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Kongkaew S, Rungrotmongkol T, Punwong C, Noguchi H, Takeuchi F, Kungwan N, Wolschann P, Hannongbua S. Interactions of HLA-DR and Topoisomerase I Epitope Modulated Genetic Risk for Systemic Sclerosis. Sci Rep 2019; 9:745. [PMID: 30679605 PMCID: PMC6345791 DOI: 10.1038/s41598-018-37038-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 11/29/2018] [Indexed: 12/11/2022] Open
Abstract
The association of systemic sclerosis with anti-Topoisomerase 1 antibody (ATASSc) with specific alleles of human leukocyte antigen (HLA)-DR has been observed among various ethnics. The anti-Topoisomerase 1 antibody is a common autoantibody in SSc with diffuse cutaneous scleroderma, which is one of the clinical subtypes of SSc. On the other hand, an immunodominant peptide of topoisomerase 1 (Top1) self-protein (residues 349-368) was reported to have strong association with ATASSc. In this study, molecular dynamics simulation was performed on the complexes of Top1 peptide with various HLA-DR subtypes divided into ATASSc-associated alleles (HLA-DRB1*08:02, HLA-DRB1*11:01 and HLA-DRB1*11:04), suspected allele (HLA-DRB5*01:02), and non-associated allele (HLA-DRB1*01:01). The unique interaction for each system was compared to the others in terms of dynamical behaviors, binding free energies and solvation effects. Our results showed that three HLA-DR/Top1 complexes of ATASSc association mostly exhibited high protein stability and increased binding efficiency without solvent interruption, in contrast to non-association. The suspected case (HLA-DRB5*01:02) binds Top1 as strongly as the ATASSc association case, which implied a highly possible risk for ATASSc development. This finding might support ATASSc development mechanism leading to a guideline for the treatment and avoidance of pathogens like Top1 self-peptide risk for ATASSc.
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Affiliation(s)
- Sirilak Kongkaew
- Program in Biotechnology, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.,The Center of Excellence in Computational Chemistry, Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Thanyada Rungrotmongkol
- Biocatalyst and Environmental Biotechnology Research unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand. .,Ph.D. Program in Bioinformatics and Computational Biology, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
| | - Chutintorn Punwong
- Department of Physics, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand
| | - Hiroshi Noguchi
- School of Pharmacy, Nihon Pharmaceutical University, Saitama, 361-0806, Japan.,School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, 422-8526, Japan
| | - Fujio Takeuchi
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, 422-8526, Japan.,Faculty of Health and Nutrition, Tokyo Seiei University, Tokyo, 124-8530, Japan
| | - Nawee Kungwan
- Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand.,Center of Excellence in Materials Science and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Peter Wolschann
- The Center of Excellence in Computational Chemistry, Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.,Department of Pharmaceutical Chemistry, University of Vienna, Vienna, 1090, Austria.,Institute of Theoretical Chemistry, University of Vienna, Vienna, 1090, Austria
| | - Supot Hannongbua
- The Center of Excellence in Computational Chemistry, Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
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Degoot AM, Chirove F, Ndifon W. Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions. Front Immunol 2018; 9:1410. [PMID: 29988560 PMCID: PMC6026802 DOI: 10.3389/fimmu.2018.01410] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 06/06/2018] [Indexed: 12/30/2022] Open
Abstract
Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, CD4+ T cells mount an immune response against invading pathogens. Therefore, mechanistic identification and knowledge of physicochemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this article, we present a comprehensive trans-allelic prediction model, a generalized version of our previous biophysical model, that can predict peptide interactions for all three human MHC-II loci (HLA-DR, HLA-DP, and HLA-DQ), using both peptide sequence data and structural information of MHC-II molecules. The advantage of this approach over other machine learning models is that it offers a simple and plausible physical explanation for peptide–MHC-II interactions. We train the model using a benchmark experimental dataset and measure its predictive performance using novel data. Despite its relative simplicity, we find that the model has comparable performance to the state-of-the-art method, the NetMHCIIpan method. Focusing on the physical basis of peptide–MHC binding, we find support for previous theoretical predictions about the contributions of certain binding pockets to the binding energy. In addition, we find that binding pocket P5 of HLA-DP, which was not previously considered as a primary anchor, does make strong contribution to the binding energy. Together, the results indicate that our model can serve as a useful complement to alternative approaches to predicting peptide–MHC interactions.
