Le projet est soutenu en partie par Alfred P. Sloan Foundation, NSF Career Award (DBI 1027394), et l&#39;Institut national des sciences médicales générales (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>

Pas de conflits d&#39;intérêt déclarés.

Le protocole présenté ci-dessus est un guide général pour la structure et la fonction de modélisation utilisant le serveur I-Tasser. Bien que cette procédure automatisée fonctionne très bien pour la plupart des protéines, les interventions humaines contribuent souvent à améliorer significativement la précision de la modélisation, en particulier pour les protéines qui manquent de modèles proches de la bibliothèque APB. Les utilisateurs peuvent intervenir au cours de la modélisation I-Tasser de la manière suivante: (a) le fractionnement des protéines multi-domaines, (b) en fournissant contraintes externes pour améliorer l&#39;assemblage de la structure, et (c) supprimer des modèles lors de la modélisation. Fractionnement des protéines multi-domaines: Beaucoup de séquences de protéines à long contiennent fréquemment de multiples domaines attachés par des flexibles régions linker, ce qui rend leur élucidation de la structure en utilisant des techniques difficiles à la fois expérimentale et computationnelle. Néanmoins, en tant que domaines sont indépendamment pliage des entités distinctes et peut effectuer la fonction moléculaire, il estsouhaitable de diviser longues protéines multi-domaines et le modèle de chaque domaine séparément. Domaines de modélisation individuelle ne sera pas seulement d&#39;accélérer le processus de prédiction, mais augmente également la qualité de la requête-modèle de l&#39;alignement, résultant dans la structure plus fiables et les prédictions de fonction. Les limites du domaine dans les séquences de protéines peuvent être prédits à l&#39;aide disponible gratuitement en ligne des programmes externes tels que NCBI CDD 24, 25 ou PFAM InterProScan 26. Aussi, si les alignements LOMETS filetage sont disponibles pour la protéine de requête, les limites du domaine peut être localisé par identifier visuellement de longues étendues de résidus non-alignés dans les modèles haut de filetage (voir l&#39;étape 5.4). Ces régions non alignés pour la plupart correspondent à des régions de liaison de domaine. Si les modèles multi-domaines sont déjà disponibles dans la bibliothèque de modèles PDB avec tous les domaines requête alignées, alors la protéine requête peut être modélisée comme pleine longueur. Fournir contraintes extérieures <pclass = &quot;jove_content&quot;&gt; Les simulations d&#39;assemblage des structures de l&#39;I-Tasser sont principalement guidées par des contraintes spatiales collectées auprès des LOMETS threading modèles. Pour les protéines qui ont une bonne requête a frappé le filetage (Norm. Z-score&gt; 1) dans la bibliothèque de modèles, dérivés contraintes spatiales sont pour la plupart d&#39;une grande précision et I-Tasser va générer des modèles à haute résolution structurelle pour ces protéines. Au contraire, pour les protéines de requête qui ont peu ou pas touché le filetage (Norm. Z-score &lt;1), recueillies contraintes spatiales contiennent souvent des erreurs en raison de l&#39;incertitude du modèle et l&#39;alignement. Pour ces cibles protéiques, spécifié par l&#39;utilisateur des informations spatiales peuvent être très utiles pour améliorer la qualité du modèle prédit. Les utilisateurs peuvent fournir contraintes extérieures au serveur I-Tasser de deux façons: A. Spécifier contact / distance de contraintes Caractérisé expérimentalement inter-résidus contacts / distances, par exemple de la RMN ouréticulation des expériences, peut être spécifié en téléchargeant un fichier de retenue. Un fichier exemple est montré dans la figure 8, où la colonne 1 précise le type de contrainte, à savoir &quot;DIST&quot; ou &quot;CONTACT&quot;. Pour de retenue à distance (DIST), les colonnes 2 et 4 contiennent des positions de résidus (i, j), les colonnes 3 et 5 contiennent l&#39;atome-types dans les résidus et la colonne 6 indique la distance entre les deux atomes spécifiées. Pour les appuie-contact (contact), les colonnes 2 et 3 contiennent les positions (i, j) des résidus qui doivent être en contact. La distance entre les chaînes latérales au centre de ces paires de résidus en contact est décidé en fonction des distances observées dans les structures connues dans l&#39;APB. I-Tasser va essayer de tirer de ces paires d&#39;atomes proches de la distance spécifiée lors de l&#39;affinement de la structure des simulations. B. Spécifier un modèle de structure des protéines LOMETS programmes filetage utiliser une bibliothèque représentant APB pour trouver