M4t looking to experiment

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Narcis Fernandez-Fuentes, Carlos J. MMM module is a sequence-to-structure alignment method that is aimed at improving the alignment accuracy, especially at lower sequence identity levels. The current implementation of MMM takes inputs from three profile-to-profile-based alignment methods and iteratively compares and ranks alternatively aligned regions according to their fit in the structural environment of the template structure. The performance of M4T was benchmarked on CASP6 comparative modeling target sequences and on a larger independent test set and showed a favorable performance to current state-of-the-art methods.

Comparative modeling is currently the most accurate protein structure prediction method 1. A prerequisite for successful comparative modeling is to find at least one suitable structure that shares a detectable sequence similarity spanning most of the modeled sequence 2. Accordingly, the two most critical steps in comparative modeling are: i identifying one or more templates, and ii calculating an accurate alignment between the target sequence and template structure s 3. The first step in comparative modeling is aided by several methods developed for fold-recognition 4—6 and profile-alignment 78 that allow an efficient recognition of remotely related sequences.

M4t looking to experiment

Although these methods often identify more than one template structure, currently available modeling programs, and especially the automated servers, typically consider only one template for building a model for a target sequence. Meanwhile at CASP meetings 9 and other reports 1011 indicate that the use of multiple templates improves the quality of comparative models Accurate alignment of a target sequence to a template structure continues to be a bottleneck in producing good quality homology models. A of alignment methods have been developed and are publicly available.

However, none of these alignment methods consistently produces a better solution that is better than those from other methods 12 Furthermore, alignments produced by different methods are often better in some regions and worse in others when compared to one other. One possible solution to this problem is to consider several alignment methods and combine better-aligned parts into a unique solution The M4T server has been developed to address these issues by producing accurate alignments and models by minimizing the errors associated with the first two steps template recognition and alignment in comparative modeling.

In the first step, protein structures are searched, compared and analyzed, and a of candidates are selected to serve as templates. Next, to reduce errors associated with sequence-to-structure alignments, M4T uses an iterative implementation of the Multiple Mapping Method MMM 12 that considers solutions from several alignment methods and combines better-aligned parts into a unique solution, which, on average, is more accurate than any of the input alignments alone.

In the final step, using these critical inputs, a default comparative protein structure model building is performed using Modeller M4T server performs three main tasks in an automated manner Figure 1 : i template search and selection performed by the Multiple Template MT module; ii target sequence to template structure s alignment, performed by the Multiple Mapping Module MMM module 12 and iii model building, performed by Modeller General overview of the algorithm: first, a PSI-BLAST search is performed with a query sequence, then template s are selected in the MT-module; subsequently, MMM-module performs sequence-to-structure alignment sand finally Modeller builds the protein model s.

After searching the PDB an iterative clustering procedure identifies the most suitable templates to combine, i.

M4t looking to experiment

Templates are selected or discarded according to a hierarchical selection procedure that s for sequence identity between templates and target sequence, sequence identity among templates, crystal resolution of the templates and contribution of templates to the target sequence i. The result of the iterative clustering of templates is one or more groups of templates each containing one or more template structures Figure 2. Within each cluster, all templates are aligned to the corresponding target sequence using the iterative-MMM approach see later. In the last consolidation step the sequence-to-structure alignments of the overlapping clusters are combined.

If clusters of templates are not overlapping or the overlap between them is not sufficient for a structurally accurate superposition i. In the MT module the template candidates go through an iterative clustering and filtering process to select the least of templates with a unique contribution to the target.

The target-to-template s alignments are calculated using an iterative implementation of the Multiple Mapping Method 12 Figure 2. Next, BlastProfiler 19 is run to build representative sequence profiles for both the target and template sequences. BlastProfiler parses all iterations of PSIBLAST outputs, locates and stores those pairwise alignments between the query and database sequences that meet the filtering criteria. Such alternative alignments may include either the same or different regions of the hit sequence.

Alignments to different regions of the target are kept as separate entries. Because alignments produced in later iterations contain more specific information about the sequence profile, these alignments are preferred over earlier ones in case of overlaps. As a result, three separate profiles for the target sequence and three profiles for each template s are generated.

Finally, the target profiles are aligned to the corresponding template profiles. At the end of this step, three alternative profile-to-profile-based sequence alignments are available, which are used as input to MMM Selected template s and optimized alignment s from the MT and MMM modules described earlier are provided as inputs.

