Syed Faraz Ahmed1, Ahmed A Quadeer1, David Morales-Jimenez2, Matthew R McKay1,3. 1. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. 2. Institute of Electronics, Communications and Information Technology, Queen's University Belfast, Belfast, UK. 3. Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Abstract
MOTIVATION: Patterns of mutational correlations, learnt from patient-derived sequences of human immunodeficiency virus (HIV) proteins, are informative of biochemically linked networks of interacting sites that may enable viral escape from the host immune system. Accurate identification of these networks is important for rationally designing vaccines which can effectively block immune escape pathways. Previous computational methods have partly identified such networks by examining the principal components (PCs) of the mutational correlation matrix of HIV Gag proteins. However, driven by a conservative approach, these methods analyze the few dominant (strongest) PCs, potentially missing information embedded within the sub-dominant (relatively weaker) ones that may be important for vaccine design. RESULTS: By using sequence data for HIV Gag, complemented by model-based simulations, we revealed that certain networks of interacting sites that appear important for vaccine design purposes are not accurately reflected by the dominant PCs. Rather, these networks are encoded jointly by both dominant and sub-dominant PCs. By incorporating information from the sub-dominant PCs, we identified a network of interacting sites of HIV Gag that associated very strongly with viral control. Based on this network, we propose several new candidates for a potent T-cell-based HIV vaccine. AVAILABILITY AND IMPLEMENTATION: Accession numbers of all sequences used and the source code scripts for all analysis and figures reported in this work are available online at https://github.com/faraz107/HIV-Gag-Immunogens. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Patterns of mutational correlations, learnt from patient-derived sequences of human immunodeficiency virus (HIV) proteins, are informative of biochemically linked networks of interacting sites that may enable viral escape from the host immune system. Accurate identification of these networks is important for rationally designing vaccines which can effectively block immune escape pathways. Previous computational methods have partly identified such networks by examining the principal components (PCs) of the mutational correlation matrix of HIV Gag proteins. However, driven by a conservative approach, these methods analyze the few dominant (strongest) PCs, potentially missing information embedded within the sub-dominant (relatively weaker) ones that may be important for vaccine design. RESULTS: By using sequence data for HIV Gag, complemented by model-based simulations, we revealed that certain networks of interacting sites that appear important for vaccine design purposes are not accurately reflected by the dominant PCs. Rather, these networks are encoded jointly by both dominant and sub-dominant PCs. By incorporating information from the sub-dominant PCs, we identified a network of interacting sites of HIV Gag that associated very strongly with viral control. Based on this network, we propose several new candidates for a potent T-cell-based HIV vaccine. AVAILABILITY AND IMPLEMENTATION: Accession numbers of all sequences used and the source code scripts for all analysis and figures reported in this work are available online at https://github.com/faraz107/HIV-Gag-Immunogens. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Dariusz K Murakowski; John P Barton; Lauren Peter; Abishek Chandrashekar; Esther Bondzie; Ang Gao; Dan H Barouch; Arup K Chakraborty Journal: Proc Natl Acad Sci U S A Date: 2021-02-02 Impact factor: 11.205
Authors: Muhammad Saqib Sohail; Syed Faraz Ahmed; Ahmed Abdul Quadeer; Matthew R McKay Journal: Adv Drug Deliv Rev Date: 2021-01-17 Impact factor: 17.873
Authors: Ang Gao; Zhilin Chen; Assaf Amitai; Julia Doelger; Vamsee Mallajosyula; Emily Sundquist; Florencia Pereyra Segal; Mary Carrington; Mark M Davis; Hendrik Streeck; Arup K Chakraborty; Boris Julg Journal: iScience Date: 2021-03-15
Authors: Ang Gao; Zhilin Chen; Florencia Pereyra Segal; Mary Carrington; Hendrik Streeck; Arup K Chakraborty; Boris Julg Journal: bioRxiv Date: 2020-05-15