Name: 컴퓨터 기반의 단백질 구조 및 기능 예측을위한 프로토콜 (동영상) | JoVE Journal
Uploaded: 2011-11-03T12:00:00.000+00:00
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Description: 학습하세요 I-TASSER 파이프라인을 활용한 단백질 3차원 구조 및 기능 예측 방법을. 머신러닝 기반 2차 구조 예측과 스레딩, 프래그먼트 조립 기법을 통해 고품질 모델을 생성합니다.

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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/pubmed?term=((Template-based+modeling+and+free+modeling+by+I-TASSER+in+CASP7%5BTitle%5D)+AND+%22Proteins%22%5BJournal%5D)">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/pubmed?term=((I-TASSER%3A+Fully+automated+protein+structure+prediction+in+CASP8%5BTitle%5D)+AND+%22Proteins%22%5BJournal%5D)">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/pubmed?term=((Ab+initio+modeling+of+small+proteins+by+iterative+TASSER+simulations%5BTitle%5D)+AND+%22BMC+Biol%22%5BJournal%5D)">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/pubmed?term=((I-TASSER%3A+a+unified+platform+for+automated+protein+structure+and+function+prediction%5BTitle%5D)+AND+%22Nat+Protoc%22%5BJournal%5D)">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/pubmed?term=((LOMETS%3A+a+local+meta-threading-server+for+protein+structure+prediction%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">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/pubmed?term=((Local+energy+landscape+flattening%3A+parallel+hyperbolic+Monte+Carlo+sampling+of+protein+folding%5BTitle%5D)+AND+%22Proteins%22%5BJournal%5D)">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/pubmed?term=((REMO%3A+A+new+protocol+to+refine+full+atomic+protein+models+from+C-alpha+traces+by+optimizing+hydrogen-bonding+networks%5BTitle%5D)+AND+%22Proteins%22%5BJournal%5D)">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/pubmed?term=((High-resolution+protein+structure+refinement+using+fragment+guided+molecular+dynamics+simulations%5BTitle%5D)+AND+%22%22%5BJournal%5D)">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/pubmed?term=((COFACTOR%3A+protein-ligand+binding+site+predictions+by+global+structure+similarity+match+and+local+geometry+refinement%5BTitle%5D)+AND+%22%22%5BJournal%5D)">COFACTOR: protein-ligand binding site predictions by global structure similarity match and local geometry refinement.</a> , Forthcoming (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/pubmed?term=((On+the+origin+and+highly+likely+completeness+of+single-domain+protein+structures%5BTitle%5D)+AND+%22Proc.+Natl.+Acad.+Sci.+U.+S.+A%22%5BJournal%5D)">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/pubmed?term=((TOUCHSTONE+II%3A+a+new+approach+to+ab+initio+protein+structure+prediction%5BTitle%5D)+AND+%22Biophys.+J%22%5BJournal%5D)">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/pubmed?term=((A+comprehensive+assessment+of+sequence-based+and+template-based+methods+for+protein+contact+prediction%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((SPICKER%3A+a+clustering+approach+to+identify+near-native+protein+folds%5BTitle%5D)+AND+%22J.+Comput.+Chem%22%5BJournal%5D)">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/pubmed?term=((High-resolution+protein+structure+refinement+using+fragment+guided+molecular+dynamics+simulations%5BTitle%5D)+AND+%22%22%5BJournal%5D)">High-resolution protein structure refinement using fragment guided molecular dynamics simulations.</a> , Forthcoming (2010).</li>
<li>Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((I-TASSER+server+for+protein+3D+structure+prediction%5BTitle%5D)+AND+%22BMC+Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((How+significant+is+a+protein+structure+similarity+with+TM-score+%3D+0.5%3F%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((Scoring+function+for+automated+assessment+of+protein+structure+template+quality%5BTitle%5D)+AND+%22Proteins%22%5BJournal%5D)">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/pubmed?term=((Nomenclature+Committee+of+the+International+Union+of+Biochemistry+and+Molecular+Biology+%28NC-IUBMB%29.+Enzyme+Nomenclature.+Recommendations+1992.+Supplement+4%3A+corrections+and+additions+%281997%29%5BTitle%5D)+AND+%22Eur+J+Biochem%22%5BJournal%5D)">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/pubmed?term=((Gene+ontology%3A+tool+for+the+unification+of+biology.+The+Gene+Ontology+Consortium%5BTitle%5D)+AND+%22Nat.+Genet%22%5BJournal%5D)">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/pubmed?term=((TM-align%3A+a+protein+structure+alignment+algorithm+based+on+the+TM-score%5BTitle%5D)+AND+%22Nucleic.+Acids.+Res%22%5BJournal%5D)">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/pubmed?term=((RK+Ab+Intio+Protein+Structure+Prediction%5BTitle%5D)+AND+%22%22%5BJournal%5D)">RK Ab Intio Protein Structure Prediction.</a> , Forthcoming (2011).</li>
<li>Kim, D. E., Chivian, D., Baker, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protein+structure+prediction+and+analysis+using+the+Robetta+server%5BTitle%5D)+AND+%22Nucleic.+Acids.+Res%22%5BJournal%5D)">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/pubmed?term=((Inferring+protein+function+by+global+and+local+similarity+of+structural+analogs%5BTitle%5D)+AND+%22%22%5BJournal%5D)">Inferring protein function by global and local similarity of structural analogs.</a> , Forthcoming (2011).</li>
<li>Marchler-Bauer, A., Bryant, S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CD-Search%3A+protein+domain+annotations+on+the+fly%5BTitle%5D)+AND+%22Nucleic.+Acids.+Res%22%5BJournal%5D)">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/pubmed?term=((The+Pfam+protein+families+database%5BTitle%5D)+AND+%22Nucleic.+Acids.+Res%22%5BJournal%5D)">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/pubmed?term=((InterProScan--an+integration+platform+for+the+signature-recognition+methods+in+InterPro%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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.

