El proyecto es apoyado en parte por la Fundación Alfred P. Sloan, Premio NSF carrera (DBI 1027394), y el Instituto Nacional de Ciencias Médicas Generales (GM083107, GM084222).

<ol>
	<li>Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Template-based+modeling+and+free+modeling+by+I-TASSER+in+CASP7.">Template-based modeling and free modeling by I-TASSER in CASP7.</a> Proteins. 69, 108-117 (2007).</li><li>Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=I-TASSER:+Fully+automated+protein+structure+prediction+in+CASP8.">I-TASSER: Fully automated protein structure prediction in CASP8.</a> Proteins. 77, 100-113 (2009).</li><li>Wu, S., Skolnick, J., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ab+initio+modeling+of+small+proteins+by+iterative+TASSER+simulations.">Ab initio modeling of small proteins by iterative TASSER simulations.</a> BMC Biol. 5, 17-17 (2007).</li><li>Roy, A., Kucukural, A., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=I-TASSER:+a+unified+platform+for+automated+protein+structure+and+function+prediction.">I-TASSER: a unified platform for automated protein structure and function prediction.</a> Nat Protoc. 5, 725-738 (2010).</li><li>Wu, S., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LOMETS:+a+local+meta-threading-server+for+protein+structure+prediction.">LOMETS: a local meta-threading-server for protein structure prediction.</a> Nucleic Acids Res. 35, 3375-3382 (2007).</li><li>Zhang, Y., Kihara, D., Skolnick, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Local+energy+landscape+flattening:+parallel+hyperbolic+Monte+Carlo+sampling+of+protein+folding.">Local energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding.</a> Proteins. 48, 192-201 (2002).</li><li>Li, Y., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=REMO:+A+new+protocol+to+refine+full+atomic+protein+models+from+C-alpha+traces+by+optimizing+hydrogen-bonding+networks.">REMO: A new protocol to refine full atomic protein models from C-alpha traces by optimizing hydrogen-bonding networks.</a> Proteins. 76, 665-676 (2009).</li><li>Zhang, J., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+protein+structure+refinement+using+fragment+guided+molecular+dynamics+simulations.">High-resolution protein structure refinement using fragment guided molecular dynamics simulations.</a> , (2011).</li><li>Roy, A., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=COFACTOR:+protein-ligand+binding+site+predictions+by+global+structure+similarity+match+and+local+geometry+refinement.">COFACTOR: protein-ligand binding site predictions by global structure similarity match and local geometry refinement.</a> , (2011).</li><li>Zhang, Y., Hubner, I. A., Arakaki, A. K., Shakhnovich, E., Skolnick, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+origin+and+highly+likely+completeness+of+single-domain+protein+structures.">On the origin and highly likely completeness of single-domain protein structures.</a> Proc. Natl. Acad. Sci. U. S. A. 103, 2605-2610 (2006).</li><li>Zhang, Y., Kolinski, A., Skolnick, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TOUCHSTONE+II:+a+new+approach+to+ab+initio+protein+structure+prediction.">TOUCHSTONE II: a new approach to ab initio protein structure prediction.</a> Biophys. J. 85, 1145-1164 (2003).</li><li>Wu, S., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comprehensive+assessment+of+sequence-based+and+template-based+methods+for+protein+contact+prediction.">A comprehensive assessment of sequence-based and template-based methods for protein contact prediction.</a> Bioinformatics. 24, 924-931 (2008).</li><li>Zhang, Y., Skolnick, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SPICKER:+a+clustering+approach+to+identify+near-native+protein+folds.">SPICKER: a clustering approach to identify near-native protein folds.</a> J. Comput. Chem. 25, 865-871 (2004).</li><li>Zhang, J., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+protein+structure+refinement+using+fragment+guided+molecular+dynamics+simulations.">High-resolution protein structure refinement using fragment guided molecular dynamics simulations.</a> , (2010).</li><li>Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=I-TASSER+server+for+protein+3D+structure+prediction.">I-TASSER server for protein 3D structure prediction.</a> BMC Bioinformatics. 9, 40-40 (2008).</li><li>Xu, J., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+significant+is+a+protein+structure+similarity+with+TM-score+=+0.5?.">How significant is a protein structure similarity with TM-score = 0.5?.</a> Bioinformatics. 26, 889-895 (2010).</li><li>Zhang, Y., Skolnick, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scoring+function+for+automated+assessment+of+protein+structure+template+quality.">Scoring function for automated assessment of protein structure template quality.</a> Proteins. 57, 702-710 (2004).</li><li>Barrett, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nomenclature+Committee+of+the+International+Union+of+Biochemistry+and+Molecular+Biology+(NC-IUBMB).+Enzyme+Nomenclature.+Recommendations+1992.+Supplement+4:+corrections+and+additions+(1997).">Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB). Enzyme Nomenclature. Recommendations 1992. Supplement 4: corrections and additions (1997).</a> Eur J Biochem. 250, 1-6 (1997).</li><li>Ashburner, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+ontology:+tool+for+the+unification+of+biology.+The+Gene+Ontology+Consortium.">Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.</a> Nat. Genet. 25, 25-29 (2000).</li><li>Zhang, Y., Skolnick, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TM-align:+a+protein+structure+alignment+algorithm+based+on+the+TM-score.">TM-align: a protein structure alignment algorithm based on the TM-score.</a> Nucleic. Acids. Res. 33, 2302-2309 (2005).</li><li>Xu, D., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RK+Ab+Intio+Protein+Structure+Prediction.">RK Ab Intio Protein Structure Prediction.</a> , (2011).</li><li>Kim, D. E., Chivian, D., Baker, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+structure+prediction+and+analysis+using+the+Robetta+server.">Protein structure prediction and analysis using the Robetta server.</a> Nucleic. Acids. Res. 32, W526-W531 (2004).</li><li>Roy, A., Mukherjee, S., Hefty, P. S., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inferring+protein+function+by+global+and+local+similarity+of+structural+analogs.">Inferring protein function by global and local similarity of structural analogs.</a> , (2011).</li><li>Marchler-Bauer, A., Bryant, S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CD-Search:+protein+domain+annotations+on+the+fly.">CD-Search: protein domain annotations on the fly.</a> Nucleic. Acids. Res. 32, W327-W331 (2004).</li><li>Finn, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Pfam+protein+families+database.">The Pfam protein families database.</a> Nucleic. Acids. Res. 38, D211-D222 (2010).</li><li>Zdobnov, E. M., Apweiler, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=InterProScan--an+integration+platform+for+the+signature-recognition+methods+in+InterPro.">InterProScan--an integration platform for the signature-recognition methods in InterPro.</a> Bioinformatics. 17, 847-848 (2001).</li></ol>

