc I ◽  
Hla Alleles ◽  
Ctl Escape ◽  
Peptide Binding Affinity ◽  
Hiv 1

ABSTRACT Certain “protective” major histocompatibility complex class I (MHC-I) alleles, such as B*57 and B*27, are associated with long-term control of HIV-1 in vivo mediated by the CD8+ cytotoxic-T-lymphocyte (CTL) response. However, the mechanism of such superior protection is not fully understood. Here we combined high-throughput fitness profiling of mutations in HIV-1 Gag, in silico prediction of MHC-peptide binding affinity, and analysis of intraperson virus evolution to systematically compare differences with respect to CTL escape mutations between epitopes targeted by protective MHC-I alleles and those targeted by nonprotective MHC-I alleles. We observed that the effects of mutations on both viral replication and MHC-I binding affinity are among the determinants of CTL escape. Mutations in Gag epitopes presented by protective MHC-I alleles are associated with significantly higher fitness cost and lower reductions in binding affinity with respect to MHC-I. A linear regression model accounting for the effect of mutations on both viral replicative capacity and MHC-I binding can explain the protective efficacy of MHC-I alleles. Finally, we found a consistent pattern in the evolution of Gag epitopes in long-term nonprogressors versus progressors. Overall, our results suggest that certain protective MHC-I alleles allow superior control of HIV-1 by targeting epitopes where mutations typically incur high fitness costs and small reductions in MHC-I binding affinity. IMPORTANCE Understanding the mechanism of viral control achieved in long-term nonprogressors with protective HLA alleles provides insights for developing functional cure of HIV infection. Through the characterization of CTL escape mutations in infected persons, previous researchers hypothesized that protective alleles target epitopes where escape mutations significantly reduce viral replicative capacity. However, these studies were usually limited to a few mutations observed in vivo. Here we utilized our recently developed high-throughput fitness profiling method to quantitatively measure the fitness of mutations across the entirety of HIV-1 Gag. The data enabled us to integrate the results with in silico prediction of MHC-peptide binding affinity and analysis of intraperson virus evolution to systematically determine the differences in CTL escape mutations between epitopes targeted by protective HLA alleles and those targeted by nonprotective HLA alleles. We observed that the effects of Gag epitope mutations on HIV replicative fitness and MHC-I binding affinity are among the major determinants of CTL escape. IMPORTANCE Understanding the mechanism of viral control achieved in long-term nonprogressors with protective HLA alleles provides insights for developing functional cure of HIV infection. Through the characterization of CTL escape mutations in infected persons, previous researchers hypothesized that protective alleles target epitopes where escape mutations significantly reduce viral replicative capacity. However, these studies were usually limited to a few mutations observed in vivo. Here we utilized our recently developed high-throughput fitness profiling method to quantitatively measure the fitness of mutations across the entirety of HIV-1 Gag. The data enabled us to integrate the results with in silico prediction of MHC-peptide binding affinity and analysis of intraperson virus evolution to systematically determine the differences in CTL escape mutations between epitopes targeted by protective HLA alleles and those targeted by nonprotective HLA alleles. We observed that the effects of Gag epitope mutations on HIV replicative fitness and MHC-I binding affinity are among the major determinants of CTL escape.

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