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Affiliation(s)
- Abdoelnaser M Degoot
- African Institute of Mathematical Sciences (AIMS), Muizenberg, South Africa.,School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.,DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), Gauteng, South Africa
| | - Faraimunashe Chirove
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Wilfred Ndifon
- African Institute of Mathematical Sciences (AIMS), Muizenberg, South Africa
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Aray Y, Aguilera-García R, Izquierdo DR. Exploring the nature of the H-bonds between the human class II MHC protein, HLA-DR1 (DRB*0101) and the influenza virus hemagglutinin peptide, HA306-318, using the quantum theory of atoms in molecules. J Biomol Struct Dyn 2017; 37:48-64. [PMID: 29246090 DOI: 10.1080/07391102.2017.1418432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The nature of the H-bonds between the human protein HLA-DR1 (DRB*0101) and the hemagglutinin peptide HA306-318 has been studied using the Quantum Theory of Atoms in Molecules for the first time. We have found four H-bond groups: one conventional CO··HN bond group and three nonconventional CO··HC, π··HC involving aromatic rings and HN··HCaliphatic groups. The calculated electron density at the determined H-bond critical points suggests the follow protein pocket binding trend: P1 (2,311) >> P9 (1.109) > P4 (0.950) > P6 (0.553) > P7 (0.213) which agrees and reveal the nature of experimental findings, showing that P1 produces by a long way the strongest binding of the HLA-DR1 human protein molecule with the peptide backbone as consequence of the vast number of H-bonds in the P1 area and at the same time the largest specific binding of the peptide Tyr308 residue with aromatic residues located at the binding groove floor. The present results suggest the topological analysis of the electronic density as a valuable tool that allows a non-arbitrary partition of the pockets binding energy via the calculated electron density at the determined critical points.
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Affiliation(s)
- Yosslen Aray
- a Facultad de Ciencias , Universidad de Ciencias Aplicadas y Ambientales, UDCA , Bogotá , Colombia
| | - Ricardo Aguilera-García
- a Facultad de Ciencias , Universidad de Ciencias Aplicadas y Ambientales, UDCA , Bogotá , Colombia
| | - Daniel R Izquierdo
- a Facultad de Ciencias , Universidad de Ciencias Aplicadas y Ambientales, UDCA , Bogotá , Colombia
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Ding Y, Tang J, Guo F. Identification of Protein-Ligand Binding Sites by Sequence Information and Ensemble Classifier. J Chem Inf Model 2017; 57:3149-3161. [PMID: 29125297 DOI: 10.1021/acs.jcim.7b00307] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Identifying protein-ligand binding sites is an important process in drug discovery and structure-based drug design. Detecting protein-ligand binding sites is expensive and time-consuming by traditional experimental methods. Hence, computational approaches provide many effective strategies to deal with this issue. Recently, lots of computational methods are based on structure information on proteins. However, these methods are limited in the common scenario, where both the sequence of protein target is known and sufficient 3D structure information is available. Studies indicate that sequence-based computational approaches for predicting protein-ligand binding sites are more practical. In this paper, we employ a novel computational model of protein-ligand binding sites prediction, using protein sequence. We apply the Discrete Cosine Transform (DCT) to extract feature from Position-Specific Score Matrix (PSSM). In order to improve the accuracy, Predicted Relative Solvent Accessibility (PRSA) information is also utilized. The predictor of protein-ligand binding sites is built by employing the ensemble weighted sparse representation model with random under-sampling. To evaluate our method, we conduct several comprehensive tests (12 types of ligands testing sets) for predicting protein-ligand binding sites. Results show that our method achieves better Matthew's correlation coefficient (MCC) than other outstanding methods on independent test sets of ATP (0.506), ADP (0.511), AMP (0.393), GDP (0.579), GTP (0.641), Mg2+ (0.317), Fe3+ (0.490) and HEME (0.640). Our proposed method outperforms earlier predictors (the performance of MCC) in 8 of the 12 ligands types.
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Affiliation(s)
- Yijie Ding
- School of Computer Science and Technology, Tianjin University , No. 135, Yaguan Road, Tianjin Haihe Education Park, Tianjin 300350, China
| | - Jijun Tang
- School of Computer Science and Technology, Tianjin University , No. 135, Yaguan Road, Tianjin Haihe Education Park, Tianjin 300350, China.,Department of Computer Science and Engineering, University of South Carolina , Columbia, South Carolina 29208, United States
| | - Fei Guo
- School of Computer Science and Technology, Tianjin University , No. 135, Yaguan Road, Tianjin Haihe Education Park, Tianjin 300350, China
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