No hay conflictos de interés declarado.

El protocolo presentado es una guía general para la estructura y la función de modelado usando el servidor de I-TASSER. A pesar de que este procedimiento automatizado funciona muy bien para la mayoría de las proteínas, las intervenciones humanas a menudo ayudan a mejorar significativamente la precisión de modelos, especialmente para las proteínas que carecen de las plantillas de cerca en la biblioteca de AP. Los usuarios pueden intervenir en los modelos I-TASSER de las siguientes maneras: (a) separación de multi-dominio de las proteínas, (b) la prestación de restricciones externas para mejorar el montaje de la estructura, y (c) la eliminación de las plantillas durante el modelado. Dividir multi-dominio de la proteína: Muchas secuencias de proteínas de largo con frecuencia contienen múltiples dominios atados por la flexibilidad enlazador regiones, lo que hace su difícil elucidación de la estructura mediante técnicas experimentales y computacionales. Sin embargo, como los dominios son independientes plegables entidades y puede realizar la función molecular distinta, esconveniente dividir a largo multi-dominio de las proteínas y el modelo de cada dominio por separado. Dominios de modelado de forma individual, no sólo acelerará el proceso de predicción, sino que también aumenta la calidad de la alineación de consulta de plantilla, lo que resulta en una estructura más fiable y predicciones de la función. Los límites del dominio de las secuencias de proteínas se puede predecir el uso de programas externos disponibles gratuitamente en línea, tales como NCBI CDD 24, 25 o PFAM InterProScan 26. Además, si las alineaciones LOMETS threading están disponibles para la proteína de la consulta, los límites del dominio puede ser localizado por la identificación visual de los residuos de largos tramos no alineados en las plantillas de rosca superior (ver Paso 5.4). Estas regiones no alineados en su mayoría corresponden a las zonas de dominio enlazador. Si varios dominios las plantillas ya están disponibles en la biblioteca de plantillas AP con todos los dominios de consulta alineados, entonces la consulta proteína puede ser modelado como de larga duración. Proporcionar restricciones externas <pclass = &quot;jove_content&quot;&gt; Las simulaciones montaje de la estructura de I-TASSER son principalmente guiados por restricciones espaciales recogidos a partir de la rosca LOMETS plantillas. Para las proteínas de consulta que han afectado a buena threading (Norm. Z-score&gt; 1) en la biblioteca de plantillas, que se deriven limitaciones espaciales son en su mayoría de alta precisión y I-TASSER va a generar modelos de alta resolución estructural de estas proteínas. Por el contrario, las proteínas de consulta que han débil o no hit threading (Norm. Z-score &lt;1), recogidos restricciones espaciales suelen contener errores debido a la incertidumbre de la plantilla y la alineación. Para estos objetivos de proteínas, especificado por el usuario la información espacial puede ser muy útil para mejorar la calidad del modelo de predecir. Los usuarios pueden proporcionar restricciones externas al servidor I-TASSER de dos maneras: A. Especificar contacto / distancia restricciones Experimentalmente caracteriza residuos entre contactos / distancias, por ejemplo, a partir de RMN oentrecruzamiento experimentos, se puede especificar mediante la subida de un archivo de la moderación. Un archivo de ejemplo se muestra en la Figura 8, donde la columna 1, se especifica el tipo de restricción, es decir, &quot;DIST&quot; o &quot;Contacto&quot;. A la moderación a distancia (DIST), las columnas 2 y 4 contienen posiciones de residuos (i, j), las columnas 3 y 5 contienen el átomo-tipos en el residuo y la columna 6 se especifica la distancia entre los dos átomos especificado. Para sistemas de retención (contacto), las columnas 2 y 3 contienen las posiciones (i, j) de los residuos que deben estar en contacto. La distancia entre el centro de cadenas laterales de estos pares de residuos de contacto se decidió sobre la base de las distancias observadas en estructuras conocidas en el AP. I-TASSER a tratar de sacar estos pares de átomos cerca de la distancia especificada en las simulaciones de la estructura de refinamiento. B. Especificar una plantilla de estructura de proteínas LOMETS programas de threading utilizar una biblioteca de AP representante para encontrar pliegues plausible para la prot consultaein. Aunque el uso de una estructura representativa de la biblioteca ayuda a reducir el tiempo necesario