RESUMEN
Abstract In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.
Resumo Nos últimos anos, o desenvolvimento de tecnologias de alto rendimento para obtenção de dados sequenciais potencializou a possibilidade de análise de dados proteicos in silico. No entanto, quando se trata de estudos de interação de poliproteínas virais, existe uma lacuna na representação dessas proteínas, devido ao seu tamanho e comprimento. Para estudos utilizando técnicas de ponta como o Aprendizado de Máquina, uma boa representação dessas proteínas é imprescindível. Apresentamos uma alternativa para este problema, implementando um protocolo de fragmentação e modelagem para preparar essas poliproteínas na forma de fragmentos de peptídeos. Tal procedimento é feito por diversos scripts, implementados em conjunto no workflow que chamamos de PolyPRep, uma ferramenta escrita em script Python e disponível no GitHub. Este software está disponível gratuitamente apenas para usuários não comerciais.
Asunto(s)
Proteasa del VIH , Poliproteínas , Programas Informáticos , Simulación del Acoplamiento MolecularRESUMEN
Abstract In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.
Resumo Nos últimos anos, o desenvolvimento de tecnologias de alto rendimento para obtenção de dados sequenciais potencializou a possibilidade de análise de dados proteicos in silico. No entanto, quando se trata de estudos de interação de poliproteínas virais, existe uma lacuna na representação dessas proteínas, devido ao seu tamanho e comprimento. Para estudos utilizando técnicas de ponta como o Aprendizado de Máquina, uma boa representação dessas proteínas é imprescindível. Apresentamos uma alternativa para este problema, implementando um protocolo de fragmentação e modelagem para preparar essas poliproteínas na forma de fragmentos de peptídeos. Tal procedimento é feito por diversos scripts, implementados em conjunto no workflow que chamamos de PolyPRep, uma ferramenta escrita em script Python e disponível no GitHub. Este software está disponível gratuitamente apenas para usuários não comerciais.
RESUMEN
BACKGROUND: Marginal alopecia presents itself frequently in dermatological practice. For patients who present peripheral scalp hair loss, cicatricial and non-cicatricial alopecia should be considered, such as ophiasis, traction alopecia and frontal fibrosing alopecia. In spite of the several types of hair loss that have been described in cutaneous lupus erythematosus, the 'pseudo-fringe sign' had never been observed in such patents. OBJECTIVE: This study aims at including the 'pseudo-fringe sign' as another possibility of clinical manifestation in patients with cutaneous lupus erythematosus. METHODS: A case report of a 67-year-old lady with the diagnosis of lupus erythematosus and peripheral hair loss is shown in this study; the respective differential diagnosis have also been discussed. RESULTS: The diagnosis of a scarring alopecia caused by lupus erythematosus was established with dermoscopy and histopathology. CONCLUSION: Cutaneous lupus erythematosus is another diagnostic possibility for patients who display the 'pseudo-fringe sign'.
Asunto(s)
Alopecia , Liquen Plano , Lupus Eritematoso Cutáneo , Lupus Eritematoso Sistémico , Anciano , Alopecia/patología , Diagnóstico Diferencial , Femenino , Fibrosis , Humanos , Liquen Plano/patología , Lupus Eritematoso Cutáneo/diagnóstico , Lupus Eritematoso Sistémico/diagnósticoRESUMEN
In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.