Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 3 de 3
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Bioinformatics ; 2019 Dec 04.
Artigo em Inglês | MEDLINE | ID: mdl-31800008

RESUMO

SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: The RocaSec software is publicly available under the MIT License at https://github.com/ahmedaq/RocaSec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

2.
Bioinformatics ; 35(20): 3884-3889, 2019 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-31250884

RESUMO

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.

3.
PLoS Comput Biol ; 14(9): e1006409, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-30192744

RESUMO

Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus (HIV) and the Hepatitis C Virus (HCV) have been demonstrated to be informative of viral fitness. Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under diverse selective pressures. Here, considering multiple HIV and HCV proteins, we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tightly linked to the structural and functional properties of the respective proteins. Specifically, by introducing a robust statistical method based on sparse principal component analysis, we identify near-disjoint sets of collectively-correlated residues (sectors) having mostly a one-to-one association to largely distinct structural or functional domains. This suggests that the distinct phenotypic properties of HIV/HCV proteins often give rise to quasi-independent modes of evolution, with each mode involving a sparse and localized network of mutational interactions. Moreover, individual inferred sectors of HIV are shown to carry immunological significance, providing insight for guiding targeted vaccine strategies.


Assuntos
Infecções por HIV/virologia , HIV-1 , Hepacivirus , Hepatite C/virologia , Algoritmos , Alelos , Biologia Computacional , Simulação por Computador , Análise Mutacional de DNA , DNA Viral , Progressão da Doença , Evolução Molecular , Proteína do Núcleo p24 do HIV/fisiologia , Antígenos HLA/química , Humanos , Sistema Imunitário , Distribuição Normal , Fenótipo , Análise de Componente Principal , Domínios Proteicos , Relação Estrutura-Atividade , Produtos do Gene nef do Vírus da Imunodeficiência Humana/fisiologia
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA