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 ◽  
...  

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.

2019 ◽  
Vol 20 (3) ◽  
pp. 170-176 ◽  
Author(s):  
Zhongyan Li ◽  
Qingqing Miao ◽  
Fugang Yan ◽  
Yang Meng ◽  
Peng Zhou

Background:Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.Methods:We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.Results:Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.Conclusion:There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.


Blood ◽  
1999 ◽  
Vol 93 (3) ◽  
pp. 936-941 ◽  
Author(s):  
Magdalena Magierowska ◽  
Ioannis Theodorou ◽  
Patrice Debré ◽  
Françoise Sanson ◽  
Brigitte Autran ◽  
...  

Abstract Human immunodeficiency virus (HIV)-1–infected long-term nonprogressors (LT-NP) represent less than 5% of HIV-1–infected patients. In this work, we tried to understand whether combined genotypes of CCR5-▵32, CCR2-64I, SDF1-3′A and HLA alleles can predict the LT-NP status. Among the chemokine receptor genotypes, only the frequency of the CCR5-▵32 allele was significantly higher in LT-NP compared with the group of standard progressors. The predominant HLA alleles in LT-NP were HLA-A3, HLA-B14, HLA-B17, HLA-B27, HLA-DR6, and HLA-DR7. A combination of both HLA and chemokine receptor genotypes integrated in a multivariate logistic regression model showed that if a subject is heterozygous for CCR5-▵32 and homozygous for SDF1 wild type, his odds of being LT-NP are increased by 16-fold, by 47-fold when a HLA-B27 allele is present with HLA-DR6 absent, and by 47-fold also if at least three of the following alleles are present: HLA-A3, HLA-B14, HLA-B17, HLA-DR7. This model allowed a correct classification of 70% of LT-NPs and 81% of progressors, suggesting that the host’s genetic background plays an important role in the evolution of HIV-1. The chemokine receptor and chemokine genes along with the HLA genotype can serve as predictors of HIV-1 outcome for classification of HIV-1–infected subjects as LT-NPs or progressors.


2021 ◽  
Author(s):  
Ronghui You ◽  
Wei Qu ◽  
Hiroshi Mamitsuka ◽  
Shanfeng Zhu

Computationally predicting MHC-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring the biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with binding interaction convolution layer (BICL), which allows integrating all potential binding cores (in a given peptide) and the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels. Extensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as five-fold cross-validation, leave one molecule out, validation with independent testing sets, and binding core prediction. All these results with visualization of the predicted binding cores indicate the effectiveness and importance of properly modeling biological facts in deep learning for high performance and knowledge discovery. DeepMHCII is publicly available at https://weilab.sjtu.edu.cn/DeepMHCII/.


2019 ◽  
Author(s):  
Kengo Hirao ◽  
Sophie Andrews ◽  
Kimiko Kuroki ◽  
Hiroki Kusaka ◽  
Takashi Tadokoro ◽  
...  

SummaryThe HIV accessory protein Nef plays a major role in establishing and maintaining infection, particularly through immune evasion. Many HIV-2 infected people experience long-term viral control and survival, resembling HIV-1 elite control. HIV-2 Nef has overlapping but also distinct functions from HIV-1 Nef. Here we report the crystal structure of HIV-2 Nef core. The dileucine sorting motif forms a helix bound to neighboring molecules, and moreover, isothermal titration calorimetry demonstrated that the CD3 endocytosis motif can directly bind to HIV-2 Nef, ensuring AP-2 mediated endocytosis for CD3. The highly-conserved C-terminal region forms a α-helix, absent from HIV-1. We further determined the structure of SIV Nef harboring this region, demonstrating similar C-terminal α-helix, which may contribute to AP-1 binding for MHC-I downregulation. These results provide new insights into the distinct pathogenesis of HIV-2 infection.


Immunology ◽  
2018 ◽  
Vol 154 (3) ◽  
pp. 394-406 ◽  
Author(s):  
Kamilla Kjaergaard Jensen ◽  
Massimo Andreatta ◽  
Paolo Marcatili ◽  
Søren Buus ◽  
Jason A. Greenbaum ◽  
...  

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0220459
Author(s):  
Eva Ramírez de Arellano ◽  
Francisco Díez-Fuertes ◽  
Francisco Aguilar ◽  
Humberto Erick de la Torre Tarazona ◽  
Susana Sánchez-Lara ◽  
...  
Keyword(s):  

1994 ◽  
Vol 3 (8) ◽  
pp. 1261-1266 ◽  
Author(s):  
Wendell A. Lim ◽  
Robert O. Fox ◽  
Frederic M. Richards

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