para calcular las alineaciones estructura de la secuencia, es posible que una proteína de buena plantilla se pierde en la biblioteca o la plantilla no han sido identificados por los programas de LOMETS threading, a pesar de que es presentes en la biblioteca. En estos casos, el usuario debe especificar la estructura de la proteína deseada en la plantilla. Para especificar la estructura de la proteína como una plantilla adicional, los usuarios pueden subir un archivo de formato PDB estructura o especificar el ID de AP de una estructura de la proteína depositada en la biblioteca de AP. El I-TASSER va a generar la alineación de consulta de plantilla mediante el programa de Muster 23 y recogerá las limitaciones espaciales tanto de la plantilla especificada por el usuario y las plantillas LOMETS para guiar a la simulación de montaje de la estructura. Debido a la precisión de las restricciones LOMETS es diferente para diferentes objetivos, el peso de las restricciones LOMETS es más fuerte en fácil (homóloga) targets que en el disco duro (no homólogos) los objetivos, que han sido sistemáticamente sintonizado en nuestra formación de referencia. Los usuarios también pueden especificar su propia plantilla de consulta alineaciones. El servidor acepta la alineación en dos formatos: el formato FASTA (Figura 9) y el formato 3D (Figura 9B). El formato de FASTA es estándar y se describe en <a href="http://zhanglab.%20ccmb.med.umich.edu/FASTA/">http://zhanglab. ccmb.med.umich.edu / FASTA /</a> . El formato 3D es similar al formato estándar PDB ( <a href="http://www.wwpdb.org/documentation/format32/sect9.html">http://www.wwpdb.org/documentation/format32/sect9.html</a> ), pero dos columnas adicionales derivados de las plantillas se agregan a los registros ATOM (véase la Figura 9B): Columnas 1-30: Atom (C-alfa solamente) y los residuos de los nombres de la secuencia de consulta. Columnas 31-54: Coordenadas de los átomos de C-alfa de la consulta copiada de los átomos correspondientes en la plantilla. Columnas 55-59: número de residuo correspondiente en la plantilla sobre la base de la alineación Columnas 60-64: nombre de residuo correspondiente en la plantilla Excluir a las proteínas de plantillas Las proteínas son moléculas flexibles y pueden adoptar varios estados conformacionales de cambiar su actividad biológica. Por ejemplo, las estructuras de las proteínas quinasas y muchas proteínas de membrana se han resuelto en la conformación de activos e inactivos. También la presencia o ausencia de ligando puede causar grandes movimientos estructurales. Si bien todos los estados de conformación de la plantilla son iguales para los programas de threading, es deseable que el modelo de la consulta el uso de plantillas en un solo estado en particular. Una nueva opción en el servidor permite al usuario excluir las proteínas de la plantilla durante el modelado estructura. Esta característica también permite al usuario elegir el nivel de homología de los modelos que se utilizarán para el modelado. Los usuarios pueden excluir plantilla de proteínas from la biblioteca I-TASSER por: A. Especificación de un corte de identidad de secuencia Los usuarios pueden utilizar esta opción para excluir las proteínas homólogas de la biblioteca de plantillas I-TASSER. El nivel de homología se establece en función del punto de corte de identidad de secuencia, es decir, el número de residuos idénticos entre la consulta y la proteína de la plantilla dividida por la longitud de la secuencia de la secuencia de la consulta. Por ejemplo, si el usuario escribe en el &quot;70%&quot; en la forma prevista, todas las proteínas de las plantillas que tienen una identidad de secuencia&gt; 70% a la proteína de consulta I-serán excluidos de la biblioteca de plantillas I-TASSER. B. Excluir las proteínas específicas de plantilla Proteínas específicas de la plantilla puede ser excluido de la biblioteca de plantillas I-TASSER al cargar una lista que contiene los identificadores de AP de las estructuras a ser excluidos. Un archivo de ejemplo se muestra en la Figura 10. A medida que la misma proteína puede existir en varias entradas en la biblioteca de AP, I-TASSER síRVer de forma predeterminada excluye las plantillas especificadas (en la columna 1), así como todas las otras plantillas de la biblioteca que tienen una identidad&gt; 90% de las plantillas especificadas. Los usuarios también pueden especificar una identidad de corte diferentes, por ejemplo, 70%, en todas las plantillas con la identidad de&gt; 70% a las proteínas de la plantilla será excluido.

