Browsing by Author "Liu, I-Hsin"

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Evolutionary conservation and interacting preference for identifying Protein-DNA interactions
(2008)
Chang, Yao-Lin
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Liu, I-Hsin
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Kao, Cheng-Yan
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Yang, Jinn-Moon
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生物資訊及系統生物研究所
Protein-DNA interactions play a central role in many genetic processes of cells. With the growing crystal structures of protein-DNA complexes, the computational approaches are becoming more and more useful for modeling protein-DNA interactions. This paper proposes template-based alignment with a new scoring function which combined the evolutionary conservation and protein-DNA interacting scores of DNA-contact residues. We showed that the combined scoring function is better to model the protein-DNA interactions than applying only one. Our method achieved high accuracy in identifying DNA-binding domains of 69 representative families and with the correlation 0.6 in predicting the binding free energy of the alanine scanning data. By applying the method to the hormone receptor family, it showed that our method can identify the DNA-binding specificity in different subfamilies. The evolutionary conservation is able to reflect the evolution pressure of DNA-contact residues and the interaction preferences can indicate the binding affinity between the protein and DNA. Experimental results show that both the evolution conservation and the DNA-binding capability of the DNA-contact residues are essential for identifying DNA-binding domains and protein-DNA interactions.
Genome-wide structural modelling of TCR-pMHC interactions
(2013-10-16)
Liu, I-Hsin
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Lo, Yu-Shu
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Yang, Jinn-Moon
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生物科技學系
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生物資訊及系統生物研究所
Background: The adaptive immune response is antigen-specific and triggered by pathogen recognition through T cells. Although the interactions and mechanisms of TCR-peptide-MHC (TCR-pMHC) have been studied over three decades, the biological basis for these processes remains controversial. As an increasing number of high-throughput binding epitopes and available TCR-pMHC complex structures, a fast genome-wide structural modelling of TCR-pMHC interactions is an emergent task for understanding immune interactions and developing peptide vaccines. Results: We first constructed the PPI matrices and iMatrix, using 621 non-redundant PPI interfaces and 398 non-redundant antigen-antibody interfaces, respectively, for modelling the MHC-peptide and TCR-peptide interfaces, respectively. The iMatrix consists of four knowledge-based scoring matrices to evaluate the hydrogen bonds and van der Waals forces between sidechains or backbones, respectively. The predicted energies of iMatrix are high correlated (Pearson's correlation coefficient is 0.6) to 70 experimental free energies on antigen-antibody interfaces. To further investigate iMatrix and PPI matrices, we inferred the 701,897 potential peptide antigens with significant statistic from 389 pathogen genomes and modelled the TCR-pMHC interactions using available TCR-pMHC complex structures. These identified peptide antigens keep hydrogen-bond energies and consensus interactions and our TCR-pMHC models can provide detailed interacting models and crucial binding regions. Conclusions: Experimental results demonstrate that our method can achieve high precision for predicting binding affinity and potential peptide antigens. We believe that iMatrix and our template-based method can be useful for the binding mechanisms of TCR-pMHC complexes and peptide vaccine designs.
PAComplex: a web server to infer peptide antigen families and binding models from TCR-pMHC complexes
(2011-07-01)
Liu, I-Hsin
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Lo, Yu-Shu
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Yang, Jinn-Moon
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生物科技學系
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生物資訊及系統生物研究所
One of the most adaptive immune responses is triggered by specific T-cell receptors (TCR) binding to peptide-major histocompatibility complexes (pMHC). Despite the availability of many prediction servers to identify peptides binding to MHC, these servers are often lacking in peptide-TCR interactions and detailed atomic interacting models. PAComplex is the first web server investigating both pMHC and peptide-TCR interfaces to infer peptide antigens and homologous peptide antigens of a query. This server first identifies significantly similar TCR-pMHC templates (joint Z-value >= 4.0) of the query by using antibody-antigen and protein-protein interacting scoring matrices for peptide-TCR and pMHC interfaces, respectively. PAComplex then identifies the homologous peptide antigens of these hit templates from complete pathogen genome databases (>= 10(8) peptide candidates from 864 628 protein sequences of 389 pathogens) and experimental peptide databases (80 057 peptides in 2287 species). Finally, the server outputs peptide antigens and homologous peptide antigens of the query and displays detailed interacting models (e.g. hydrogen bonds and steric interactions in two interfaces) of hitTCR-pMHC templates. Experimental results demonstrate that the proposed server can achieve high prediction accuracy and offer potential peptide antigens across pathogens. We believe that the server is able to provide valuable insights for the peptide vaccine and MHC restriction. The PAComplex sever is available at http://PAcomplex.life.nctu.edu.tw.
Template-based scoring functions for visualising biological insights of H-2K(b)-peptide-TCR complexes
(2013)
Liu, I-Hsin
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Lo, Yu-Shu
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Yang, Jinn-Moon
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生物資訊及系統生物研究所
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Institude of Bioinformatics and Systems Biology
Major Histocompatibility Complex (MHC), peptide and T-Cell Receptor (TCR) play an essential role of adaptive immune responses. Many prediction servers are available for identification of peptides that bind to MHC class I molecules but often lack detailed interacting residues for analysing MHC-peptide-TCR interaction mechanisms. This study considers both the interface similarity and the interacting force for identifying binding models. Our model, considering both the MHC-peptide and the peptide-TCR interfaces, is able to provide visualisation and the biological insights of binding models. We believe that our model is useful for the development of peptide-based vaccines.
Template-based Scoring Functions for Visualizing Biological Insights of H-2K(b)-peptide-TCR Complexes
(2010-01-01)
Liu, I-Hsin
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Lo, Yu-Shu
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Yang, Jinn-Moon
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生物資訊及系統生物研究所
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Institude of Bioinformatics and Systems Biology
Class-I major histocompatibility complex (MHC), peptide, and T-cell receptor (TCR) play an essential role of adaptive immune responses. Many prediction servers are available for identification of peptides that bind to MHC class I molecules. These servers are often lack of detailed interacting residues and binding models for analyzing MHC-peptide-TCR interaction mechanisms. This study numerously enhanced the template-based scoring function derived from protein-protein interactions for identifying MHC-peptide-TCR binding models. The scoring function considers both the template similarity and interacting force to ensure the statistically significant interface similarity between the peptide candidates and structure templates. The result shows that our scoring function is comparative to the public websites for identifying MHC binding peptides. Our model, considering both the MHC-peptide and peptide-TCR interfaces, is able to provide visualization and the biological insights of MHC-peptide-TCR binding models. We believe that our model is useful for the development of peptide-based vaccines.