Name: a protocol for computer-based protein structure and function prediction
Uploaded: 2011-11-03T12:00:00
Duration: 16 min 41 s
Description: Watch this Scientific Journal Video about A Protocol for Computer-Based Protein Structure and Function Prediction at JoVE.com

The project is supported in part by Alfred P. Sloan Foundation, NSF Career Award (DBI 1027394), and the National Institute of General Medical Sciences (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 conflicts of interest declared.

The protocol presented above is a general guideline for structure and function modeling using the I-TASSER server. Although, this automated procedure works very well for most of the proteins, human interventions often help to significantly improve the modeling accuracy, especially for the proteins which lack close templates in the PDB library. Users can intervene during the I-TASSER modeling in the following ways: (a) splitting of multi-domain proteins; (b) providing external restraints to improve the structure assembly; and (c) removing templates during the modeling.
Splitting multi-domain protein:
Many long protein sequences frequently contain multiple domains tethered by flexible linker regions, which makes their structure elucidation difficult using both experimental and computational techniques. Nevertheless, as domains are independently folding entities and can perform distinct molecular function; it is desirable to split long multi-domain proteins and model each domain separately. Modeling domains individually will not only speed up the prediction process, but also increases the quality of query-template alignment, resulting in more reliable structure and function predictions.
Domain boundaries in protein sequences can be predicted using freely available external online programs such as NCBI CDD24, PFAM25 or InterProScan26. Also, if LOMETS threading alignments are available for the query protein, domain boundaries can be located by visually identifying long stretches of unaligned residues in the top threading templates (see Step 5.4). These unaligned regions mostly correspond to domain linker regions. If multi-domain templates are already available in the template PDB library with all the query domains aligned, then the query protein can be modeled as full length.
Provide external restraints
The structure assembly simulations in I-TASSER are mainly guided by spatial restraints collected from the LOMETS threading templates. For query proteins that have good threading hit (Norm. Z-score &gt; 1) in the template library, derived spatial restraints are mostly of high accuracy and I-TASSER will generate high resolution structural models for these proteins. Contrarily, for query proteins that have weak or no threading hit (Norm. Z-score &lt; 1), collected spatial restraints often contain errors because of the uncertainty of the template and the alignment. For these protein targets, user-specified spatial information can be very helpful to improve the quality of the predicted model. Users can provide external restraints to the I-TASSER server in two ways:
A. Specify contact/distance restraints
Experimentally characterized inter-residue contacts/distances, for example from NMR or cross-linking experiments, can be specified by uploading a restraint file. An example file is shown in Figure 8, where Column 1 specifies the type of restraint, i.e. "DIST" or "CONTACT". For distance restraint (DIST), columns 2 and 4 contain residue positions (i, j), columns 3 and 5 contain the atom-types in the residue and column 6 specifies the distance between the two specified atoms. For contact restraints (CONTACT), columns 2 and 3 contain the positions (i, j) of residues which should be in contact. The distance between the side chains center of these contacting residue pairs is decided based on observed distances in known structures in PDB. I-TASSER will try to draw these atom pairs close to the specified distance during the structure refinement simulations.
B. Specify a protein structure template
LOMETS threading programs use a representative PDB library to find plausible folds for the query protein. Although using a representative structure library helps to reduce the time required to compute the sequence-structure alignments, it is possible that a good template protein is missed in the library or the template may not have been identified by LOMETS threading programs, even though it is present in the library. In these cases, the user should specify the desired protein structure as the template.
To specify protein structure as an additional template, users can either upload a PDB formatted structure file or specify the PDB ID of a deposited protein structure in PDB library. The I-TASSER will generate the query-template alignment using MUSTER program23 and will collect spatial restraints from both the user specified template and LOMETS templates to guide the structure assembly simulation. Because the accuracy of the LOMETS restraints is different for different targets, the weight of the LOMETS restraints is stronger in easy (homologous) targets than that in hard (non-homologous) targets, which have been systematically tuned in our benchmark training.
Users can also specify their own query-template alignments. The server accepts alignment in two formats: the FASTA format (Figure 9A) and the 3D format (Figure 9B). The FASTA format is standard and described at <a href="http://zhanglab.%20ccmb.med.umich.edu/FASTA/">http://zhanglab. ccmb.med.umich.edu/FASTA/</a>. The 3D format is similar to the standard PDB format (<a href="http://www.wwpdb.org/documentation/format32/sect9.html">http://www.wwpdb.org/documentation/format32/sect9.html</a>), but two additional columns derived from the templates are added to the ATOM records (see Figure 9B):
 
Columns 1-30: Atom (C-alpha only) and residue names for the query sequence. 
Columns 31-54: Coordinates of C-alpha atoms of the query copied from the corresponding atoms in the template. 
Columns 55-59: Corresponding residue number in the template based on alignment 
Columns 60-64: Corresponding residue name in the template
Exclude templates proteins
Proteins are flexible molecules and can adopt multiple conformational states to change their biological activity. For example, structures of many protein kinases and membrane proteins have been solved in both active and inactive conformation. Also presence or absence of bound ligand can cause large structural movements. While all the conformational states of the template are alike for the threading programs, it is desirable to model the query using templates in only one particular state. A new option on the server allows the user to exclude template proteins during structure modeling. This feature would also allow the user to choose the homology level of templates to be used for the modeling. Users can exclude template proteins from the I-TASSER library by:
A. Specifying a sequence identity cutoff
Users can use this option to exclude homologous proteins from the I-TASSER template library. The homology level is set based on the sequence identity cutoff, i.e. the number of identical residue between the query and the template protein divided by the sequence length of the query sequence. For example, if the user types in "70%" in the provided form, all templates proteins which have a sequence identity &gt;70% to the query protein I-will be excluded from the I-TASSER template library.
B. Exclude specific template proteins
Specific template proteins can be excluded from the I-TASSER template library by uploading a list containing PDB IDs of the structures to be excluded. An example file is shown in Figure 10. As the same protein can exist as multiple entries in the PDB library, I-TASSER server will by default exclude the specified templates (in Column1) as well as all other templates from the library that have an identity &gt;90% to the specified templates. Users can also specify a different identity cutoff, e.g. 70%, where all templates with identity &gt;70% to specified template proteins will be excluded.

<ol>
	<li>FASTA formatted amino acid sequence of the protein to be modeled (see, <a href="http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml">http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml</a>).</li>
	<li>A personal computer with access to the internet and a web browser.</li>
	<li>Molecular visualizing software, e.g. RASMOL or PYMOL, for analyzing the predicted tertiary structure and functional sites.</li>
</ol>

a protocol for computer-based protein structure and function prediction

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently &gt;20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

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

Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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