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Model Ranking and Analysis Service

Determining the correct structure of a protein with known sequence is of great importance for biomedical research. As a leading company in the area of protein engineering, Creative Biolabs is capable of offering a full package of protein structure prediction services for global customers. Particularly, we provide high-quality model ranking and analysis service to help you determine the most appropriate structure to promote the development of your projects.

Importance of Model Ranking

Unlike experimentally derived structures, where the accuracy of the structure can be estimated from experiment and typically falls within a narrow range, models typically can span a broad range of the accuracy spectrum as numerous alternative models will be produced during protein structure prediction. To select the final model, scoring functions which aim at estimating the expected accuracy of models have been developed. These methods fall into two categories: the first category of scoring functions relies on the analysis of single models based on evolutionary or physiochemical criteria, e.g. by comparing models with statistical properties of known structures; the second category derives a quality score from the information contained in an ensemble of models for a given sequence using an all-against-all structural comparison of the models. Both approaches have their advantages and the choice of the methods depends on the situation and the availability of information.

The workflow of the MULTICOM method comprised of six steps. (1) A pool of tertiary structure models is predicted for a target protein. (2) Models are scored and ranked by different QA methods. (3) Models are clustered into groups based on structural similarity. (4) The consensus of individual QA rankings and other information are synthesized to generate the final ranking of all the models. (5) The final ranking and the clustering results are integrated to select top five diverse models for submission. (6) The top five models are combined to generate five refined models to be submitted to CASP11. Fig.1 The workflow of the MULTICOM method comprised of six steps. (1) A pool of tertiary structure models is predicted for a target protein. (2) Models are scored and ranked by different QA methods. (3) Models are clustered into groups based on structural similarity. (4) The consensus of individual QA rankings and other information are synthesized to generate the final ranking of all the models. (5) The final ranking and the clustering results are integrated to select top five diverse models for submission. (6) The top five models are combined to generate five refined models to be submitted to CASP11. (Cao, 2015)

Service

As highlighted recently, correctly ranking different models and identifying the most accurate model among a set of alternatives for the same target protein is of great vitality. With years of experience and professional scientists, Creative Biolabs has developed advanced technology platforms for protein model ranking and analysis. Based on our scoring system, both global and local quality estimates on the basis of a single model can be calculated to assist model selection. In addition, either a single or multiple model(s) can be assessed meanwhile with each model assigned a quality score used to rank the structures.

Features

Based on abundant experience, Creative Biolabs has established a powerful and advanced platform that can be used to assess your protein structure through our multifunctional protein model scoring system. We are committed to facilitating the protein candidate discovery. Please don’t hesitate to contact us to let us know your demands, and our team will get back to you as soon as we can.

Reference

  1. Cao, R.; et al. Large-scale model quality assessment for improving protein tertiary structure prediction. Bioinformatics. 2015, 31(12), pp. i116-i123.

All listed services and products are For Research Use Only. Do Not use in any diagnostic or therapeutic applications.

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