<ol><li> FASTA formato secuencia de aminoácidos de la proteína a ser el modelo (véase, <a href="http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml">http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml</a> ). </li><li> Un ordenador personal con acceso a Internet y un navegador web. </li><li> Software de visualización molecular, por ejemplo, RasMol o PyMOL, para el análisis de la estructura prevista superior y sitios funcionales. </li></ol>

a protocol for computer-based protein structure and function prediction

Los proyectos de secuenciación del genoma ha cifrado millones de secuencias de proteínas, que requieren el conocimiento de su estructura y función de mejorar la comprensión de su función biológica. Aunque los métodos experimentales pueden proporcionar información detallada para una pequeña fracción de estas proteínas, modelado computacional que se necesita para la mayoría de las moléculas de proteínas que son caracterizados experimentalmente. El servidor de I-TASSER es un banco de trabajo on-line de alta resolución de modelado de la estructura y función de proteínas. Dada una secuencia de la proteína, una salida típica del servidor I-TASSER incluye la predicción de estructura secundaria, prevé la accesibilidad solvente de cada residuo, las proteínas homólogas de plantilla detectado por roscado y alineaciones de la estructura, hasta cinco de larga duración en los modelos estructurales terciario, y basados ​​en estructuras anotaciones funcionales para la clasificación de las enzimas, los términos de ontología de genes y la proteína-ligando sitios de unión. Todas las previsiones están marcadas con una puntuación de confianza quecuenta la precisión de la predicción son sin conocer los datos experimentales. Para facilitar las solicitudes especiales de los usuarios finales, el servidor proporciona canales para aceptar especificado por el usuario entre la distancia y el contacto con residuos de mapas para cambiar los modelos I-TASSER, sino que también permite al usuario especificar cualquier proteína como plantilla, o para excluir a cualquier plantilla proteínas durante las simulaciones de la estructura de montaje. La información estructural podría ser recogida por los usuarios sobre la base de evidencias experimentales o conocimientos biológicos con el fin de mejorar la calidad de la I-TASSER predicciones. El servidor fue evaluado como el mejor de los programas de estructura de las proteínas y las predicciones en función de los experimentos CASP reciente de toda la comunidad. Actualmente hay&gt; 20.000 científicos registrados en más de 100 países que están utilizando el on-line I-TASSER servidor.

Watch this Scientific Journal Video about A Protocol for Computer-Based Protein Structure and Function Prediction at JoVE.com

A Protocol for Computer-Based Protein Structure and Function Prediction

Directrices para el ordenador basado en la caracterización estructural y funcional de la proteína mediante la canalización I-TASSER se describe. A partir de secuencias de proteínas de la consulta, los modelos 3D se generan con múltiples alineaciones threading e iterativo simulaciones estructurales de montaje. Inferencias funcionales a partir de entonces son elaborados sobre la base de los partidos a las proteínas de estructura conocida y funciones.

