Caracterización in silico y análisis filogenético de la malato sintasa de Paracoccidioides brasiliensis asociada a su proceso de infección

Objetivo: Realizar la caracterización in silico de la proteína malato sintasa de Paracoccidioides brasiliensis relacionada con su proceso de infección. Materiales y métodos: Se obtuvo la secuencia aminoacídica de malato sintasa de P. brasiliensis (código de accesión: AQ75800.3) del GenBank. Dicha secuencia fue utilizada para determinar sus principales parámetros bioquímicos, dominios y regiones conservadas, predicción de estructuras secundarias y modelamiento tridimensional, empleando las herramientas bioinformáticas ProtParam, Clustal Omega, PHD Secondary Structure Prediction, SWISSMODEL, Phyre2 y Robetta, respectivamente. La visualización y comparación de los modelos tridimensionales se realizó empleando PyMol. Finalmente, se buscó el modelo de sustitución aminoacídica con modelgenerator_v_85 y se usó para generar una filogenia de máxima verosimilitud en PHYML, la cual se visualizó en FigTree.

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Evaluation of in silico protein secondary structure prediction methods by employing statistical techniques

Biomedical and Biotechnology Research Journal (BBRJ) ◽

10.4103/bbrj.bbrj_28_17 ◽

2017 ◽

Vol 1 (1) ◽

pp. 29 ◽

Cited By ~ 5

Author(s):

Kandavelmani Angamuthu ◽

Shanmughavel Piramanayagam

Keyword(s):

Secondary Structure ◽

In Silico ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Statistical Techniques ◽

Prediction Methods ◽

Protein Secondary Structure Prediction

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Análisis bioinformático de la endolisina LysB4: Un agente antimicrobiano utilizado en el biocontrol de Bacillus cereus en alimentos

Ágora Revista Científica ◽

10.21679/arc.v3i2.71 ◽

2017 ◽

Vol 3 (2) ◽

pp. 375

Author(s):

Renzo Punil ◽

Gustavo A. Sandoval

Keyword(s):

Secondary Structure ◽

Bacillus Cereus ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Para Determinar

Objetivo: Realizar el análisis bioinformático de la endolisina LysB4 empleada en el biocontrol de Bacilllus cereus en la industria alimentaria. Materiales y Métodos: Se realizó la búsqueda de endolisinas bacterianas empleando la base de datos del GenBank. A partir de dicha información, se obtuvo la secuencia aminoacídica de la endolisina LysB4 (código de accesión AFF27501.1). Dicha secuencia fue utilizada para determinar sus principales parámetros bioquímicos, dominios conservados entre proteínas cercanamente relacionadas, predicción de estructuras secundarias y modelamiento tridimensional, empleando las herramientas bioinformáticas ProtParam, Prosite, PHD Secondary Structure Prediction, Cn3D, SWISS-MODEL, Phyre2 y Robetta, respectivamente. La visualización de modelos tridimensionales se realizó empleando PyMol.

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In silico Secondary Structure Prediction Method (Kalasalingam University Structure Prediction Method) using Comparative Analysis

Trends in Bioinformatics ◽

10.3923/tb.2010.11.19 ◽

2010 ◽

Vol 3 (1) ◽

pp. 11-19 ◽

Cited By ~ 3

Author(s):

A. Mugilan ◽

Ajitha . ◽

M. Cathrin ◽

M. Kumar ◽

Devi . ◽

...

Keyword(s):

Comparative Analysis ◽

Secondary Structure ◽

In Silico ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Prediction Method ◽

Structure Prediction Method ◽

Secondary Structure Prediction Method

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Caracterización in silico de la proteína Dpr de Streptococcus mutans asociada a la formación de caries dental humana

Ágora Revista Científica ◽

10.21679/arc.v1i1.28 ◽

2015 ◽

Vol 1 (1) ◽

pp. 26

Author(s):

Malú Chavez-Gamarra ◽

Gustavo A. Sandoval

Keyword(s):