<table><tbody><tr><td>Material Name</td><td>Type</td><td>Company</td><td>Catalogue Number</td></tr><tr><td>FASTA formatted amino acid sequence of the protein to be modeled (see, &lt;a href=&quot;http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml&quot;&gt;http://www.ncbi.nlm.nih.gov/BLAST/fasta.shtml&lt;/a&gt;).</td><td></td><td></td><td></td></tr><tr><td>A personal computer with access to the internet and a web browser.</td><td></td><td></td><td></td></tr><tr><td>Molecular visualizing software, e.g. RASMOL or PYMOL, for analyzing the predicted tertiary structure and functional sites.</td><td></td><td></td><td></td></tr></tbody></table>

a protocol for computer-based protein structure and function prediction

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.

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 ...

The aim of this procedure is to computationally predict three dimensional structures and biological function of protein molecules starting from their amino acid sequences. This is accomplished by first predicting the secondary structure of the proteins by machine learning. The sequences and the predicted secondary structure are then matched with the solved structures in the PDB library to identify the best possible structure templates.This procedure is called threading. Following the threading procedure, the IT AER program will split the templates into fragments based on the sequence template alignments, and then reassemble the fragments to full length models in the third step. Full atomic models are constructed by atomic level refinements to optimize hydrogen bonding networks and remove ster over overlaps.The last step of the procedure is to identify the biological function of the proteins by matching the prediction structures with proteins of known function in the function library. The main advantage of ITER over existing structure modeling method is the inherent structure fragment assembly approach, which can consistently drive the threading alignments closer to the native state. These high quality structure models also form the basis of accurate structure based functional annotations to promote the usage of ITER in the scientific community.Our lab has made available a website where the protein sequences can be submitted to iter. This website acts as a nexus with which the users worldwide may register an interface to computer cluster, which manages and runs ITER simulations. An ITER simulation job consists of over a dozen smaller sub simulations.These simulations when run on a single computer with a single processor core can take over a hundred hours. The Zang lab computer cluster takes and distributes these sub simulations across hundreds of computers and is capable of running over 2000 simulations. Concurrently with our computer cluster, we are capable of completing hundreds of I taster simulations every day.Even with this capacity, much work has to be done to optimize the system and minimize waiting time for our online IT AER users. To start with the structure and function modeling experiment, log onto the IT AER webpage. The URL addresses for all relevant webpages discussed here can be found in the written protocol.Copy and paste the amino acid sequence into the provided form, or directly upload the sequence by clicking the browse button. Provide an email address and a name for the job. Users can optionally specify external into residue contact or distance restraints.Add in an additional template or exclude some template proteins during the structure modeling process. To submit the sequence, click on the run it taser button. Check the status of the submitted job by visiting the IT taser queue page.Click on the search tab and use the job ID number or the query sequence to search for the submitted job. After the structure and function modeling is finished, a notification email containing an image of the predicted structures and a web link will be sent to the provided email address. Click on this link to view and download the results.Begin structure analysis by examining the secondary structure prediction, which is displayed as H for alpha helix, S for beta strand, or C for coil. Also, consider the confidence score of prediction for each residue. Look for regions with long structures of regular secondary structure predictions to estimate the core region in the protein.The structural class of the protein can also be analyzed based on the distribution of secondary structure elements. View the predicted solvent accessibility to ascertain buried and solvent exposed regions. In the query values of predicted solvent accessibility range from a score of zero for a buried residue to a score of nine for an exposed residue.Regions containing mostly buried residues can be used to delineate the core region in the protein while regions with solvent exposed and hydrophilic residues are potential hydration or functional sites. To view the predicted tertiary structures of the query protein, scroll down to the displayed interactive JMO Appt left. Click on the applet to change the appearance of the displayed structure.Zoom into a specific region, select specific residue types in the predicted model or calculate into residue distances. Analyze the confidence scores of the structural modeling to estimate the quality of the predicted structures. Csco values are typically in the range of negative five to two, wherein a higher score reflects a model of better quality.The estimated TM score and RMSD of the first model is shown as estimated accuracy of model one. Click on the more about csco link. To analyze the csco cluster size and cluster density of all the models, analyze the top 10 threading templates of the query protein as identified by low mets threading programs.By scrolling down the results page, view the normalized Z-score to analyze the quality of threading alignments. Alignments with a normalized csco greater than one, reflect a confident alignment and are most likely to have the same fold as the query protein. Examine the sequence identity in the threading aligned region and for the whole chain to assess the homology between the query and the template proteins.High sequence identity is an indicator of evolutionary relatedness between the query and template proteins. View the threading aligned residue shown in color to visually identify conserved residues or motifs in the query and the template proteins a higher sequence identity in the threading aligned region as compared to whole chain alignment also indicates the presence of conserved structural motifs or domains in the query. Assess the coverage of the threading alignment by inspecting the alignment.If the coverage of the top alignment is low and confined to only a small region of the query protein or absent for a long segment of query sequence, it indicates that the query protein contains more than one domain. In this case, it is recommended to split the sequence and model the domains individually. View the next table of the result page to determine the top 10 structural analogs of the first predicted model as identified by the structural alignment program, TM align.A TM score greater than 0.5 indicates that the detected analog and model have a similar topology and can be used to determine the structural class or protein family of the query protein. Those with a TM score less