Un protocolo para la estructura de la proteína por computadora y la función de predicción

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the ...

a-protocol-for-computer-based-protein-structure-function

Research

JoVE Journal

Biology

68.1K vistas.  University of Michigan. Directrices para el ordenador basado en la caracterización estructural y funcional de la proteína mediante la canalización I-TASSER se describe. A partir de secuencias de proteínas de la consulta, los modelos 3D se generan con múltiples alineaciones threading e iterativo simulaciones estructurales de montaje. Inferencias funcionales a partir de entonces son elaborados sobre la base de los partidos a las proteínas de estructura conocida y funciones.

Video: A Protocol for Computer-Based Protein Structure and Function Prediction

The Importance of Correct Protein Concentration for Kinetics and Affinity Determination in Structure-function Analysis 

In this study, we explore the interaction between the bovine cysteine protease inhibitor cystatin B and a catalytically inactive form of papain (Fig. 1), a plant cysteine protease, by real-time label-free analysis using Biacore X100. Several cystatin B variants with point mutations in areas of interaction with papain, are produced. For each cystatin B variant we determine its specific binding concentration using calibration-free concentration analysis (CFCA) and compare the values obtained with total protein concentration as determined by A280. After that, the kinetics of each cystatin B variant binding to papain is measured using single-cycle kinetics (SCK). We show that one of the four cystatin B variants we examine is only partially active for binding. This partial activity, revealed by CFCA, translates to a significant difference in the association rate constant (ka) and affinity (KD), compared to the values calculated using total protein concentration. Using CFCA in combination with kinetic analysis in a structure-function study contributes to obtaining reliable results, and helps to make the right interpretation of the interaction mechanism.

The Importance of Correct Protein Concentration for Kinetics and Affinity Determination in Structure-function Analysis

We apply label-free protein interaction analysis using Biacore X100 for structure-function analysis of the binding of several cystatin B mutants to papain through kinetic characterization. Calibration-free concentration analysis (CFCA) measures the concentration of protein with retained binding activity without the need for a standard curve. We show that confirmation of concentrations using CFCA increases the reliability of the kinetic analysis and that kinetic constants can reliably be determined even if the activity of a recombinant protein is reduced.

La importancia de la concentración de la proteína correcta para la cinética y la determinación de afinidad en el análisis de la estructura-función

In this study, we explore the interaction between the bovine cysteine protease inhibitor cystatin B and a catalytically inactive form of papain (Fig. 1), ...

Investigación

Biología

Using the GELFREE 8100 Fractionation System for Molecular Weight-Based Fractionation with Liquid Phase Recovery

The GELFREE 8100 Fractionation System is a novel protein fractionation system designed to maximize protein recovery during molecular weight based fractionation. The system is comprised of single-use, 8-sample capacity cartridges and a benchtop GELFREE Fractionation Instrument. During separation, a constant voltage is applied between the anode and cathode reservoirs, and each protein mixture is electrophoretically driven from a loading chamber into a specially designed gel column gel. Proteins are concentrated into a tight band in a stacking gel, and separated based on their respective electrophoretic mobilities in a resolving gel. As proteins elute from the column, they are trapped and concentrated in liquid phase in the collection chamber, free of the gel. The instrument is then paused at specific time intervals, and fractions are collected using a pipette. This process is repeated until all desired fractions have been collected. If fewer than 8 samples are run on a cartridge, any unused chambers can be used in subsequent separations.
This novel technology facilitates the quick and simple separation of up to 8 complex protein mixtures simultaneously, and offers several advantages when compared to previously available fractionation methods. This system is capable of fractionating up to 1mg of total protein per channel, for a total of 8mg per cartridge. Intact proteins over a broad mass range are separated on the basis of molecular weight, retaining important physiochemical properties of the analyte. The liquid phase entrapment provides for high recovery while eliminating the need for band or spot cutting, making the fractionation process highly reproducible1.

The accompanying video describes the use of the GELFREE 8100 Fractionation System, which partitions complex protein samples on the basis of molecular weight and recovers the fractions in liquid phase. The video describes how the technology works, how it is used, and provides resultant data, with polyacrylamide gel electrophoresis analysis of fractionated bovine liver homogenate.

Utilizando el sistema de fraccionamiento GELFREE 8100 de peso molecular basado en Fraccionamiento con la recuperación de la fase líquida

The GELFREE 8100 Fractionation System is a novel protein fractionation system designed to maximize protein recovery during molecular weight based ...