M4t looking to experiment

Two measures are calculated to assess model quality. The DOPE score was published recently and it showed a favorable performance over other energy scores to rank models relatively to each other DOPE score is useful if a user calculates several models for the same protein. In order to assess model quality in absolute terms we also calculate PROSA scores and energy profile The performance of M4T was extensively benchmarked on a set of modeling cases and CASP targets, where a backdated version of PDB was used for searching for templates [to be published elsewhere; Fernandez-Fuentes, N.

Submitted ]. All comparative model targets from CASP6 were tested by building models with M4T using the single best identified template and then by using multiple templates. For 11 out of 24 CASP6 comparative modeling targets it was possible to combine multiple templates. M4T also compared well with state-of-the art methods and human experts in protein modeling. It is less trivial to compare these because alignments may be different due to different methods used, different profiles employed or manual editing.

Also, certain users may have used information on multiple structures. In addition, expert users may have attempted side chain and loop modeling in certain parts of the models. As another qualitative comparison, in nine cases the differences between the best CASP model and M4T were too small to draw any conclusion, while in five and nine cases M4T or CASP models were ificantly more accurate for one case M4T did not return a model. Out of the 24 best CASP targets the largest population of targets that belonged to the same research group was 9, the second largest was 2.

In this simplified comparison M4T would fare as the second best individual performer with 5 of the 24 best targets. While it is true that from a small of test cases, such as at CASP, it is hard to conclude statistical ificance 26 we perceive this performance as encouraging and a that automated methods are becoming competitive with the best expert users.

As sequence identity decreases, the accuracy of models in terms of RMSD to the experimental solution structure that are built using multiple templates is better than the accuracy of models built using any single template alone as tested on random modeling cases Figure 3. In addition, on average, the length of the modeled sequence is longer when using multiple templates than when using a single template. When using multiple templates the length of model coverage increases by at least 1, 5, 10, 20 residues in 56, 21, RMSD model compared to the actual experimental structure versus sequence identity.

Using a dataset of proteins with known structures two sets of models were built: 1 using one template only best E -value hit; light bars2 using multiple templates selected by MT grey bars. The percentage of sequence identity is calculated between the hit sequence with the highest E -value and the query sequence.

Error of the mean is indicated. M4T server is implemented on an Apache server running Fedora Core 5 operating system. Databases required by the server, namely, PDB 16 and NR 18are locally installed and weekly updated. All the queries are submitted to a queuing system.

are either displayed in HTML format or sent to the user by e-mail as a hyperlink. The M4T server has a straightforward interface Figure 4.

M4t looking to experiment

In order to use this server, the user must provide a target sequence, which can be entered in a text box, or can be ed as a text file. The target sequence must be in raw text containing one-letter amino acid codes without any headers. If an e-mail address is provided the user is also notified by e-mail when the prediction is finished including a hyperlink where the can be accessed.

M4T ass a unique job identifier for each submitted query e. This job identifier can be used to check the status of the submission i. M4T returns a full atom model s in PDB format and the alignment s used to build the model. When the prediction process is finished, the server will send a notification by e-mail to the user if an e-mail address was provided.

Otherwise, users have to visit the submission and access the by using the job identifier. are kept on the server for 5 days only. Occasionally, M4T may fail to provide a prediction. All details of the process are registered in a log file that users can examine. In addition users can contact the authors via e-mail to m4t fiserlab. A web server for comparative protein structure prediction is described that takes advantage of a recently developed new sequence to structure alignment technique and the optimal selection and use of multiple template structures.

The most time-consuming parts of the M4T algorithm are the database searches and calculation of profiles clustering. The server is deed to deliver high quality comparative models to the non-experts users, with competitive quality to those produced by manual expert modelers. Google Scholar.

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. In. Advanced Search. Search Menu. Article. Close mobile search Article. Volume Article Contents Abstract. Oxford Academic. Carlos J. Brajesh Kumar Rai. Eduardo Fajardo.

Revision received:. Select Format Select format. Permissions Icon Permissions. Figure 1. Open in new tab Download slide. Figure 2. Figure 3. Figure 4. Screenshots of the submission and web s. Google Scholar Crossref. Search. FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. In silico protein recombination: enhancing template and sequence alignment selection for comparative protein modelling. Multiple mapping method: a novel approach to the sequence-to-structure alignment problem in comparative protein structure modeling.

Consensus alignment for reliable framework prediction in homology modeling. FRankenstein becomes a cyborg: the automatic recombination and realignment of fold recognition models in CASP6. Tolerating some redundancy ificantly speeds up clustering of large protein databases. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

Modeller: generation and refinement of homology-based protein structure models. Google Scholar PubMed. Statistical potential for assessment and prediction of protein structures. Issue Section:. Download all slides. Comments 0. Add comment Close comment form modal. I agree to the terms and conditions. You must accept the terms and conditions.

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