than 0.3 signify a random structure similarity. Analyze the sequence identity and RMSD in the structurally aligned region to assess the conservation of spatial motifs in the model and the structural analog.Visually inspect the colored and aligned residue pairs in the alignment to identify these structurally conserved residues and motifs. Look at the predicted EC numbers table to view the top five potential enzyme OGs of the query protein. The confidence level of the EC number prediction using these templates is shown as the EC score based on benchmarking analysis.Functional similarity between the query and template protein can be reliably interpreted using an EC score greater than 1.1. Next, look for consensus of function amongst the templates, which have a similar fold as the query protein. If multiple templates have the same EC number and the EC score is greater than 1.1, the confidence level of prediction is very high.However, if the EC score is high, but there is a lack of consensus among the identified hits, the prediction becomes less reliable and the users are recommended to consult the gene ontology. Term predictions view the predicted gene ontology terms table to identify the top 10 homologs of query protein in the PDB library annotated with gene ontology terms, each protein is usually associated with multiple gene ontology terms describing its molecular functions, biological processes, and cellular location. Click on each term to visit the amigo website and analyze its definition and lineage.Analyze the functional homology score column to access the functional similarity between the query and template proteins. The confidence level of transferring functional annotation from these proteins can also be estimated. View the consensus prediction of gene ontology terms table to analyze the concurrence of function between the templates.These common functions are used for predicting the gene ontology terms of the query protein and to assess the confidence level of geo term predictions. Finally, scroll down to the bottom of the page to view the top 10 ligand binding site predictions for the query protein predicted binding sites are ranked based on the number of predicted ligand confirmations that share a common binding pocket. The best identified binding site is already displayed in the JM OL appt.Click on the radio buttons to analyze other predictions and visualize the ligand interacting residues. The BS score reveals local similarity between the model and the templates binding site. A BS score greater than 1.1 indicates high sequence and structural similarity near the predicted binding site.In the model as compared to the known binding site in the template, the IT is a main webpage contains links for other useful features. The forum feature allows the user to create an online account and seek help from other ITER users regarding structure modeling or for help interpreting the results. The download feature allows users to download iter and related packages and install them on their computer.This helps reduce the time required to perform the modeling experiments. The queue feature allows the status of all submitted jobs to be seen on the IT a Q page. Users can also visually inspect the image of modeled structures for finished jobs.On this page, also shown at the CSCO expected TM score and expected RMSD of the first model and the submission date shown here is an excerpt of the IT AER results page showing the faster formatted query sequence, the predicted secondary structure, and the associated confidence scores and predicted solvent accessibility of the residues. The analyzed core region and the potential hydration site in the query are highlighted in cyan and red rectangles respectively. Here the tertiary structure predictions for the query proteins are shown.The predicted models are displayed in an interactive JML app outlet, allowing the user to change the display of the molecule. The models can also be downloaded by clicking on the download links, the confidence score to estimate the quality of the model is reported as the csco. An example of the itta A results page showing the top 10 identified threading templates and alignments by Loomis threading programs is presented.The quality of the threading alignments is evaluated based on the normalized Z-score, where a value greater than one reflects a confident alignment. Aligned residues in the template that are identical to the corresponding query residues are highlighted in color to indicate the presence of a conserved residue or motif. Conversely, a lack of alignment in most of the top templates indicates the presence of multiple domains in the query protein and the unaligned residues correspond to domain linker regions.This table displays the top 10 identified structural analogs and structural alignments identified by the TM aligned Structural alignment Program. The ranking of the analogs is based on the TM score of the structural alignment. A TM score greater than 0.5 indicates that the two compared structures have a similar topology.While a TM score less than 0.3 means a similarity between two random structures. Structurally aligned residue pairs are highlighted in color based on their amino acid property while the unaligned regions are indicated by a dash. Here is an example of the ITR result page showing identified enzyme homologs of the query protein in the PDB library.The confidence level of EC number prediction is analyzed based on the EC score, where an EC score greater than 1.1 indicates functional similarity between query and template protein. The gene ontology term prediction table for the query protein includes functional homologs for the query protein in the gene ontology template library ranked based on their functional homology score. Common functional features from these top scoring hits are derived to generate the final gene ontology term predictions for the query protein.The quality of the predicted gene ontology terms is estimated based on geo score, where a geo score greater than 0.5 indicates a reliable prediction shown here as an example of the IT AZA result page showing the top 10 protein ligand binding site predictions using the cofactor algorithm. The ranking of the predicted binding sites is based on the number of predicted ligand confirmations that share a common binding pocket. In the query BS score is a measure of the local sequence and structure similarity between the predicted and templates binding site and is useful for analyzing the conservation of binding site pockets.Although ISER is one of the most efficient algorithms for protein structure and function prediction, it is important to remember that it's just a prediction from computer algorithms. Any experimental data or function insights, for example, residue contacts binding information will be extremely useful for increasing the accuracy of predictions. The IT AER server has a portal to include these informations during the modeling procedure To accommodate increasing interest in it.Aer, the Zang lab has released the IT AER software free for non-commercial research. We are actively developing an improving IT AER and improving eye taster, and in the hope that its availability will lead to large scale application outside of the Zang lab and will benefit and spur further research in the scientific community.