Analysis of the Development of a Morphological Phenotype as a Function of Protein Concentration in Budding Yeast

Gene deletion and protein overexpression are common methods for studying functions of proteins. In this article, we describe a protocol for analysis of phenotype development as a function of protein concentration at population and single-cell levels in Saccharomyces cerevisiae. Although this protocol is based on the overexpression of a protein, it can easily be adapted for morphological phenotypes dependent on suppression of protein expression. Our lab is interested in studying the signaling properties of the endocytic adaptor protein epsin. To that purpose we used a dominant negative approach in which we over-expressed the conserved Epsin N-Terminal Homology (ENTH) domain in order to interfere with the functions of endogenous epsin-2 (Ent2 or YLR206W). We observed that overexpression of the ENTH domain of Ent2 (ENTH2) in wild type cells led to a cell division defect that is dependent on the mislocalization of a family of scaffolding proteins, septins.

Gene deletion and protein overexpression are common methods for studying functions of proteins. In this article, we describe a protocol for analysis of phenotype development as a function of protein concentration at population and single-cell levels in Saccharomyces cerevisiae.

Análisis de la elaboración de un fenotipo morfológico en función de la concentración de proteínas en la levadura en ciernes

Gene deletion and protein overexpression are common methods for studying functions of proteins. In this article, we describe a protocol for analysis of ...

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in current research and a plethora of methods for their investigation is available1. Protein-protein interactions often are highly dynamic and may depend on subcellular localization, post-translational modifications and the local protein environment2. Therefore, they should be investigated in their natural environment, for which co-immunoprecipitation approaches are the method of choice3. Co-precipitated interaction partners are identified either by immunoblotting in a targeted approach, or by mass spectrometry (LC-MS/MS) in an untargeted way. The latter strategy often is adversely affected by a large number of false positive discoveries, mainly derived from the high sensitivity of modern mass spectrometers that confidently detect traces of unspecifically precipitating proteins. A recent approach to overcome this problem is based on the idea that reduced amounts of specific interaction partners will co-precipitate with a given target protein whose cellular concentration is reduced by RNAi, while the amounts of unspecifically precipitating proteins should be unaffected. This approach, termed QUICK for QUantitative Immunoprecipitation Combined with Knockdown4, employs Stable Isotope Labeling of Amino acids in Cell culture (SILAC)5 and MS to quantify the amounts of proteins immunoprecipitated from wild-type and knock-down strains. Proteins found in a 1:1 ratio can be considered as contaminants, those enriched in precipitates from the wild type as specific interaction partners of the target protein. Although innovative, QUICK bears some limitations: first, SILAC is cost-intensive and limited to organisms that ideally are auxotrophic for arginine and/or lysine. Moreover, when heavy arginine is fed, arginine-to-proline interconversion results in additional mass shifts for each proline in a peptide and slightly dilutes heavy with light arginine, which makes quantification more tedious and less accurate5,6. Second, QUICK requires that antibodies are titrated such that they do not become saturated with target protein in extracts from knock-down mutants.
Here we introduce a modified QUICK protocol which overcomes the abovementioned limitations of QUICK by replacing SILAC for 15N metabolic labeling and by replacing RNAi-mediated knock-down for affinity modulation of protein-protein interactions. We demonstrate the applicability of this protocol using the unicellular green alga Chlamydomonas reinhardtii as model organism and the chloroplast HSP70B chaperone as target protein7 (Figure 1). HSP70s are known to interact with specific co-chaperones and substrates only in the ADP state8. We exploit this property as a means to verify the specific interaction of HSP70B with its nucleotide exchange factor CGE19.

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

We present a variation of the QUICK (QUantitative Immunoprecipitation Combined with Knockdown) approach that was introduced previously to distinguish between true and false protein-protein interactions. Our approach is based on 15N metabolic labeling, the modulation of affinities of protein-protein interactions by the presence/absence of ATP, immunoprecipitation, and quantitative mass spectrometry.

El Protocolo para la identificación de las interacciones proteína-proteína base de<sup&gt; 15</sup&gt; N metabólico Etiquetado, inmunoprecipitación, cuantitativa Espectrometría de Masas y Modulación Affinity

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in ...

