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Homology Modeling


Homology modeling method refers to: Use a protein with a known structure that has homology to an unknown structure protein as a template, and apply bioinformatics methods to predict its three-dimensional structure based on the primary sequence through computer simulation and calculation.

A schematic flow chart of the homology modeling method.Figure 1. A schematic flow chart of the homology modeling method. (Luccio, E. D.; Koehl, P. 2011)

Our Standard Process for Homology Modeling

At Alfa Chemistry, homologous modeling can be carried out in the following eight steps:

Search for the template of the structural model

The modeling method assumes that two homologous proteins share the same skeleton. When building a model for the protein to be predicted, a template based on the structure of the homologous protein is built. The template is a protein with a known structure, and the sequence of the protein is very similar to the target protein.

Sequence alignment

The sequence of the target protein is aligned with the sequence of the template protein, so that the amino acid residues of the target protein match those of the template protein. Template sequence and target sequence similarity should > 30 %. Moreover, insertion and deletion operations are allowed in the comparison.

Build the skeleton.

We apply the atomic coordinates of the template structure to the target protein, and only copy the coordinates of the matching residues. The main chain atom position is adjusted to make the bone structure conform to the principle of stereochemistry. In general, this step is designed to construct the main chain structure of the target protein.

Build the ring area

Our experts create the ring area based on known ring structure or predict it from scratch based on the principles of quantum chemistry.

Construct the side chain of the target protein.

The coordinates of the same residues in the template are directly used as the residue coordinates of the target protein. However, for residues with incomplete matching, the side chain conformations are different and further prediction is required. In general, the prediction of side chain coordinates is achieved by employing empirical data with known structures.

Construct the loop of the target protein

In the step 2 of sequence alignment, regions for loops which often correspond to loops between secondary structure elements may be added. For loop regions, additional models need to be established. We usually apply an empirical method to find an optimal loop region from a protein with a known structure, and build its structure by copying its structure data.

Optimize the model

A preliminary structural model is established for the target protein through the above process. However, there may be some incompatible spatial coordinates in this model. Therefore, improvements and optimizations are required such as using molecular dynamics, simulated annealing and other methods to optimize the structure. For example, the energy is minimized to find the lowest energy point, that is, the stable conformation for the side chain optimization. In addition, we use simulated annealing and molecular dynamics methods to eliminate the unreasonable contact between atoms in the three-dimensional model obtained through the above process.

Model evaluation

The results of protein structure prediction need to be verified. Alfa Chemistry applies a common evaluation standard called RMSD, which represents the root-mean-square deviation of the corresponding atoms between the target protein and the template protein.

Features of Our Services

  • High reliability
  • Automation and fast
  • High structural similarity

Application of Homology Modeling

  • Understand the structural properties of the target protein
  • Study the interaction between the target and drug
  • Analyze its 'structure-function' relationship
  • Discovery of potential therapeutic drugs for a disease

Our homology modeling services remarkably reduce the cost, promote further experiments, and accelerate the process of drug design for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!


  • Luccio, E. D.; Koehl, P. A quality metric for homology modeling: the H-factor. Bmc Bioinformatics. 2011, 12(1): 48.

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