ORCID Profile
0000-0002-2141-7185
Current Organisation
Universidad de Granada
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Elsevier BV
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Statistica Sinica (Institute of Statistical Science)
Date: 2021
Publisher: Oxford University Press (OUP)
Date: 04-12-2019
DOI: 10.1093/BIOINFORMATICS/BTZ890
Abstract: Patterns of mutational correlations, learnt from protein sequences, have been shown to be informative of co-evolutionary sectors that are tightly linked to functional and/or structural properties of proteins. Previously, we developed a statistical inference method, robust co-evolutionary analysis (RoCA), to reliably predict co-evolutionary sectors of proteins, while controlling for statistical errors caused by limited data. RoCA was demonstrated on multiple viral proteins, with the inferred sectors showing close correspondences with experimentally-known biochemical domains. To facilitate seamless use of RoCA and promote more widespread application to protein data, here we present a standalone cross-platform package ‘RocaSec’ which features an easy-to-use GUI. The package only requires the multiple sequence alignment of a protein for inferring the co-evolutionary sectors. In addition, when information on the protein biochemical domains is provided, RocaSec returns the corresponding statistical association between the inferred sectors and biochemical domains. The RocaSec software is publicly available under the MIT License at hmedaq/RocaSec. Supplementary data are available at Bioinformatics online.
Publisher: IEEE
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: IEEE
Date: 04-2015
Publisher: Public Library of Science (PLoS)
Date: 07-09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: IEEE
Date: 05-2019
Publisher: Oxford University Press (OUP)
Date: 28-06-2019
DOI: 10.1093/BIOINFORMATICS/BTZ524
Abstract: 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. 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. Accession numbers of all sequences used and the source code scripts for all analysis and figures reported in this work are available online at araz107/HIV-Gag-Immunogens. Supplementary data are available at Bioinformatics online.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for David Morales Jimenez.