Using High Resolution Computed Tomography to Visualize the Three Dimensional Structure and Function of Plant Vasculature

High resolution x-ray computed tomography (HRCT) is a non-destructive diagnostic imaging technique with sub-micron resolution capability that is now being used to evaluate the structure and function of plant xylem network in three dimensions (3D) (e.g. Brodersen et al. 2010; 2011; 2012a,b). HRCT imaging is based on the same principles as medical CT systems, but a high intensity synchrotron x-ray source results in higher spatial resolution and decreased image acquisition time. Here, we demonstrate in detail how synchrotron-based HRCT (performed at the Advanced Light Source-LBNL Berkeley, CA, USA) in combination with Avizo software (VSG Inc., Burlington, MA, USA) is being used to explore plant xylem in excised tissue and living plants. This new imaging tool allows users to move beyond traditional static, 2D light or electron micrographs and study samples using virtual serial sections in any plane. An infinite number of slices in any orientation can be made on the same sample, a feature that is physically impossible using traditional microscopy methods.
Results demonstrate that HRCT can be applied to both herbaceous and woody plant species, and a range of plant organs (i.e. leaves, petioles, stems, trunks, roots). Figures presented here help demonstrate both a range of representative plant vascular anatomy and the type of detail extracted from HRCT datasets, including scans for coast redwood (Sequoia sempervirens), walnut (Juglans spp.), oak (Quercus spp.), and maple (Acer spp.) tree saplings to sunflowers (Helianthus annuus), grapevines (Vitis spp.), and ferns (Pteridium aquilinum and Woodwardia fimbriata). Excised and dried samples from woody species are easiest to scan and typically yield the best images. However, recent improvements (i.e. more rapid scans and sample stabilization) have made it possible to use this visualization technique on green tissues (e.g. petioles) and in living plants. On occasion some shrinkage of hydrated green plant tissues will cause images to blur and methods to avoid these issues are described. These recent advances with HRCT provide promising new insights into plant vascular function.

High resolution x-ray computed tomography (HRCT) is a non-destructive diagnostic imaging technique that can be used to study the structure and function of plant vasculature in 3D. We demonstrate how HRCT facilitates exploration of xylem networks across a wide range of plant tissues and species.

El uso de alta resolución de tomografía computarizada para visualizar la estructura tridimensional y la función de la Planta Vasculatura

High resolution x-ray computed tomography (HRCT) is a non-destructive diagnostic imaging technique with sub-micron resolution capability that is now being ...

RNA Secondary Structure Prediction Using High-throughput SHAPE

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone.

High-throughput selective 2' hydroxyl acylation analyzed by primer extension (SHAPE) utilizes a novel chemical probing technology, reverse transcription, capillary electrophoresis and secondary structure prediction software to determine the structures of RNAs from several hundred to several thousand nucleotides at single nucleotide resolution.

ARN de predicción de estructura secundaria Utilizando SHAPE de alto rendimiento

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed ...

A Protocol for Phage Display and Affinity Selection Using Recombinant Protein Baits

Using recombinant phage as a scaffold to present various protein portions encoded by a directionally cloned cDNA library to immobilized bait molecules is an efficient means to discover interactions. The technique has largely been used to discover protein-protein interactions but the bait molecule to be challenged need not be restricted to proteins. The protocol presented here has been optimized to allow a modest number of baits to be screened in replicates to maximize the identification of independent clones presenting the same protein. This permits greater confidence that interacting proteins identified are legitimate interactors of the bait molecule. Monitoring the phage titer after each affinity selection round provides information on how the affinity selection is progressing as well as on the efficacy of negative controls. One means of titering the phage, and how and what to prepare in advance to allow this process to progress as efficiently as possible, is presented. Attributes of amplicons retrieved following isolation of independent plaque are highlighted that can be used to ascertain how well the affinity selection has progressed. Trouble shooting techniques to minimize false positives or to bypass persistently recovered phage are explained. Means of reducing viral contamination flare up are discussed.

Phage display is a powerful technique to capture proteins or protein moieties that interact with an immobilized molecule of interest. Once a decision of the type of phage cDNA library to create and screen has been made, the protocol described here permits efficient affinity selection leading to identification of interactors.

Un Protocolo de fagos y de selección de afinidad utilizando cebos proteína recombinante

Using  recombinant phage as a scaffold to present various protein portions encoded by  a directionally cloned cDNA library to immobilized bait molecules ...

