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AlphaFold is a deep learning model developed by the DeepMind team at Google for predicting the three-dimensional structure of proteins. In case, you are new to AlphaFold,  here  is a quick introduction to the program. AlphaFold uses a neural network to predict the three-dimensional structure of a protein from its amino acid sequence. The neural network is trained on a large dataset of known protein structures and amino acid sequences, using a technique called supervised learning. The model uses a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to learn the complex relationships between protein sequences and structures. The output of the AlphaFold model is a prediction of the three-dimensional structure of the protein, represented as a set of coordinates for each atom in the protein. These predictions are evaluated using a range of metrics, including the predicted local distance difference test (PLDDT), predicted torsion angle metri...

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 pr...