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

Research

JoVE Journal

Biology

We developed computational de novo protein design methods capable of tackling several important areas of protein design. To disseminate these methods we present Protein WISDOM, an online tool for protein design (http://www.proteinwisdom.org). Starting from a structural template, design of monomeric proteins for increased stability and complexes for increased binding affinity can be performed.

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

A protocol for the online investigation of protein sequence-structure-dynamics relationships using Bio3D-web is presented.

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

The goal of this protocol is to develop a reference for divergent proteins in a group that lacks coherent criteria for nomenclature and classification. This reference will facilitate analyses and discussion of the group as a whole and can be used in addition to established names.

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

We describe a methodology based on sequence diversification to estimate the amino acid preferences of multispecific binding sites in protein-protein interactions (PPIs). In this strategy, thousands of potential peptide ligands are generated and screened in silico, thus overcoming some limitations of available experimental methods.

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Protein design involves the construction of amino acid sequences and the incorporation of specific motifs to create functional variants. This approach is critical for the development of antimicrobial peptides (AMPs) to combat antibiotic-resistant pathogens. This paper presents a procedure for protein construction using various bioinformatics tools.

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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...

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

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.

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

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.

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

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.

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

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.

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

학습하세요 I-TASSER 파이프라인을 활용한 단백질 3차원 구조 및 기능 예측 방법을. 머신러닝 기반 2차 구조 예측과 스레딩, 프래그먼트 조립 기법을 통해 고품질 모델을 생성합니다.

A Protocol for Computer-Based Protein Structure and Function Prediction

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