Discovering Protein Interactions and Characterizing Protein Function Using HaloTag Technology

Research in proteomics has exploded in recent years with advances in mass spectrometry capabilities that have led to the characterization of numerous proteomes, including those from viruses, bacteria, and yeast.&#160; In comparison, analysis of the human proteome lags behind, partially due to the sheer number of proteins which must be studied, but also the complexity of networks and interactions these present. To specifically address the challenges of understanding the human proteome, we have developed HaloTag technology for protein isolation, particularly strong for isolation of multiprotein complexes and allowing more efficient capture of weak or transient interactions and/or proteins in low abundance. &#160;HaloTag is a genetically encoded protein fusion tag, designed for covalent, specific, and rapid immobilization or labelling of proteins with various ligands. Leveraging these properties, numerous applications for mammalian cells were developed to characterize protein function and here we present methodologies including: protein pull-downs used for discovery of novel interactions or functional assays, and cellular localization. We find significant advantages in the speed, specificity, and covalent capture of fusion proteins to surfaces for proteomic analysis as compared to other traditional non-covalent approaches. We demonstrate these and the broad utility of the technology using two important epigenetic proteins as examples, the human bromodomain protein BRD4, and histone deacetylase HDAC1.&#160; These examples demonstrate the power of this technology in enabling &#160;the discovery of novel interactions and characterizing cellular localization in eukaryotes, which will together further understanding of human functional proteomics. &#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;

HaloTag technology is a multifunctional technology which has shown significant success in isolation of both small and large protein complexes from mammalian cells.&#160; Here we highlight the advantages of this technology compared to existing alternatives and demonstrate its utility to study numerous aspects of protein function inside eukaryotic cells.

El descubrimiento de las interacciones de proteínas y caracterización de la función de proteínas utilizando HaloTag Tecnología

Research in proteomics has exploded in recent years with advances in mass spectrometry capabilities that have led to the characterization of numerous ...

Functional Reconstitution and Channel Activity Measurements of Purified Wildtype and Mutant CFTR Protein

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a unique channel-forming member of the ATP Binding Cassette (ABC) superfamily of transporters. The phosphorylation and nucleotide dependent chloride channel activity of CFTR has been frequently studied in whole cell systems and as single channels in excised membrane patches. Many Cystic Fibrosis-causing mutations have been shown to alter this activity. While a small number of purification protocols have been published, a fast reconstitution method that retains channel activity and a suitable method for studying population channel activity in a purified system have been lacking. Here rapid methods are described for purification and functional reconstitution of the full-length CFTR protein into proteoliposomes of defined lipid composition that retains activity as a regulated halide channel. This reconstitution method together with a novel flux-based assay of channel activity is a suitable system for studying the population channel properties of wild type CFTR and the disease-causing mutants F508del- and G551D-CFTR. Specifically, the method has utility in studying the direct effects of phosphorylation, nucleotides and small molecules such as potentiators and inhibitors on CFTR channel activity. The methods are also amenable to the study of other membrane channels/transporters for anionic substrates.

Described here is a rapid and effective procedure for functional reconstitution of purified wild-type and mutant CFTR protein that preserves activity for this chloride channel, which is defective in Cystic Fibrosis. Iodide efflux from reconstituted proteoliposomes mediated by CFTR allows studies of channel activity and the effects of small molecules.

Reconstitución funcional y la actividad del canal Las mediciones de Purificada Wildtype y mutante CFTR Proteína

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a unique channel-forming member of the ATP Binding Cassette (ABC) superfamily of ...

Inducible LAP-tagged Stable Cell Lines for Investigating Protein Function, Spatiotemporal Localization and Protein Interaction Networks

Multi-protein complexes, rather than single proteins acting in isolation, often govern molecular pathways regulating cellular homeostasis. Based on this principle, the purification of critical proteins required for the functioning of these pathways along with their native interacting partners has not only allowed the mapping of the protein constituents of these pathways, but has also provided a deeper understanding of how these proteins coordinate to regulate these pathways. Within this context, understanding a protein's spatiotemporal localization and its protein-protein interaction network can aid in defining its role within a pathway, as well as how its misregulation may lead to disease pathogenesis. To address this need, several approaches for protein purification such as tandem affinity purification (TAP) and localization and affinity purification (LAP) have been designed and used successfully. Nevertheless, in order to apply these approaches to pathway-scale proteomic analyses, these strategies must be supplemented with modern technological developments in cloning and mammalian stable cell line generation. Here, we describe a method for generating LAP-tagged human inducible stable cell lines for investigating protein subcellular localization and protein-protein interaction networks. This approach has been successfully applied to the dissection of multiple cellular pathways including cell division and is compatible with high-throughput proteomic analyses.

We describe a method for generating localization and affinity purification (LAP)-tagged inducible stable cell lines for investigating protein function, spatiotemporal subcellular localization and protein-protein interaction networks.

Inducible líneas celulares estables LAP-etiquetado para la investigación de la función de proteínas, espacio-temporal y localización de las redes de interacción de proteínas

Multi-protein complexes, rather than single proteins acting in isolation, often govern molecular pathways regulating cellular homeostasis. Based on this ...