The growth of sequence and structure databases has led to major advances in computational protein tertiary structure prediction. However, predictions based on this inaccurate model will result in erroneous predictions due to poor template selection and inaccurate target-template sequence alignments. With years of experience, we provide customized service for detection of errors in protein models to precisely meet customer requirements.

Introduction of Errors in Protein Models

Experimental techniques that can be used as modeling templates and de novo prediction techniques greatly improve structural prediction methods. At the same time, the critical evaluation of protein structure prediction technology has also promoted the research field. There are several sources of error in the determination of a protein structure. Errors enter not only in the collection of the experimental data, but especially in their interpretation. Limited diffraction resolution and poor phase often result in electron density maps that are difficult to interpret. Therefore, predictions based on this inaccurate model will lead to various types of errors, including mistracing of the protein chain due to uncertainty in backbone connectivity, misalignment or misregistration of residues, and misplacement of side-chain and backbone atoms. Error detection in modeling has important implications for the accuracy or quality assessment of predictive models, as well as for biochemical experimental design and drug design.

Fig 1. Typical errors in comparative modeling. (M. S. Madhusudhan, et al., 2007)

Services

The quality of predictive models is highly dependent on correct template selection and sequence alignment between query and template. It is difficult to analyze or identify an incorrect model just by checking internal parameters or calculating energy values. As a leading service provider of protein engineering, Creative BioMart has successfully developed the model evaluation prediction server (Key Technology Evaluation of Protein Structure Prediction and Fully Automated Evaluation of Key Technology of Structure Prediction) to detect errors in models, including secondary structure prediction, residue-residue basis contact prediction, fold assignment, and comparison models.

For error detection in protein modeling, we fully take into account various influencing factors, including sequence and structure compatibility, internal parameters, bond torsional strain, disallowed conformations, or computational energies. Our detection mainly targets the following errors:

• Side-chain packing errors.
• Distortions and shifts in correctly aligned regions.
• Errors in regions without a template.
• Errors resulting from misalignments.
• Selection of incorrect templates.

Strategies for Addressing Errors in Protein Modeling

In response to the above errors, our scientists are committed to developing various strategies to provide solutions for the errors in protein modeling.

• By controlling for these two factors using a structural alignment or a sequence alignment generated based on a comparison of two solved structures, errors in the final model can be significantly reduced.
• A "consensus" approach to collecting multiple fold identification and multiple alignment search results increases the likelihood of identifying the correct template.
• Using multiple templates in the model building step is worse than using a single correct template, but better than using a single suboptimal template.

Creative BioMart'ss mission is to minimize errors in protein modeling, provide global customers with high-quality and reliable structural predictions, and assist in the evaluation of modeling quality. We will work with you to develop the most appropriate strategy and provide the most meaningful data for your research for accelerating the research of life sciences. If you are interested in our services, please do not hesitate to contact us for more information.

Reference

1. M. S. Madhusudhan, Marc A. Marti-Renom, et al.. (2007) Comparative Protein Structure Modelin. In book: The Proteomics Protocols Handbook (pp.831-860). DOI:10.1385/1-59259-890-0:831.

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