les replis plausible pour la prot requêteein. Bien que l&#39;utilisation d&#39;une bibliothèque structure représentative contribue à réduire le temps nécessaire pour calculer les alignements de séquences-structure, il est possible qu&#39;une protéine bon modèle est manquée dans la bibliothèque ou le modèle peut ne pas avoir été identifiés par les programmes LOMETS filetage, même si elle est présents dans la bibliothèque. Dans ces cas, l&#39;utilisateur doit spécifier la structure de la protéine voulue comme le modèle. Pour spécifier la structure des protéines comme un modèle supplémentaire, les utilisateurs peuvent soit télécharger un fichier de structure PDB formaté ou spécifier l&#39;ID APB d&#39;une structure de protéine déposée dans la bibliothèque de l&#39;APB. Le I-Tasser va générer l&#39;alignement des requêtes modèle à l&#39;aide du programme 23 de rassemblement et permettra de recueillir contraintes spatiales à la fois du modèle de l&#39;utilisateur spécifié et des modèles pour guider LOMETS la simulation de montage de structure. Parce que la précision des contraintes LOMETS est différente pour les différentes cibles, le poids des contraintes LOMETS est plus forte en facile (homologue) TArgets que celui dur (non homologues) des objectifs, qui ont été systématiquement à l&#39;écoute de notre formation de référence. L&#39;utilisateur peut également spécifier leur propre requête template alignements. Le serveur accepte l&#39;alignement en deux formats: le format FASTA (figure 9A) et le format 3D (Figure 9B). Le format FASTA est standard et décrite au <a href="http://zhanglab.%20ccmb.med.umich.edu/FASTA/">http://zhanglab. ccmb.med.umich.edu / FASTA /</a> . Le format 3D est similaire au format standard APB ( <a href="http://www.wwpdb.org/documentation/format32/sect9.html">http://www.wwpdb.org/documentation/format32/sect9.html</a> ), mais deux colonnes supplémentaires dérivées à partir des modèles sont ajoutés aux dossiers atome (voir figure 9B): Colonnes 1-30: Atome (C-alpha uniquement) et les résidus de noms pour la séquence requête. Colonnes 31-54: Les coordonnées de C-alpha des atomes de la requête copiés à partir des atomes correspondants dans le modèle. Colonnes 55-59: numéro résidu correspondant dans le modèle basé sur l&#39;alignement Colonnes 60-64: nom résidu correspondant dans le modèle Exclure les protéines modèles Les protéines sont des molécules flexibles et peuvent adopter plusieurs états conformationnels de changer leur activité biologique. Par exemple, les structures des protéines kinases de nombreuses protéines membranaires ont été résolues dans la conformation à la fois actives et inactives. Aussi la présence ou l&#39;absence de ligand lié peut provoquer de grands mouvements structurels. Alors que tous les états conformationnels du modèle sont semblables pour les programmes de filetage, il est souhaitable de modèle de la requête en utilisant des modèles dans un seul état particulier. Une nouvelle option sur le serveur permet à l&#39;utilisateur d&#39;exclure les protéines de modèle lors de la modélisation de structure. Cette fonction permettrait également à l&#39;utilisateur de choisir le niveau d&#39;homologie de modèles à utiliser pour la modélisation. Les utilisateurs peuvent exclure les protéines template from de la bibliothèque I-Tasser par: A. Définition d&#39;une coupure d&#39;identité de séquence Les utilisateurs peuvent utiliser cette option pour exclure des protéines homologues de la bibliothèque de modèles I-Tasser. Le niveau d&#39;homologie est fixé en fonction du seuil d&#39;identité de séquence, c&#39;est à dire le nombre de résidus identiques entre la requête et la protéine modèle, divisé par la longueur de séquence de la séquence requête. Par exemple, si l&#39;utilisateur tape dans &quot;70%&quot; dans la forme prévue, toutes les protéines modèles qui ont une identité de séquence&gt; 70% à la protéine requête I-seront exclus de la bibliothèque de modèles I-Tasser. B. Exclure protéines modèle spécifique Protéines modèle spécifique peut être exclu de la bibliothèque de modèles I-Tasser en téléchargeant une liste contenant les ID APB des structures d&#39;être exclus. Un fichier exemple est montré dans la figure 10. Comme la même protéine peut exister en tant que de multiples entrées dans la bibliothèque APB, I-Tasser soiRVer sera par défaut d&#39;exclure les modèles spécifiés (dans Colonne1) ainsi que tous les autres modèles de la bibliothèque qui ont une identité&gt; 90% aux modèles spécifiés. L&#39;utilisateur peut également spécifier un seuil d&#39;identité différents, par exemple, 70%, où tous les modèles avec l&#39;identité&gt; 70% aux protéines modèle spécifié seront exclus.

