How Will You Decide Appropriateness of a better Protein Model?

In protein science research, researchers are always looking to predict the structures with better accuracy. There are several metrics available, that can tell the overall nature of the model quality. One of them is the Template Modeling score or normally called TM score. Programs like I-TASSER will provide this score but unfortunately, majority of the normal users skip it. So, here I am going to tell you that what it TM and which TM score can be considered a better one.  Remember this score will always be in the range of 0-1, and for your ease 0.2 means 20% and vice versa. If you are using AlphaFold for structure modeling, I would recommend here understanding the metrics of AlphaFold for a quality of protein structures.

Template modeling score is an important metric used to assess the quality of protein structure predictions generated by template-based modeling methods such as homology modeling and comparative modeling. The score is a measure of the similarity between the predicted structure and the template structure used to guide the prediction process. Further, you can read about What is protein structure modeling?

The following are some of the reasons why the template modeling score is important in protein structure predictions:

Quality assessment: The template modeling score provides a quantitative measure of the quality of the predicted structure. A high template modeling score indicates a close match between the predicted structure and the template structure, while a low score indicates that the predicted structure deviates significantly from the template.

Confidence in predictions: The template modeling score provides a confidence level in the accuracy of the predicted structure. A high score suggests that the prediction is likely to be correct, while a low score indicates that the prediction is less reliable and may require further refinement or validation.

Model selection: The template modeling score is used to rank and select the best models from a set of predictions. This is especially useful when multiple models are generated for a single protein, as the score can be used to identify the model with the highest accuracy.

Benchmarking: The template modeling score is a commonly used benchmark to evaluate the performance of protein structure prediction methods. By comparing the scores of different methods, researchers can determine the best approach for predicting a given protein structure.

In summary, the template modeling score is an important metric for evaluating the quality of protein structure predictions and for determining the confidence in the accuracy of these predictions. It is widely used in various applications of protein structure prediction and provides valuable information for model selection, benchmarking, and confidence in assessment.