Secondary Structure ◽

Streptococcus Mutans ◽

In Silico ◽

Structure Prediction ◽

Secondary Structure Prediction

<p>Objetivo: Realizar la caracterización in silico de la protína Dpr de Streptococcus mutans relacionada con su tolerancia al daño oxidativo durante la formación de caries dental humana. Material y métodos: Para llevar a cabo esta caracterización se ha empleado la secuencia aminoacídica de la proteína Dpr de S. mutans cepa GS-5 obtenida a partir de la base de datos del GenBank (código de accesión BAA96472.1), la cual fue utilizada para la determinación de sus parámetros bioquímicos, dominios conservados, predicción de estructuras<br />secundarias y modelamiento por homología, empleando las herramientas bioinformáticas ProtParam, Prosite, PHD Secondary Structure Prediction y SWISS-MODEL, respectivamente. La visualización de modelos tridimensionales se realizó empleando PyMol. Resultados: La proteína Dpr de S. mutans es una molécula ácida y de bajo peso molecular y además presenta un dominio altamente conservado involucrado en la unión al hierro. A partir de la predicción de su estructura tridimensional, la proteína Dpr presenta una conformación dodecamérica que estaría implicada en su alta afinidad por hierro así como por otros iones divalentes. Conclusión: La proteína Dpr de S. mutans resulta importante para el proceso infectivo de esta bacteria la cual debe generar una variedad de estrategias para la inhibición de la colonización de la cavidad bucal.</p>

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IRSS: a web-based tool for automatic layout and analysis of IRES secondary structure prediction and searching system in silico

BMC Bioinformatics ◽

10.1186/1471-2105-10-160 ◽

2009 ◽

Vol 10 (1) ◽

pp. 160 ◽

Cited By ~ 22

Author(s):

Tzong-Yuan Wu ◽

Chi-Chun Hsieh ◽

Jun-Jie Hong ◽

Chung-Yung Chen ◽

Yuh-Show Tsai

Keyword(s):

Secondary Structure ◽

In Silico ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Web Based ◽

Automatic Layout

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In silico methods for co-transcriptional RNA secondary structure prediction and for investigating alternative RNA structure expression

Methods ◽

10.1016/j.ymeth.2017.04.009 ◽

2017 ◽

Vol 120 ◽

pp. 3-16 ◽

Cited By ~ 8

Author(s):

Irmtraud M. Meyer

Keyword(s):

Secondary Structure ◽

In Silico ◽

Rna Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Secondary Structure Prediction ◽

Rna Secondary Structure Prediction ◽

In Silico Methods

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Faculty Opinions recommendation of COFOLD: an RNA secondary structure prediction method that takes co-transcriptional folding into account.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718010599.793476797 ◽

2013 ◽

Author(s):

Scott Silverman

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Secondary Structure Prediction ◽

Prediction Method ◽

Rna Secondary Structure Prediction ◽

Structure Prediction Method ◽

Secondary Structure Prediction Method

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Faculty Opinions recommendation of Protein secondary structure prediction using deep convolutional neural fields.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726067888.793548469 ◽

2018 ◽

Author(s):

Patrice Koehl

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Neural Fields ◽

Protein Secondary Structure Prediction

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A Discrete Hopfield Neural Network Based MIS Finding Algorithm for Stems Selecting and Its Application in RNA Secondary Structure Prediction

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2008.00051 ◽

2009 ◽

Vol 31 (1) ◽

pp. 51-58

Author(s):

Qi LIU ◽

Yin ZHANG ◽

Xiu-Zi YE ◽

Rong-Dong YU

Keyword(s):

Neural Network ◽

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Secondary Structure Prediction ◽

Hopfield Neural Network ◽

Rna Secondary Structure Prediction

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A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

Current Drug Discovery Technologies ◽

10.2174/1570163815666180227162157 ◽

2019 ◽

Vol 16 (2) ◽

pp. 159-172 ◽

Cited By ~ 3

Author(s):

Elaheh Kashani-Amin ◽

Ozra Tabatabaei-Malazy ◽

Amirhossein Sakhteman ◽

Bagher Larijani ◽

Azadeh Ebrahim-Habibi

Keyword(s):

Systematic Review ◽

Secondary Structure ◽

Structure Prediction ◽

Web Of Science ◽

Secondary Structure Prediction ◽

Structural Information ◽

Protein Secondary Structure ◽

Structural Features ◽

Prediction Tools ◽

Insight Into

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

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