<ol><li> FASTA formaté séquence d&#39;acides aminés de la protéine à modéliser (voir, <a href="http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml">http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml</a> ). </li><li> Un ordinateur personnel avec accès à Internet et un navigateur Web. </li><li> Moléculaire du logiciel de visualisation, par exemple, ou RasMol PyMOL, pour analyser la structure tertiaire et prédit des sites fonctionnels. </li></ol>

a protocol for computer-based protein structure and function prediction

Génome projets de séquençage ont crypté des millions de séquences protéiques, qui exigent la connaissance de leur structure et leur fonction pour améliorer la compréhension de leur rôle biologique. Bien que les méthodes expérimentales peuvent fournir des informations détaillées pour une petite fraction de ces protéines, la modélisation computationnelle est nécessaire pour la majorité des molécules de protéines qui sont expérimentalement non caractérisés. Le serveur I-Tasser est une table de travail en ligne pour la modélisation à haute résolution de la structure des protéines et la fonction. Etant donné une séquence de protéine, une sortie typique du serveur I-Tasser comprend prédiction de structure secondaire, prédit accessibilité au solvant de chaque résidu, des protéines homologues modèle de threading et détectés par des alignements structure, jusqu&#39;à cinq pleine longueur tertiaires modèles structurels, et basée sur la structure annotations fonctionnelles pour la classification des enzymes, des termes Gene Ontology et protéine-ligand des sites de liaison. Toutes les prévisions sont étiquetés avec un score de confiance quiraconte comment les prédictions sont précises sans connaître les données expérimentales. Afin de faciliter les demandes spéciales des utilisateurs finaux, le serveur fournit les canaux d&#39;accepter spécifié par l&#39;utilisateur inter-résidus de distance et de contact des cartes pour changer de façon interactive la modélisation I-Tasser, elle permet également aux utilisateurs de spécifier toutes les protéines de matrice, ou à exclure n&#39;importe quel modèle protéines lors des simulations de montage de structure. Les informations structurelles pourraient être recueillies par les utilisateurs sur la base des preuves expérimentales ou connaissances biologiques dans le but d&#39;améliorer la qualité de I-Tasser prédictions. Le serveur a été évalué comme le meilleur des programmes de la structure des protéines et des prévisions de fonction dans les expériences récentes CASP échelle de la communauté. Il n&#39;y a actuellement&gt; 20 000 chercheurs inscrits dans plus de 100 pays qui utilisent la ligne I-Tasser serveur.

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

Lignes directrices pour l&#39;informatique basée caractérisation structurale et fonctionnelle de protéines en utilisant le pipeline I-Tasser est décrite. A partir de la séquence protéique de requête, les modèles 3D sont générés à l&#39;aide de plusieurs alignements de filetage et itératif des simulations assemblage structural. Inférences fonctionnelles sont ensuite tirées à partir des matches à protéines avec une structure connue et fonctions.

Un protocole pour la structure des protéines Computer-Based et prédiction de la fonction

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

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Biology

68.1K vues.  University of Michigan. Lignes directrices pour l'informatique basée caractérisation structurale et fonctionnelle de protéines en utilisant le pipeline I-Tasser est décrite. A partir de la séquence protéique de requête, les modèles 3D sont générés à l'aide de plusieurs alignements de filetage et itératif des simulations assemblage structural. Inférences fonctionnelles sont ensuite tirées à partir des matches à protéines avec une structure connue et fonctions.

Vidéo: 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.

L&#39;importance de la concentration de protéines correct pour Cinétique et Détermination d&#39;affinité en analyse structure-fonction

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

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Biologie

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.

Utiliser le système de fractionnement GELFREE 8100 pour les Poids Moléculaire à base de fractionnement avec récupération de la phase liquide

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.

Analyse du développement d&#39;un phénotype morphologique en fonction de la concentration de protéines dans la levure bourgeonnante

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.

Un protocole pour l&#39;identification des interactions protéine-protéine Basé sur<sup&gt; 15</sup&gt; N marquage métabolique, immunoprécipitation, la spectrométrie de masse quantitative et d&#39;affinité de modulation

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.

Utilisation à haute résolution tomodensitométrie pour visualiser la structure tridimensionnelle et la fonction des vaisseaux sanguins des végétaux

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 prédiction de la structure secondaire via SHAPE à haut débit

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 protocole pour Phage Display et Affinity sélection en utilisant appâts protéine 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.

Découvrir interactions des protéines et caractérisation de protéines de fonction utilisant la technologie HaloTag

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.

Reconstitution fonctionnelle et Activité du Chan mesures de type sauvage purifiée et Mutant protéine CFTR

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.

Inductibles stables lignées cellulaires LAP marqués pour Enquêter la fonction des protéines, Spatiotemporal Localisation et Interaction Networks Protein

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